**Author details**

**Item Lowland dipterocarp Riparian Hill dipterocarp**

Above 177.29 138.07 246.88 Below 30.59 23.61 42.64 Total 207.88 161.67 289.52

**Table 7.** Biomass and carbon stocks of LDF, RF and HDF of PNP.

with average diameter of 24.0 cm.

these activities contribute 17% from the total CO2

**Carbon (t/ha) Carbon (t/ha) Carbon (t/ha)**

water stress [26]. According to Pereira Da Silva et al. [27], many factors influence the tree growth in the forest. Usually, tropical tree species exhibits different behavior under different environmental conditions regardless of species or families. Macdicken and Brewbaker [28] agreed with this finding in which they found a significant different between site location and species interactions which indicate different environmental requirements for each species. In support to these findings, Brackand and Wood [29] provided a fact that tree growth was influenced by the environmental factors in the forest. Factors such as climatic, soil, topographic and competition combine to create a site. Therefore, the biomass value in a forest is indirectly

Global climate change is the current major threat to the earth. Due to the rapid deforestation and land clearing and conversion that have been actively taking place since 1850 [3] the emis-

was reported that forests can remove twice the amount that is lost by deforestation. It was estimated that the total carbon pool in the forest ecosystems approximately 1150 Gt, of which

**Table 7** exhibits the carbon storage of lowland dipterocarp, riparian and HDF in PNP. The estimation of carbon storage within each forest was not greatly varies between different species or tree components. The carbon storage in HDF at 289.52 t/ha was higher than LDF and RF. LDF was 207.88 t/ha whereas the lowest was RF at 161.67 t/ha. Meanwhile, above ground carbon in HDF was 246.89 t/ha, in LDF was 177.29 t/ha while RF was 138.07 t/ha, respectively (see **Table 7**). The carbon storage in HDF was the highest due to the higher biomass in this forest. This is because the tree density in HDF was higher compared to the other two forests types (*n* = 579). As comparison to other study, Hikmat [13] found nearly the same result in three virgin jungle reserves (VJR) in Peninsular Malaysia. Carbon storage in Mata Ayer VJR, Bukit Bauk VJR and Gunung Pulai VJR recorded 221.43, 303.16 and 176.33 t/ha, respectively. In another study, [16] estimated carbon storage in Air Hitam Forest Reserve was 89.57 t/ha. This value was considerably lower than the present study because Air Hitam Forest Reserve was recovering from the past disturbances. Therefore, most of the sample trees were composed of small diameter trees

14% in temperate forests, 37% in tropical forests and 49% is in the boreal forests [3].

keeps increasing. Referring to the report from National Research Council [30],

released to the atmosphere. However, it

affected by these factors because tree biomass value depends on the tree diameter.

**3.6. Carbon stocks**

136 National Parks - Management and Conservation

sion of CO2

Nor Farika Zani1,2, Mohd Nazip Suratman1,2, Adzmi Yaacob3 and Nazlin Asari1,2\*

\*Address all correspondence to<EMAIL_ADDRESS>
1 Faculty of Applied Sciences, Universiti Teknologi MARA, Shah Alam, Malaysia

2 Centre of Biodiversity and Sustainable Development, Universiti Teknologi MARA, Shah Alam, Malaysia

3 Faculty of Plantation and Agrotechnology, Universiti Teknologi MARA, Shah Alam, Malaysia


*5.2. MTT-SCMDE*

To estimate the clutter measurement density accurately for these multi-target tracking environments, we propose a method to calculate the probability that adjacent measurements are generated from clutter and use this probability to estimate the clutter measurement density.

To derive the clutter measurement probability, two events are defined.

Let **T***k* denote the set of the cluster targets at scan *k*, and *Hk* represent the event that **T***k* exists at scan *k* such as:

$$H\_k = \bigcup\_{\sigma \in \mathbf{T}\_k} \chi\_k^{\sigma}. \tag{36}$$

The probability that **z***k*,*j* is generated from clutter under *Hk*, the mutual exclusiveness of **z***k*,*j* sources, becomes:

$$P(\chi\_{k,j}^{0}|\mathbf{Z}^{k-1}, H\_k) = a\_k \prod\_{\sigma \in \mathbf{T}\_k} \left(1 - P\_{k,j}^{\sigma}\right),\tag{37}$$

where *Pσ k*,*j* is the prior probability that **z***k*,*j* is generated from target *σ* introduced in (17) and where *αk* is a normalization constant.

The probability that **z***k*,*j* is generated from target *τ* ∈ **T***k* becomes:

$$P(\chi\_{k,j}^{\mathsf{T}}|\mathbf{Z}^{k-1}, H\_k) \quad = \quad \mathfrak{a}\_k P\_{k,j}^{\mathsf{T}} \prod\_{\substack{\eta \in \mathsf{T}\_k \\ \eta \neq \tau}} \left(1 - P\_{k,j}^{\eta}\right) \tag{38}$$

$$\alpha = \alpha\_k \frac{P\_{k,j}^{\tau}}{1 - P\_{k,j}^{\tau}} \prod\_{\eta \in \mathbf{T}\_k} \left( 1 - P\_{k,j}^{\eta} \right) . \tag{39}$$

From (37) and (39), *αk* can be obtained from the mutual exclusiveness of **z***k*,*j* sources such as:

$$P(\chi\_{k,j}^0|\mathbf{Z}^{k-1}, H\_k) + \sum\_{\mathbf{r} \in \mathbf{T}\_k} P(\chi\_{k,j}^\mathbf{r}|\mathbf{Z}^{k-1}, H\_k) = 1. \tag{40}$$

Then, *αk* is obtained as:

$$\mathfrak{a}\_{k} = \frac{1}{\prod\_{\sigma \in \mathbf{T}\_{k}} \left(1 - P\_{k,j}^{\sigma}\right) \left(1 + \sum\_{\eta \in \mathbf{T}\_{k}} \frac{P\_{k,j}^{\eta}}{1 - P\_{k,j}^{\eta}}\right)} \tag{41}$$

Therefore, the clutter measurement probability *P*(*χ*0 *k*,*j* |**Z***k*−1, *Hk*) in (37) can be expressed as:

$$P(\chi\_{k,j}^0 | \mathbf{Z}^{k-1}, H\_k) = \frac{1}{1 + \sum\_{\eta \in \mathcal{T}\_k} \left( \frac{P\_{k,j}^{\eta}}{1 - P\_{k,j}^{\eta}} \right)}. \tag{42}$$

The proposed MTT-SCMDE utilizes the clutter measurement probabilities of the element of **Y***i k* defined in (30). Let *C*(*l*) *k*,*i* be the clutter measurement probability of **z** (*l*) *k*,*i* , the *l*th nearest measurement from **z***k*,*i*. If the cumulative sum *C*(*l*) *k*,*i* from *l* = 1 is bigger than the predetermined sparsity order *n*, which is the expected number of clutter measurements, the summation is stopped for the sparsity calculation. If:

$$\sum\_{l=1}^{m-1} \mathbb{C}\_{k,i}^{(l)} < n \le \sum\_{l=1}^{m} \mathbb{C}\_{k,i}^{(l)} \tag{43}$$

then the radius *r* (*n*) *k*,*i* for the hyper-sphere volume calculation becomes:

$$r\_{k,i}^{(n)} = \left\| \mathbf{z}\_{k,i}^{(m+1)} - \mathbf{z}\_{k,i} \right\|,\tag{44}$$

where **z** (*m*+1) *k*,*i* ∈ **Y***i k*.

In this paper, **z** (*n*+1) *k*,*i* is used to calculate *r* (*n*) *k*,*i* instead of **z** (*n*) *k*,*i* used in [23] for single target tracking to produce less biased estimates of the clutter measurement density for multi-target tracking environments.

The estimated sparsity of order *n* becomes:

$$\hat{\gamma}\_{k,i}^{(n)} = \frac{V\left(r\_{k,i}^{(n)}\right)}{\sum\_{l=1}^{m} \mathbb{C}\_{k,i}^{(l)}},\tag{45}$$

where *V r* (*n*) *k*,*i* is the volume of the hyper-sphere with radius *r* (*n*) *k*,*i* defined in (44).

When estimating the sparsity, the existing SCMDE utilizes the number of measurements in the hyper-sphere, while the proposed MTT-clutter measurement density estimation method utilizes the mean number of clutter measurements with the clutter measurement probability to reduce biases in the clutter measurement density estimates in multi-target tracking applications.

Figure 2 shows an expansion of the volume of the hyper-sphere of the MTT-SCMDE if it is used for data association in single target environments. Compared to Figure 1, the volume of the hyper-sphere for each sparsity order is increased.

**Figure 2.** Hyper-spheres for the multi-target tracking (MTT)-SCMDE used for single target tracking in a 2D space.


**1. Introduction**

In 2017 and 2018, the global investments (net capital flows) in renewable energy and energy efficiency projects reduced by 1% and 3%, respectively, and there is a risk that they will decrease even more [1]. This could threaten the achievements of the Sustainable Development Goals (SDGs) and the Paris Agreement for climate change. One reason behind the slowed development of green projects is difficulties in accessing private finance. The future of clean energy no longer concerns science and technology; it is all about access to finance [2]. People consider green energy projects, like other energy projects, as infrastructural projects. Infrastructural projects are capital-intensive and long-term projects. According to the Asian Development Bank [3], in developing Asia alone, there is a gap of \$26 trillion investment from 2016 to 2030, or \$1.7 trillion per year, if the region is to maintain its growth momentum, eradicate poverty, and respond to climate change (climate-adjusted estimate). Of the total climate-adjusted investment needs over the period 2016–2030, the largest share, or \$14.7 trillion, will be for energy (power).

There are two major barriers associated with green energy projects: (a) a lower rate of return compared to fossil fuel projects and (b) a higher risk of investment compared to fossil fuel projects (see [4]). Due to the associated risk and due to the Basel capital requirements, many banks are reluctant to finance green energy projects. Another reason why debt finance is hard to secure for new green energy projects is that, historically, regulated utility rates spread risks in utilities across consumers. However, the lack of purchasing power agreements (PPAs), especially in less developed countries, generates uncertainty about the tariff and increases the risk of investment. The third reason behind the uncertainty that has shrunk the new investment in the renewable energy sector is rapid technological progress and cost reduction. Thanks to the technological progress, costs of renewable

energy technologies reduced drastically. For example, solar PV module prices have reduced by around 80% since the end of 2009 and wind turbine prices have dropped by 30–40%. Although this is good news on one side, it prompts investors to pause to see how much the prices will drop on the other side.

All in all, it is important to take the necessary steps for mitigating the risks of green financing to unlock the participation of financial institutions in these projects [4]. One solution is to incentivize non-banking financial institutions (NBFIs), such as pension funds or insurance companies, to engage in green energy projects. The advantages of pension funds and insurance companies over banks are that these institutions pursue asset-liability matching and their resources are long-term (10, 20, or 40 years). Insurance companies or pension funds can finance infrastructure projects, including large green energy projects such as large hydropower, as they are long-term projects (10–20 years). Therefore, it is very important to develop pension funds and insurance companies in developing countries to fill the financing gap of infrastructure projects, including energy and green energy projects [4].

A good example of the role of non-banking financial institutions in unlocking green investment comes from Australia. The Powering Australian Renewables Fund (PARF) is a financing initiative that AGL Energy of Australia created and has \$2-3 billion funds targeting to unlock the investments in large-scale renewable energy projects by diversifying the risks and reducing the financing costs. A partnership with the Queensland Investment Corporation (QIC) established the fund in 2016 on behalf of its clients, the Future Fund and those investing in the QIC Global Infrastructure Fund. The target is to accelerate Australia's transition to a low-carbon economy with the potential to meet 10% of the Federal Government's Renewable Energy Target (RET).

One point that is important to consider is that public financial institutions, including green banks [5], need to crowd in private investment. This means that the government needs to be the investor of last resort, which involves investing in green projects when there is an urgent need for them and the private sector is not showing eagerness to invest. As for the role of the government, one important issue is political incentive when there is a conflict in terms of the election horizon and the maturity of green projects, which are often long-term. To overcome this problem, [6] proposed the establishment of a regional network, namely the European Sustainable Banking Network (EU SBN) for Europe. The [6] proposal consisted of three major actions: (1) green certification of private and public financial institutions, (2) sustainability rating of project proposals, and (3) systematic monitoring of the performance of banks and the financed investment projects. This means that, despite changing governments, this network will continue to carry out the activities and its mission.

A method that this paper will present is the introduction of green credit guarantee schemes (GCGSs), which [7] developed and introduced, to reduce the risk of financing. It is also important for banks to have specific programs for a precautionary approach to green lending, as well as compliance and risk management, which this paper will highlight.

Our paper contributes to the literature by providing innovative solutions for unlocking green finance and investment from banks and NBFIs. These solutions will help financial institutions to minimize and manage the risk of green financing. They include developing green credit guarantee schemes for reducing the financial risk, utilizing community-based trust funds, introducing insurance mechanisms and de-risking to cover non-financial risks, and using the spillover tax to increase the rate of return on green projects.

The structure of the paper is as follows. In Section 2, we highlight the challenges of developing green projects. Section 3 focuses on introducing and analyzing the enabling conditions for green financing. Section 4 provides an example of the implementation of the proposed tools and instruments, and Section 5 delivers concluding remarks and outlines the policy implications.

### **2. Challenges for Development of Green Projects**

This section highlights the challenges for the development of green projects.

### *2.1. Lack of Long-Term Financing*

Large-scale green energy projects, such as hydro-power or large solar farms, are long-term projects; hence, they need long-term financing. A shortage of long-term financing hinders the progress of green development. Banks still dominate Asian economies, and the banking sector constrains long-term finance. The development of public financial institutions (PFIs) that provide long-term financing or the development of pension funds and insurance companies are major solutions for filling the long-term financing and investment gap. [8] by analyzing the state investment banks (SIBs) in Australia, the UK, and Germany, found that SIBs take a broader role than capital provision and de-risking to mobilize finance. SIBs have an educational role to enable financial sector learning. The authors showed that SIBs signal trust and produce track records to crowd in private finance.

Figure 1 shows the structure of financial markets in selected Asian countries. Banks dominate the financial systems in Asia. Banks' resources are deposits, and deposits are short to medium term. On the other hand, infrastructure projects and energy projects are long-term. A maturity mismatch arises if banks' resources are allocated to financing long-term projects.

> **Investor Base in**

aggregate of

Thailand,

**Corporate Financing in Emerging Asia**

**Emerging Asia**

**Figure 1.** Bank-dominated financial systems in Asia. Source: [9].

### *2.2. Existence of Various Risks*

Republic of China, India,

> Republic of Korea,

Philippines,

GDP = gross domestic product

Cap. = capital

Note: Emerging Asia is the

Indonesia, the

Malaysia, the

Vietnam.

the People's

and

As most green energy technologies are new, there are several associated risks [10]. From mechanical breakdowns of wind power generator gearboxes to breakages of the panels of photovoltaic projects, the potential losses can reach millions of dollars, with major damage interrupting projects and businesses. This is not the whole story, however, as other risks accompany these projects—especially the weather. Most green energy projects depend on the climate and sunlight. The unpredictability of the weather, such as clouds that reduce the sun's irradiation or changes in wind strength, can have a significant negative impact on energy production and a ffect the feasibility of these projects. In addition, as many equipment for green projects are high-tech, it is expensive, creating risks regarding the feasibility. In order to identify and assess the diverse risks in green residential buildings that use green technologies, Ref. [11] conducted a survey of 30 construction companies in Singapore. Their survey results and study indicated that the top five critical risks in green residential building construction projects are: "complex procedures to obtain approvals", "overlooked high initial cost", "unclear requirements of owners", "employment constraint", and "lack of availability of green materials and equipment".

In addition to the aforementioned risks, the manufacturing of green technologies depends on cross-country supply chains and trade. Economies that are net importers of final products may be major exporters of materials or subcomponents for the same technologies. Hence, the exchange rate is another risk for green technologies.

Figure 2 shows the balance of trade for the four major clean energy technologies. Crystalline silicon (c-Si) photovoltaic (PV) and LED packages are the most heavily traded, perhaps because they are easier to ship than the other end products. The balance of trade is not the full story, however. While major PV deployment markets, such as the United States and Germany, are net importers of PV modules, they are also the largest exporters of polysilicon to make those modules, which Japan and the People's Republic of China largely purchase.

**Figure 2.** Balance of trade in select clean energy technology end products and across the C-Si PV module supply chain, 2014. (\$ million). c-Si = crystalline silicon and PV = photovoltaic. Source: Authors' development based on data from the [12]. Note: The values in parentheses are negative.

Green projects also involve other associated risks. Some of them are project-specific (demand risk) and some are general (e.g., natural disaster and political risks).

#### *2.3. Low Rate of Return and Lack of Capacity among Market Players*

Green technologies are often earlier in the development stage and less commercially viable than technologies in the fossil fuel field, many of which date back 100 years. This makes green technologies more expensive and riskier. A lack of access to conventional financing sources increases the debt cost (borrowing interest rate). New and expensive green technologies and access to expensive debt markets reduce the rate of return in green projects compared with fossil fuel projects. On the other hand, the majority of energy subsidies globally supports fossil fuels rather than the green sector.

Longstanding subsidies for fossil fuels, combined with the lack of a price for carbon emissions, have favored fossil fuels, like oil and coal [13]. In 2015, both consumers and producers of fossil fuels received about \$425 billion in subsidies globally—via direct payments, tax breaks, loan guarantees, cheap rental of public land, and research and development (R&D) grants [14]. According to the Organisation for Economic Co-operation and Development [15], almost 800 individual policies support the production or consumption of fossil fuels. According to [16], estimated fossil fuel subsidies were \$4.9 trillion worldwide in 2013 and \$5.3 trillion in 2015 (6.5% of global gross domestic product (GDP) in both years). Another form of subsidy, an indirect one, takes place when governments do not tax fossil fuel companies e fficiently [16]. This means that the price that consumers pay for coal, gas, or oil does not consider the damage that these products cause, such as climate change or air pollution, making green projects less viable than fossil fuel energy projects. Targeting the subsidies and allocating them to the low-carbon sector will increase the rate of return of these projects and make them interesting to private investors.

On the other hand, another challenge is the lack of capacity for and information about green technologies among the market players (governments, investors, and financial institutions). Moreover, the lack of green data and green databases is an obstacle to the development of these projects. Lack of borrowers' green and environmental information limits banks' abilities to assess the environmental risks involved in project and corporate finance [17]. In this regard, establishment of a centralized database at the industry level would enable the lenders to assess the business and market risks related to the environment.

### **3. Enabling Conditions of Green Finance**

To overcome the challenges mentioned in Section 2, this section provides practical solutions to create enabling conditions for green finance.

### *3.1. Increasing the Role of PFIs*

The first and most important challenge for financing green projects is the lack of access to long-term finance and investment (Section 2.1). PFIs, or publicly created and/or mandated financial institutions, could be important entities for filling the financing gap in the green sector. For example, five PFIs in Europe—France's Caisse des Dépôts Group, Germany's KfW Bankengruppe, the United Kingdom's Green Investment Bank, the European Bank for Reconstruction and Development for transition economies, and the European Investment Bank—provided more than €100 billion in equity investment and financing for energy e fficiency, renewable energy, and sustainable transport projects during the period 2010–2012 [18].

It is important for PFIs to open a separate file for green financing. They also need to integrate environmental considerations into conventional project financing. For example, the Japan Bank for International Cooperation (JBIC) launched GREEN operations in 2010, using measurement, reporting, and verification (J-MRV) as a method to evaluate the greenhouse gas emission reductions of the projects that it finances. Along with the basic concept and procedures for quantifying the reductions, the J-MRV guidelines include individual methodologies for each sector of the project and/or technology, varying from renewable energy to transport. When the GREEN operations began, J-MRV had only three methodologies for the projects that were most in demand, including renewable energy and the installation of energy-e fficient industrial equipment. However, the JBIC gradually developed new methodologies, increasing to 10 in 2016 [19].

Although the role of PFIs could be very important, some important points need to be considered regarding the involvement of PFIs in green financing. The first point is that they need to focus more on long-term financing (long-term loans) than commercial private banks, the resources (deposits) of which are short-term (one, two, or three years). Private banks are not able to provide long-term loans, so the

maturity of PFI loans has to be longer than that of private banks. The second point is to set a stable and fixed interest rate that is lower than that of private banks, the interest rates of which often fluctuate, since green projects need a stable and fixed interest rate for steady growth. Private banks have to pay taxes and set up branch o ffices, so they have more costs than governments, which translate into higher interest rates than those of PFIs. The third point is to mitigate the negative e ffects of government lending through PFIs by limiting the government's role as a lender. This implies o ffering PFI loans only when private banks cannot provide loans and avoiding the crowding-out e ffect on private banks. A successful case in this regard is the German KfW, in which the government funding with low interest rates passes through private banks to green projects, housing, small and medium-sized enterprises (SMEs), and so on rather than via direct lending from the government to projects. Accessing funds with lower interest rates is crucial for the development of the green sector. [20] examined 133 representative utility-scale photovoltaic and onshore wind projects in Germany over the last 18 years. Their empirical results revealed that the financing conditions have strongly improved. As drivers, they identified the macroeconomic conditions (the general interest rate) and experience e ffects within the renewable energy finance industry.

### *3.2. Increasing the Share of NBFIs in Long-Term Investments*

Just in institutions in OECD countries, investors are managing more than \$100 trillion of assets. An increasing number of institutional investors have adopted strategies to mitigate climate exposure. These include negative screening (the exclusion of non-green sectors/companies from portfolios), positive screening (the proactive identification of positive climate themes), active ownership (the exercising of statutory rights to promote green standards in portfolio companies), sustainability ratings (portfolio scoring based on green criteria), and the hedging of climate risks (through portfolio allocation or the use of derivatives). These strategies reflect specific fund manager mandates and the recognition that climate risks can have a tangible impact on corporate valuations and, as a result, institutional fund performance [21]. Most recently, it has become apparent that two major pressures from investors and regulators can also boost the participation of institutional investors in green projects. On the side of investors, environmentally friendly green concerns increasingly a ffect people's saving and investment decisions. This trend is happening especially among the younger generations. Savers with NBFIs are asking for stricter compliance with environmental, social, and governance criteria.

On the side of regulators, some jurisdictions are debating whether financial institutions should have a mandate to integrate environmental, social, and governance issues into their investment decision policies. One example is the Financial Stability Board's creation of the Task Force on Climate-Related Financial Disclosures, which has recommended that global organizations enhance their financial disclosures related to the potential e ffects of climate change [21].

However, when we look at the actual activities of institutional investors, their asset allocation to direct infrastructure investment in general remains small—less than 1% for OECD pension funds—and the "green" investment components are even more limited. These issues relate to the perception that green investments do not o ffer a su fficiently attractive risk-adjusted financial return and the fact that institutional investors still lack knowledge and expertise, as well as investment channels [22]. Using the spillover e ffect of the green energy supply and reducing the risk of their investment through GCGSs will increase their eagerness to engage in green projects (Sections 3.3 and 3.4).

### *3.3. Using Spillover Tax to Increase the Rate of Return*

Governments often regulate electricity tari ffs and usually do not determine them based on market mechanisms. Regulated tari ffs make it di fficult for private investors to invest in infrastructure projects because of the low rate of return on their investments. Increasing investment incentives requires the use of the spillover e ffects that energy supplies originally created and refunding the spillover tax revenues to investors in energy projects. The energy supply brings factories and businesses into the electrified region. The power supply enables the construction of new residences, and the property

value increases. Corporate income taxes and sales taxes also rise in areas with a new energy supply. Local or central governments collect these spillover tax revenues, and they do not usually return them to investors in energy projects. Investors only receive the user charges/electricity tariffs accruing from the electricity supply. If part of the spillover tax revenues was returned to private investors, their rate of return would increase over a prolonged period, and this could support their maintenance costs [10].

Figure 3 shows that the total rate of return on a green energy project in the first year is almost zero because of the large initial investment. In addition, the spillover impact of the energy supply on the region is very low or almost zero, as it takes time for the spillover to affect the regional output and for the tax revenue of local and central governments to emerge.

**Figure 3.** Using GCGSs and spillover tax in green projects. GCGS = green credit guarantee scheme. Source: Authors' compilation.

From *t*, the rate of return and the spillover tax start to increase. If the private investor relies only on user charges for the revenue of the project, the rate of return in the initial stages is very low and it takes time to increase, so the project is not viable. Hence, we suggest injecting the increase in the tax revenue generated from the spillover effect of the energy supply into the green project to secure at least the *R* rate of return (the benchmark rate). However, the spillover tax is not sufficient until *t*∗, so governments can issue long-term government bonds with *N* years of maturity to support the private investors until then. In the absence of a bond market, a green credit guarantee fund/corporation could provide a supporting role for securing the *R* rate of return. We believe that, although the establishment of a GCGS will impose a cost and budget burden on the government in the initial years until *t*∗, the future increases in the tax revenue due to the spillover effect of the energy supply would compensate for it. In addition, thanks to the tax revenue due to the spillover effect, the government will obtain additional revenue, as Equation (1) shows:

$$\int\_{0}^{t^\*} \left( \overline{\mathcal{R}} - \text{Actual return} \right) < \int\_{t^\*}^{\mathcal{N}} \left( \text{Actual return} - \overline{\mathcal{R}} \right) \tag{1}$$

#### *3.4. Collecting Carbon Tax from Polluting Industries and Injecting It into Green Projects*

One way to increase the rate of return on green projects is to inject the carbon tax collected from polluting industries and firms into green projects. This policy would make green projects attractive for private investors while forcing polluting firms to shift to greener technologies. Although this policy may increase the production costs and raise the price levels initially, it will increase the R&D expenditures on these technologies in the medium term because of the higher demand for green

technologies—thus reducing the costs via technological progress. In this subsection, we show how imposing carbon taxation can change firms' behavior and induce them to use greener technologies.

 We consider the Cobb–Douglas production function for these firms, while α*i* and β*i* are the elasticity of production of capital and labor, respectively. There is a constant return to scale, hence α*i* + β*i* = 1.

Equations (4) and (5) show the profit equations for firms 1 and 2:

$$
\pi\_t^1 = P\_t^1 y\_t^1 - r\_t^1 K\_t^1 - w\_t^1 L\_t^1 \tag{4}
$$

$$
\pi\_t^2 = P\_t^2 y\_t^2 - r\_t^2 K\_t^2 - w\_t^2 L\_t^2 \tag{5}
$$

where π1 *t* and π2 *t* denote the profit of firms 1 and 2; *P*1 *t* and *P*2 *t* show the output prices for the products of firms 1 and 2, respectively; *r*1 *t* and *r*2 *t* denote the interest rate that firms 1 and 2 pay on their borrowed capital from the bank; and *w*1 *t* and *w*2 *t* denote the wage rates that firms 1 and 2 pay to their labor inputs.

Firms follow profit maximization behavior. To find the optimal level of *Ki t* that maximizes the profit of each firm, we obtain the first-order condition, as in Equations (6) and (7):

$$\frac{\partial \pi\_t^1}{\partial \mathcal{K}\_t^1} = \frac{\alpha\_1 y\_t^1}{\mathcal{K}\_t^1} = r\_t^1 \to \mathcal{K}\_t^1 = \frac{\alpha\_1 y\_t^1}{r\_t^1} \tag{6}$$

$$\frac{\partial \pi\_t^2}{\partial \mathcal{K}\_t^2} = \frac{\alpha\_2 y\_t^2}{\mathcal{K}\_t^2} = r\_t^2 \to \mathcal{K}\_t^2 = \frac{\alpha\_2 y\_t^2}{r\_t^2} \tag{7}$$

The allocation of the total funds in this economy is equal to the sum of the capital of both firms, as in Equation (8):

$$\mathbf{K}\_t^T = \mathbf{K}\_t^1 + \mathbf{K}\_t^2 \tag{8}$$

The objective of the government in this economy is to maximize the sum of the outputs of both firms, as in Figure 4.

In the previous case, we did not consider the level of emissions (carbon dioxide). However, in reality, each firm has carbon emissions. In the case below, we consider that each firm has not only a di fferent level of output but also a di fferent level of emissions, and the production functions are as in Equations (9) and (10):

$$\log\left(y\_{t'}^1 \, \bigcirc \mathcal{O}\_t^1\right) = f\_t^1\left(\mathcal{K}\_{t'}^1 L\_t^1\right) \tag{9}$$

$$\mathcal{S}\_2\Big(y\_t^2, \mathcal{CO}\_t^2\Big) = f\_t^2\Big(\mathcal{K}\_t^2, \mathcal{L}\_t^2\Big) \tag{10}$$

where *CO*1 *t*and *CO*2 *t*show the carbon emissions of firm 1 and firm 2, respectively, at time t.

In addition to output (gross domestic product (GDP)) maximization, the second objective of the government is to minimize the carbon emissions of both firms (Equation (11)):

$$\text{Min }\text{CO}\_{t} = \text{CO}\_{t}^{1} + \text{CO}\_{t}^{2} \tag{11}$$

Therefore, the ultimate objective of the government is to maximize the cumulative output (GDP) and minimize the cumulative carbon emissions, as in Equation (12):

$$\mathcal{W} = \mathcal{W}\_1 (y\_t - y^\*)^2 + \mathcal{W}\_2 (\mathcal{CO}\_t - \mathcal{CO}^\*)^2 \tag{12}$$

where *y*∗ is the GDP in full employment (the desired GDP level) and (*yt* − *y*∗) is the GDP gap. *CO*∗ is the desired emission level, and (*COt* − *CO*∗) is the gap between the current emission level and the desired emission level.

The chart on the left of Figure 5 shows two extreme cases. Point A reflects conventional economic theory—profit maximization without consideration of the environment. Point A is the maximization of the GDP (the sum of the outputs of firms 1 and 2). Point B just shows environmental concern and indicates the point that minimizes the sum of the emissions of firms 1 and 2. Since firm 1 has more carbon emissions, point B is where only firm 2, which has lower emissions, is producing and the output of firm 1 is zero. Points C and D show the optimal level of production for each firm, and the production function includes both the output and the carbon emission levels, as in Equations (9) and (10).

**Figure 4.** Allocation of funds to firms and output maximization. Notes: *e* is the equilibrium point, *K*1∗*t* denotes the optimal capital for firm 1, and *K*2∗*t* denotes the optimal capital for firm 2. Source: Authors' compilation.

Next, the government charges a carbon tax, which affects the profits of polluting firms. Equations (13) and (14) show the carbon production, which is a function of the capital and labor inputs. A higher level of output will emit more carbon dioxide.

$$\mathbf{C}O\_t^1 = \boldsymbol{\varphi}^1 \left(\mathbf{K}\_t^1, L\_t^1\right) = \left(\mathbf{K}^1\right)^{\mathcal{V}1} \left(L^1\right)^{\delta\_1} \tag{13}$$

$$^1\text{CO}\_t^2 = \wp^2(\text{K}\_t^2, \text{L}\_t^2) = \left(\text{K}^2\right)^{\mathbb{V}^2} \left(\text{L}^2\right)^{\delta\_2} \tag{14}$$

Equations (15) and (16) show the new profit equations of firms 1 and 2 after charging the carbon taxes, which will reduce their profits. We assume that the carbon tax rate is progressive, so a higher tax rate applies emission levels when industries pollute more. This is why firms 1 and 2 have different carbon tax rates, as in Equations (15) and (16) (*T*1*t*and *T*2*t*):

$$
\pi\_t^1 = P\_t^1 y\_t^1 - r\_t^1 K\_t^1 - w\_t^1 L\_t^1 - T\_t^1 \mathbb{C} \mathcal{O}\_t^1 \tag{15}
$$

$$
\pi\_t^2 = P\_t^2 y\_t^2 - r\_t^2 K\_t^2 - w\_t^2 L\_t^2 - T\_t^2 \text{CO}\_t^2 \tag{16}
$$

As is clear from Figure 6, the optimal level of production of both firms in the case of the charging of a carbon tax is not A or B but E, which lies between them; its position depends on many factors, including the level of emissions and the tax ratio.

**Figure 5.** Equilibrium levels of output with different objectives. GDP = gross domestic product. Note: *e* is the equilibrium point considering different objectives, *K*1∗*t* denotes the optimal capital for firm 1, and *K*2∗*t*denotes the optimal capital for firm 2. Source: Authors' compilation.

Note: Point E is the new equilibrium point after charging the carbon tax. Source: Authors' compilation.

**Figure 6.** Optimal level of output when charging carbon tax.

This carbon tax system will induce new firms to start investing in green technologies and establish their industries with green technologies. This will create a spillover effect of green industry/infrastructure in that region, as Section 3.3 explains.

We can express the spillover effects of green technologies as follows:

$$y\_t^{Gi} = h(K\_t^{Gi}, L\_t^{Gi}, E\_t^{Gi}) \tag{17}$$

where *yGt* is the output of the firm that has green production (e.g., green or green energy) and *KGt*, *LGt*, *and EGt*are the capital, labor, and green energy production inputs, respectively.

Equation (18) depicts the spillover effect of the green energy supply. The first part in the parenthesis shows the spillover effect and the second part shows the direct effect.:

$$dy\_t^{Gi} = \left(\frac{\partial \mathbb{I}}{\partial K\_t^{Gi}} \frac{\partial K\_t^{Gi}}{\partial E\_t^{Gi}} + \frac{\partial \mathbb{I}}{\partial L\_t^{Gi}} \frac{\partial L\_t^{Gi}}{\partial E\_t^{Gi}} + \frac{\partial \mathbb{I}}{\partial E\_t^{Gi}}\right) dE\_t^{Gi} \tag{18}$$

The spillover effect of the green (green) energy supply will increase the tax revenue of the government from the region that has the green energy supply. In Equation (19), we assume that the government will inject 50% of the increase in government tax revenue into green projects and retain the other 50% as the government's ultimate revenue:

$$Tdy\_t^{\odot i} = 50\% \text{ (goverment)} + 50\% \text{ (low} - \text{carbon projects}) \tag{19}$$

As Equations (20) and (21) show, the injection of tax revenue originally generated from the spillover effect of the green energy supply, and the carbon taxes collected from polluting industries will increase the rate of return on green projects and induce private sector investment in the green sector:

$$r\_t^{G1} = \frac{a^{G1} y\_t^{G1}}{K\_t^{G1}} + T\_t^1 \mathcal{CO}\_t^1 + a \left( T dy\_t^G \right) \tag{20}$$

$$r\_t^{G2} = \frac{a^{G2} y\_t^{G2}}{K\_t^{G2}} \ \ + \ T\_t^2 \mathcal{CO}\_t^2 + b \left( T dy\_t^G \right) \tag{21}$$

where *rGi t* is the rate of return on the green (green) project and α*GiyGi t KGi t* is the initial rate of return on the green project, which is very low. Relying on this alone, the project will not be feasible or attractive to private investors. *TitCOit* is the carbon tax that the government charges on *i* polluting projects (firms) and then injects into green projects, while *TdyGt* is the tax revenue of the government from the spillover effect of the green energy supply. Percentage *a* of this increase in tax revenue will return to project 1 and percentage *b* will return to project 2 to increase their rates of return. We assumed earlier that *a* + *b* = 0.5, which means that the government will inject 50% of the increase in tax revenue that the spillover effect of the green energy supply caused into green projects and take the other half as its final tax revenue. As the implementation of this scheme shows, the rate of return on green projects will increase; on the other hand, the carbon taxation will force the polluting industries to shift to cleaner industries and green technologies.

#### *3.5. Development of Green Credit Guarantee Schemes to Reduce the Credit Risk*

Credit guarantee corporations (CGCs) are public institutions supporting sectors that lack access to finance (SMEs and start-ups). CGCs serve as a guarantor and cover the risk of lending to risky sectors, such as SMEs. Japan initiated CGCs in the 1930s, and presently, many developing and developed economies are using CGCs, especially in the SME sector. The green CGCs that [7] initially proposed improve the creditworthiness of green (green) projects that lack physical collateral and have a weak credit rating.

Figure 7 shows three participants in GCGs—banks, green projects, and green CGCs. Green CGCs will increase the loan supply to green projects. The green credit guarantee for green projects will reduce the asymmetry of information and decrease the expected default losses, because the CGC (government) guarantees a portion of the loan default, so banks will want to lend money to guaranteed green projects. An investor in a green project applies for a green credit guarantee when submitting the loan application. Then, a green CGC performs a creditworthiness evaluation of the project and the project owner (individual or corporate). Not all green projects are eligible to receive a guarantee—this depends on the borrower's credit score and an evaluation of the probability of success of the green project. Depending on the results of the creditworthiness assessment of a green credit guarantee corporation (GCGC), the investor pays a guarantee fee or premium. The fee depends on the project risk rating and the borrower's credit score. Using the same guarantee fee for all borrowers would create a moral hazard [23]. Banks also need to apply to a GCGC for a green credit guarantee, which issues the relevant certificate. Next, the bank disburses the loan to the green project (borrower), and the borrower starts to pay the loan instalments. In the case of default, the GCGC compensates a portion of the loan amount—the credit guarantee ratio—and subrogates the bank. An adjustment of the optimal credit guarantee ratio is necessary to avoid moral hazard [23]. This means that healthy banks that manage their nonperforming loans and have higher creditworthiness should receive a higher credit guarantee ratio from the government, while unsound banks need a lower guarantee and very risky banks do not obtain a guarantee. The regulator for a green finance's selection of an optimal guarantee ratio also creates an incentive for financial institutions to improve their creditworthiness to receive a higher guarantee ratio.

**Figure 7.** GCGS for the management of green credit risk. CGC = credit guarantee corporation and GCGS = green credit guarantee scheme. Source: The authors following [7]. Note: The numbering shows the sequence of the workflow.

### *3.6. Addressing Green Investment Risks via De-Risking*

Various financial and nonfinancial risks are associated with green projects, as Section 2.2 explained. Since risks have an impact on access to credit, it is very important to mitigate them. De-risking is a potentially powerful policy option to redirect financial flows to green investments in two ways: financial and policy. Financial de-risking lowers the perceived risks and requires returns and, thus, reduces the investment costs. Financial de-risking can occur as a result of transferring a large portion of the risk to another party; for example, insurance risks of governments or development banks, loan guarantees, or support policies like feed-in-tari ffs, subsidies, or low-carbon promotion tools, such as carbon pricing. These measures are not necessarily limited to countries receiving investments, and multilateral organizations, such as development banks, employ them equally. The UNFCCC Green Climate Fund (GCF) with a target value of US \$100 billion/yr by 2020 is expected to play an important role in financial de-risking and delivering the required investment levels for the large-scale renewable energy projects in developing countries in line with the Paris Agreement [24].

Unfortunately, in many developing countries, financial de-risking to date has mainly related to coal, while only a small fraction has focused on green alternatives [25]. The green credit guarantee scheme that this paper proposes is a tool for reducing the financial risk for private investors, as the GCGS (government) covers a portion of the risk. [26] discussed the possibility of financial de-risking by increasing the transparency and improving the laws and regulations. De-risking can increase the effectiveness and e fficiency of policies aiming to attract green investments [27]. Policy de-risking instruments are including programs, policies, and regulations that reduce the risks of private sector investments in green and low-carbon projects that are typically implemented by the governments. In utility-scale renewable energy, these policies and instruments include auction processes and reforms to ensure financially sound utilities (cost-recovery). In energy e fficiency, the de-risking instruments include the design, implementation, and enforcement of various minimum energy e fficient standards, such as green building codes or in lighting and appliances [28].

### *3.7. Summary of Tools and Instruments for Green Investments*

Table 1 presents the tools and instruments that this section outlined, as well as suggestions for reducing the risk of green projects, raising the rate of return, increasing the capacity of the investors and other stakeholders, and facilitating access to finance and investment.

**Table 1.** Tools and instruments for green investments.

Sources: Authors based on: [7,10,18,27,29].

### **4. Example of Green Finance Management**

In this section, we provide an example of the development of an environmental project. The objective is to show how the implementation of a GCGS, which this paper proposes, can reduce the risk of investment in projects. One of the major challenges that cities, mainly mega cities, in developing countries face is the environmental impact of generating huge amounts of solid waste. This issue is severe in Asia, as the Asian urban population increases by 44 million people every year. By 2050, half of the world's population will live in Asia and the Pacific countries [30]. The People's Republic of China generates 150 million tons of waste annually, and globally, it is the largest generator of municipal solid waste; India ranks second in the world [31]. One of the major obstacles to the development of solid waste management projects is the lack of a municipal budget and the low interest of private investors in this sector because of the low rate of return. Many municipalities in large Asian cities allocate more than 20% of the municipal budget to solid waste management.

In this section, we suggest practical funding schemes for fixed capital and working capital to incentivize solid waste management for private investors. These schemes are applicable to green and environmental projects, including waste-to-energy projects, which face similar barriers.

### *4.1. CGSs for Providing Fixed Capital*

As mentioned earlier, many countries have used CGSs in various forms over the decades to increase the flow of funds to targeted sectors and segments of the economy that have di fficulties accessing finance, including SMEs. A CGS absorbs the risk, and the guarantee that it provides acts as collateral. Therefore, by reducing the level of risk, banks are more willing to lend to borrowers. In addition, as the CGS acts as a guarantor, it needs to assess the creditworthiness of the borrower by monitoring the status of the project or borrower to improve the quality of lending.

Figure 8 shows that a CGS has three players. The first is the borrower, which can be a green project. In this example, it is a waste management project seeking finance. When borrowers approach a bank, they often refuse to lend because of the asymmetry of information and a lack of collateral. The second player is the lender, which is a financial institution (bank). The third player is the guarantor, which is the CGC, which usually the government runs, providing a full or partial guarantee. The CGS has a cost, so the borrower needs to pay a credit premium to the CGC. However, in the early stages of development of a CGC, it needs enough capital to cover the risks, so it requires government support. After some years, it can become financially sustainable. For this example, this scheme is especially applicable to sectors that require large fixed capital, waste-to-energy projects, or green projects. In this example, as is clear from Figure 8, the central government or the municipality funds the CGC. After assessing the credit history of the borrower (individual or corporate) and evaluating the feasibility of the project, the CGC agrees to provide this project with a guarantee. Then, it guarantees a certain amount (e.g., 80%), and the borrower proceeds to approach a bank to apply for a loan. When the bank sees that about 80% of the loan amount has a guarantee, it is eager to lend to this project. For the remaining 20%, the bank may ask for collateral. As this is a small amount, it is easier for the project owner to provide it.

**Figure 8.** Establishment of a CGS for reducing the supply–demand gap of finance in waste management projects. CGC = credit guarantee corporation. Source: Authors.

### *4.2. Establishment of Community-Based Funds for Providing Working Capital*

In addition to the fixed capital, the second major challenge facing waste management and many green projects (e.g., waste-to-energy projects) is the di fficulty in funding their working capital. Therefore, it is important to design a scheme that can adapt to the socioeconomic environment in Asia to help the private sector fund the working capital of these projects.

In many large cities, landfills occupy large tracts of land, and the space is limited. By establishing sorting, recycling, composting, and waste-to-energy facilities, the freed landfills could work better to support other, more beneficial purposes for generating user charges, rent, and making revenue from the sale of electricity generated from waste for municipalities or private investors, which could be a sustainable source of funding for working capital.

Figure 9 illustrates a waste management trust fund, a type of community-based funding or hometown investment trust fund for providing working capital for these projects.

**Figure 9.** Establishment of community-based funds for waste management projects. NGO = non-governmental organization and WMTF = waste management trust fund. Source: Authors.

In Japan, the development of hometown investment trust funds occurred mainly after the Fukushima nuclear power disaster in March 2011, when the government shut down the nuclear power plant as it was unsafe. Many people, especially in the a ffected region of Fukushima, showed an interest in renewable energy, such as solar and wind power, instead of nuclear power [32]. However, green projects carry a high risk, and most banks are reluctant to lend to them. Therefore, local people started to collect small amounts of money (\$100–\$5000) from the region through a local fund to build a solar power plant and wind power generator. They planned to establish a green energy plant, generate electricity, use the power that they generated, sell the excess to the power company, and make some profit. This was the reason for the establishment of the hometown investment trust funds, with the basic idea of connecting the investors to the projects in their hometown. Individual investors can invest a small amount of money through the internet in the projects toward which they are sympathetic. The investors not only seek a profit but also are well-disposed toward the project or the region. If these projects are successful, the banks will also be interested in lending to them. We believe that hometown investment trust funds are suitable for waste management projects, and the waste management trust fund is a new type of hometown investment trust fund targeting waste management projects. Hometown investment trust funds have expanded from Japan to Cambodia, Malaysia, Vietnam, and Mongolia [4]. Similar funds apply to green projects in developing countries, especially in regions where communities are integrated and trust exists among their members. These funds will help high-risk sectors, including the green sector, to grow.

As Figure 9 shows, waste management trust funds are project-oriented and designed for running waste management projects. The working capital comes from three sources: (1) rents from the freed

landfills, (2) user charges from waste generators (the facility can burn the waste of other regions or other countries and receive the charges and fees—as occurs in many European cities), and (3) the sale of the electricity generated by the waste. In addition, the waste management trust funds receive donations from locals, as well as central governments from their municipal and urban management budgets in the form of seed money for increasing the rate of return of the projects.

### **5. Conclusions and Policy Recommendations**

The lack of long-term financing, the existence of various risks, the low rate of return, and market actors' lack of capacity are major challenges for developing green projects. Using the spillover e ffects on green energy projects would increase the rate of return of these projects. PFIs can use both traditional and innovative approaches to link green projects with finance by enhancing their access to capital, facilitating risk reduction and sharing, improving the capacity of market actors, and shaping broader market practices and conditions. PFIs should avoid the negative e ffects of government lending (crowding out of private investment) by engaging in long-term lending at stable rates and only lending where private banks cannot lend. The green R&D sector is among the sectors for which PFI lending is suitable. GCGSs will reduce the asymmetry of information and decrease the expected default losses, thereby covering part of the risk and unlocking financial institutions' private investments and lending to green projects. GCGCs and PFIs can play important roles in credit enhancement and reduce the risks and improve the capacity for the adoption of a green economy. To prevent moral hazard, the guarantee ratio of GCGCs needs to be variable, depending on the creditworthiness of borrowers and financial institutions, and not fixed. To achieve a sustainable financing scheme of banks and NBFIs, it is important for the government to consider leverage, transparency, and specific results in the financial scheme (the case of a waste management project).

**Author Contributions:** F.T.-H. contributed to this research article in the formal analysis, investigation, resources, data curation, and writing of the original draft. N.Y. contributed to this research article for conceptualization and methodology. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by JSPS Kakenhi (2019-2020) Grant-in-Aid for Young Scientists No. 19K13742 and Grant-in-Aid for Excellent Young Researcher of the Ministry of Education of Japan (MEXT).

**Acknowledgments:** This research paper is output of the research project titled: "Unlocking the potentials of Private Sector Financing for Accelerated Low-Carbon Energy Transition (Phase II)" of the Economic Research Institute for ASEAN and East Asia (ERIA) which done by the authors. Authors are grateful to ERIA and the Asian Development Bank Institute (ADBI) for supporting this research. The paper benefited from the precious comments received from the anonymous referees of *Energies* and authors are grateful to them.

**Conflicts of Interest:** The authors declare no conflicts of interest.


*Article* **Experimental Investigation on the Machinability of PCBN Chamfered Tool in Dry Turning of Gray Cast Iron**

**Ganggang Yin 1,2,\*, Jianyun Shen 3 , Ze Wu 2 , Xian Wu 3,\* and Feng Jiang 4**

**Abstract:** Polycrystalline cubic boron nitride (PCBN) tools are widely used for hard machining of various ferrous materials. The edge structure of the PCBN cutting tool greatly affects the machining performance. In this paper, dry turning experiments were conducted on gray cast iron with a PCBN chamfered tool. Both the cutting temperature and the cutting force were measured, and then the surface quality and tool wear mechanisms were analyzed in detail. It was found that the cutting temperature and cutting force increased with the increase in feed rate, depth of cut, and cutting speed. The surface roughness firstly decreased, and then increased with an increase in feed rate. The minimum surface roughness was obtained with a feed rate of 0.15 mm/r which exceeded the tool chamfer width. The PCBN tool wear mode was mainly micro notches on the rake face and micro chipping on the tool chamfer, while the adhesion wear mechanism was the main tool wear mechanism.

**Keywords:** PCBN tool; gray cast iron; surface quality; tool wear

#### **1. Introduction**

Cast iron materials are widely used in the automotive industry, e.g., in engine boxes and brake discs [1,2]. In terms of hard machining cast iron, Martinho et al. investigated the continuous dry turning of gray cast iron using ceramic tools with and without diamond coating; the coated tools presented higher main cutting force and lower flank wear [3,4]. Heck et al. found the different wear behaviors of PCBN cutting tools during machining of compacted graphite iron compared to gray cast iron [5]. Grzesik et al. complexly assessed cutting force, cutting temperature, tool wear progress, and surface roughness in orthogonal turning of nodular cast iron with CBN tools with a chamfer width of 100 µm [6]. Chen et al. studied the influence of material properties on the machining performance in machining of high-chromium white cast iron with CBN tools, and it was found that the micro hardness of workpiece material showed significant impacts on the tool wear, tool life, cutting forces and surface quality [7].

The PCBN material has many excellent properties, such as high hardness, high temperature resistance, good chemical stability, and low friction coefficient [8,9]. These excellent properties make it become the most perfect cutting tool material for hard machining of various steel materials [10,11], such as hardened steel, cast iron, bearing steel, and superalloys. As one of the super-hard cutting tools, PCBN cutting tools have been widely applied in aerospace, automobile, and construction machinery manufacturing. There is extensive literature focused on the fabrication and applications of PCBN cutting tools [12,13]. Regarding the preparation of CBN tool materials, Yun et al. designed and fabricated CBN tool materials with a graded structure to improve material strength and toughness [14]. Mo et al. found that the increase in CBN percentage increased the hardness and wear resistance of PCBN composite, and then increased tool life in the cutting of hard-bearing steel GCr15 [15].

**Citation:** Yin, G.; Shen, J.; Wu, Z.; Wu, X.; Jiang, F. Experimental Investigation on the Machinability of PCBN Chamfered Tool in Dry Turning of Gray Cast Iron. *Processes* **2022**, *10*, 1547. https://doi.org/ 10.3390/pr10081547

Academic Editor: Raul D.S.G. Campilho

Received: 24 June 2022 Accepted: 31 July 2022 Published: 7 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Tamang et al. investigated the brazing of CBN material to tungsten carbide by microwave hybrid heating by applying a paste of Ag–Cu–In–Ti alloy [16]. Fei et al. attempted the pulsed magnetic treatment on CBN tools to improve the cutting performance and found a relatively lower cutting force and less wear area [17]. In terms of the applications of CBN cutting tools, Manoj et al. experimentally studied the machinability of AISI D6 tool steel using low-percentage-CBN tools. They analyzed the effect of machining parameters on cutting temperature, cutting force, and surface roughness, and they found that crater wear, micro chipping, and cutting edge breakage were the main tool wear mechanisms during continuous turning [18]. Gutnichenko et al. investigated the hard turning of helical gear hubs made of low-carbon alloyed steel after heat treatment with hardened up to HRC 60–63 using PCBN cutting tools; it was found that the main tool wear characterizations were flank wear and crater formation, and different strategies were also proposed to monitor tool wear and predict tool life [19]. Schultheiss et al. evaluated the tool wear mechanisms in the machining of gray cast iron with PCBN tools, the results showed that flank and notch wear were the main wear mechanisms, and the non-chamfered tools exhibited the higher notch wear [20].

The tool cutting edge geometry greatly affects the cutting performance, such as the tool nose radius and cutting edge passivation [21,22]. Kumar et al. investigated the effect of cutting parameters and tool geometry in hard machining of hardened AISI H13 material; it was found that the surface roughness decreased while the cutting forces increased with increasing tool nose radius [23]. The chamfered tool structure has a narrow edge ground on the tool rake face with a negative rake angle to enhance tool cutting edge strength. A chamfered tool can improve tool life and is widely used in the cutting tool treatment for rough machining [24,25]. Grzesik et al. developed an empirical model of friction for oblique cutting with CBN chamfered tools; it was found that the chamfered CBN tool decreased the friction coefficient [26]. Tang et al. developed a special chamfer on cutting tools and used it to suppress tool wear in the machining of hard material [27]. Souza et al. investigated the effect of edge preparation of PCBN tools on the wear performance during orthogonal turning of high-speed steels; the PCBN tools with ground land and honed edge radius, as well as a high CBN content, showed chipping in the rake face [28]. Ventura et al. studied the effect of cutting edge geometry on the tool wear behavior of CBN tools during interrupted turning of hardened steel; the results indicated that the chamfered cutting edge reinforces the cutting edge without excessively increasing the mechanical and thermal loads [29]. In summary, a reasonable tool geometry design is helpful to obtain better machining quality and tool life.

In this paper, to assess the machinability of the PCBN chamfered tool in hard machining of gray cast iron, dry turning experiments were performed on gray cast iron with the PCBN cutting tools. The effect of machining parameters on the cutting temperature and cutting force was measured. Then, the machined surface roughness and tool wear were studied in detail. The related machining mechanisms were analyzed to understand the cutting process according to the experimental results. The results are helpful for machining parameter selection in the machining of brittle materials with PCBN cutting tools.

#### **2. Experimental Procedure**

Due to their excellent properties, PCBN tools are commonly used in the machining of cast iron materials. The used cutting tool for rough machining in this work was the PCBN cutting tool (grade BN500, Sumitomo, Tokyo, Japan) prepared by Xiamen Xiazhi Technology Tool Co., Ltd., Xiamen, China, as shown in Figure 1. The grain size of CBN materials was about 1–2 µm, and the measured volume proportion of CBN material in the PCBN tools was about 56%. The PCBN tool presented a nose radius of 1.5 mm and cutting edge radius of 25 µm. The tool rake angle and clearance angles were 0◦ and 5◦ , while the cutting edge angle is 45◦ . The tool chamfer with a width of 0.1 mm and a negative rake angle of −20◦ was prepared on the rake face of the PCBN tool, as shown in Figure 1a.

The tool chamfer is helpful to enhance the cutting edge, and then avoid the premature chipping of the tool cutting edge in the machining of the brittle materials. angle of −20° was prepared on the rake face of the PCBN tool, as shown in Figure 1a. The tool chamfer is helpful to enhance the cutting edge, and then avoid the premature chipping of the tool cutting edge in the machining of the brittle materials. ping of the tool cutting edge in the machining of the brittle materials.

angle of −20° was prepared on the rake face of the PCBN tool, as shown in Figure 1a. The tool chamfer is helpful to enhance the cutting edge, and then avoid the premature chip-

*Processes* **2022**, *10*, x FOR PEER REVIEW 3 of 15

*Processes* **2022**, *10*, x FOR PEER REVIEW 3 of 15

Dry turning experiments were conducted on a CNC turning machine tool (CAK6150, Shenyang Machine Tool Co., Ltd., Shenyang, China) as shown in Figure 2a. This machine tool is widely used to manufacture rotary parts in various applications. The maximum spindle rotating speed was 2000 rpm. The workpiece material was gray cast iron HT200, which is widely used for the brake disc and engine cylinder in the automotive industry [20,30]. The material properties of the workpiece are listed in Table 1. The workpiece samples were cut into small pieces with dimensions of Ø 50 × 250 mm, and then clamped in a three-jaw chuck machine tool. In the turning experiments, the cutting temperature was measured using a thermal imager (TIM 200, Micro Epsilon, Germany), with a temperature measurement range of −20–900 °C. The cutting force was recorded using a dynamometer (9257B, Kistler, Winterthur, Switzerland), which is widely used in various applications for cutting force measurement [31]. The cutting force measurement system was composed of a dynamometer, connecting cable, charge amplifier (5080A, Kistler, Winterthur, Switzerland), DAQ system, and PC, as shown in Figure 2b. The sampling frequency used in cutting force measurement was 20 kHz to obtain the complete cutting force signal without Dry turning experiments were conducted on a CNC turning machine tool (CAK6150, Shenyang Machine Tool Co., Ltd., Shenyang, China) as shown in Figure 2a. This machine tool is widely used to manufacture rotary parts in various applications. The maximum spindle rotating speed was 2000 rpm. The workpiece material was gray cast iron HT200, which is widely used for the brake disc and engine cylinder in the automotive industry [20,30]. The material properties of the workpiece are listed in Table 1. The workpiece samples were cut into small pieces with dimensions of Ø 50 × 250 mm, and then clamped in a three-jaw chuck machine tool. In the turning experiments, the cutting temperature was measured using a thermal imager (TIM 200, Micro Epsilon, Germany), with a temperature measurement range of −20–900 ◦C. The cutting force was recorded using a dynamometer (9257B, Kistler, Winterthur, Switzerland), which is widely used in various applications for cutting force measurement [31]. The cutting force measurement system was composed of a dynamometer, connecting cable, charge amplifier (5080A, Kistler, Winterthur, Switzerland), DAQ system, and PC, as shown in Figure 2b. The sampling frequency used in cutting force measurement was 20 kHz to obtain the complete cutting force signal without distortion. Shenyang Machine Tool Co., Ltd., Shenyang, China) as shown in Figure 2a. This machine tool is widely used to manufacture rotary parts in various applications. The maximum spindle rotating speed was 2000 rpm. The workpiece material was gray cast iron HT200, which is widely used for the brake disc and engine cylinder in the automotive industry [20,30]. The material properties of the workpiece are listed in Table 1. The workpiece samples were cut into small pieces with dimensions of Ø 50 × 250 mm, and then clamped in a three-jaw chuck machine tool. In the turning experiments, the cutting temperature was measured using a thermal imager (TIM 200, Micro Epsilon, Germany), with a temperature measurement range of −20–900 °C. The cutting force was recorded using a dynamometer (9257B, Kistler, Winterthur, Switzerland), which is widely used in various applications for cutting force measurement [31]. The cutting force measurement system was composed of a dynamometer, connecting cable, charge amplifier (5080A, Kistler, Winterthur, Switzerland), DAQ system, and PC, as shown in Figure 2b. The sampling frequency used in cutting force measurement was 20 kHz to obtain the complete cutting force signal without distortion.

(**a**) (**b**) **Figure 2.** Experimental setup: (**a**) turning experiment, (**b**) cutting force measurement system. **Figure 2.** Experimental setup: (**a**) turning experiment, (**b**) cutting force measurement system. **Figure 2.** Experimental setup: (**a**) turning experiment, (**b**) cutting force measurement system.

In the experiment, the workpiece was pre-machined to ensure a smooth surface using a cemented carbide tool [32], and then the continuous turning experiments were performed. The detailed turning parameters are shown in Table 2. In single-factor experiments, four different levels were set for the machining parameters of spindle speed, feed rate, and depth of cut, according to the existing literatures [10,33]. The corresponding cutting speeds for different spindle rotating speeds were 110, 157, 204, and 251 m/min. The experiments

were divided into two stages; during single-factor turning experiments in the first stage, the feed travel for every level of machining parameters was 20 mm, while the total feed travel for every variable of the machining parameters was 80 mm. Then, to test the tool wear mechanism, the PCBN cutting tool was used for the continuous tool wear experiment to reach the feed travel of 4000 mm with fixed machining parameters (*v* = 204 m/min, *ap* = 0.2 mm, and *f* = 0.15 mm). After the experiment, the workpiece was cleaned using an ultrasonic cleaner, and then the surface roughness was inspected with a roughness meter (3220, Beijing Shidaizhifeng Instrument Co., Ltd., Beijing, China) at different positions along the feed direction. The sampling length and cutoff wavelength were 2.5 mm in the surface roughness measurement. Each experiment was repeated three times, and the averaging values were adapted as the results. The tool wear was inspected using a scanning electron microscope (SEM, JSM-IT500, JEOL, Tokyo, Japan).

**Table 1.** Material properties of gray cast iron HT200.

**Table 2.** Turning parameters.

#### **3. Results and Discussions**

#### *3.1. The Cutting Temperature*

In addition to the thermal image, the used thermal imager provides a camera to record a video image of the cutting process, as shown in Figure 3. The emissivity of the workpiece material was calibrated to about 0.5 in this work using a digital display heating platform. Combining the thermal image and video image, it was easier to observe the temperature field distribution of the cutting process. It was observed that the chips formed at the cutting zone with fragment shapes and were then sprayed around. In the recorded thermal image, the maximum temperature is exhibited in the cutting zone, and the sprayed chips present a second temperature source with heat dissipation during the spraying process. The mean temperature in the contact zone between the tool and workpiece is difficult to acquire; hence, during the turning experiment, the maximum temperature measured in the cutting zone and displayed in the upper right corner was adopted as the cutting temperature.

The cutting temperature dramatically affects the cutting process and the machining quality. Figure 4 shows the effect of machining parameters on the measured cutting temperature. The results show that the cutting temperature increased from 226 ◦C to 429 ◦C with the feed rate increasing from 0.05 mm/r to 0.2 mm/r. Its increasing rate was rapid at first but decreased for a feed rate larger than 0.1 mm/r. The cutting temperature increased from 278 ◦C to 423 ◦C with the cutting speed increasing from 100 m/min to 251 m/min. The initial increasing rate slowed after the cutting speed exceeded 157 m/min. This was probably due to the the heat dissipation area being similar under the same depth of cut when the cutting temperature was relatively higher with more extensive machining parameters, while the temperature difference with the surrounding environment became larger. Hence, the larger temperature difference caused more heat dissipation and resulted in a relatively low temperature increase. The cutting temperature almost linearly increased

from 257 ◦C to 369 ◦C with an increase in the depth of cut, while the heat dissipation area also increasing with the depth of cut. *Processes* **2022**, *10*, x FOR PEER REVIEW 5 of 15

**Figure 4.** Effect of machining parameters on cutting temperature: (**a**) effect of feed rate on cutting temperature; (**b**) effect of cutting speed on cutting temperature; (**c**) effect of depth of cut on cutting temperature. **Figure 4.** Effect of machining parameters on cutting temperature: (**a**) effect of feed rate on cutting temperature; (**b**) effect of cutting speed on cutting temperature; (**c**) effect of depth of cut on cutting temperature.

#### *3.2. The Cutting Force 3.2. The Cutting Force*

Figure 5 shows the recorded cutting force signal in continuous turning experiments with different feed rates. The three cutting force components in order of magnitude were the tangential force (main cutting force) Fz, the radial force (passive force) Fy, and the axial force (feed force) Fx. The radial force Fy was similar to the tangential force Fx. In the turning process, the depth of cut was much lower than the tool nose radius, and only a Figure 5 shows the recorded cutting force signal in continuous turning experiments with different feed rates. The three cutting force components in order of magnitude were the tangential force (main cutting force) Fz, the radial force (passive force) Fy, and the axial force (feed force) Fx. The radial force Fy was similar to the tangential force Fx. In the turning process, the depth of cut was much lower than the tool nose radius, and only

small segment of the tool nose arc took part in cutting process. The contact condition resulted in the equivalent tool cutting edge angle becoming small. Hence, the component of

and cut out process can be clearly observed, and the cutting force signal waveform was more stable under a large feed rate than a low feed rate of 0.05 mm/r. Under the low feed rate of 0.05 mm/r, the profound ploughing effect led to an unstable cutting process because of the small, undeformed chip thickness. The unstable cutting process resulted in noticeable fluctuations in the cutting force signal. The mean value of the cutting force in the stable cutting process was adopted to quantitatively analyze the cutting force results.

a small segment of the tool nose arc took part in cutting process. The contact condition resulted in the equivalent tool cutting edge angle becoming small. Hence, the component of the resultant cutting force in the radial direction Fy increased. From the results, the cut in and cut out process can be clearly observed, and the cutting force signal waveform was more stable under a large feed rate than a low feed rate of 0.05 mm/r. Under the low feed rate of 0.05 mm/r, the profound ploughing effect led to an unstable cutting process because of the small, undeformed chip thickness. The unstable cutting process resulted in noticeable fluctuations in the cutting force signal. The mean value of the cutting force in the stable cutting process was adopted to quantitatively analyze the cutting force results. *Processes* **2022**, *10*, x FOR PEER REVIEW 7 of 15 *Processes* **2022**, *10*, x FOR PEER REVIEW 7 of 15

**Figure 5.** The cutting force signal. **Figure 5.** The cutting force signal. **Figure 5.** The cutting force signal.

Figure 6 shows the cutting force varying with the machining parameters. The cutting forces Fx, Fy, and Fz increased from 6 N, 8 N, and 20.5 N to 36.5 N, 86.5 N, and 115.5 N, respectively, with the increase in feed rate. The increment in axial force Fx was much smaller than that in tangential force Fz and radial force Fy. A similar phenomenon to the cutting temperature was observed, whereby the increasing rate was rapid at first but decreased for feed rates larger than 0.1 mm/r. The cutting process was continuous turning in this work, and the adhesion material on the tool surface was small at the first stage. A lower tool adhesion caused a relatively low cutting force. Figure 6 shows the cutting force varying with the machining parameters. The cutting forces Fx, Fy, and Fz increased from 6 N, 8 N, and 20.5 N to 36.5 N, 86.5 N, and 115.5 N,respectively, with the increase in feed rate. The increment in axial force Fx was much smaller than that in tangential force Fz and radial force Fy. A similar phenomenon to the cutting temperature was observed, whereby the increasing rate was rapid at first but decreased for feed rates larger than 0.1 mm/r. The cutting process was continuous turning in this work, and the adhesion material on the tool surface was small at the first stage. A lower tool adhesion caused a relatively low cutting force. Figure 6 shows the cutting force varying with the machining parameters. The cutting forces Fx, Fy, and Fz increased from 6 N, 8 N, and 20.5 N to 36.5 N, 86.5 N, and 115.5 N, respectively, with the increase in feed rate. The increment in axial force Fx was much smaller than that in tangential force Fz and radial force Fy. A similar phenomenon to the cutting temperature was observed, whereby the increasing rate was rapid at first but decreased for feed rates larger than 0.1 mm/r. The cutting process was continuous turning in this work, and the adhesion material on the tool surface was small at the first stage. A lower tool adhesion caused a relatively low cutting force.

**Figure 6.** Effect of machining parameters on cutting force: (**a**) effect of feed rate on cutting force; (**b**)

**Figure 6.**

**Figure 6.** *Cont.*

(**c**)

(**c**)

lower tool adhesion caused a relatively low cutting force.

**Figure 5.** The cutting force signal.

**Figure 6.** Effect of machining parameters on cutting force: (**a**) effect of feed rate on cutting force; (**b**) effect of cutting speed on cutting force; (**c**) effect of depth of cut on cutting force. **Figure 6.** Effect of machining parameters on cutting force: (**a**) effect of feed rate on cutting force; (**b**) effect of cutting speed on cutting force; (**c**) effect of depth of cut on cutting force.

 (**a**) (**b**)

Figure 6 shows the cutting force varying with the machining parameters. The cutting forces Fx, Fy, and Fz increased from 6 N, 8 N, and 20.5 N to 36.5 N, 86.5 N, and 115.5 N, respectively, with the increase in feed rate. The increment in axial force Fx was much smaller than that in tangential force Fz and radial force Fy. A similar phenomenon to the cutting temperature was observed, whereby the increasing rate was rapid at first but decreased for feed rates larger than 0.1 mm/r. The cutting process was continuous turning in this work, and the adhesion material on the tool surface was small at the first stage. A

Although the cutting temperature increased with an increase in cutting speed, the cutting force increased gradually. A probable reason is that this cutting temperature was in the built-up edge (BUE) formation scope. A serious BUE in the cutting process increases the friction and material removal, resulting in a larger cutting force. The cutting speed should be further increased to avoid the BUE. The cutting forces Fx, Fy, and Fz increased from 4 N, 9 N, and 23 N to 65 N, 86 N, and 115 N, respectively, when the depth of cut increased from 0.1 mm to 0.4 mm. This is similar to the cutting temperature, whereby the cutting force linearly increased with an increase in the depth of cut.

#### *3.3. The Surface Quality*

Figure 7 shows the surface roughness Ra and surface morphology under different feed rates. It was found that the surface roughness firstly decreased and then increased with an increase in feed rate. The built-up edge was formed on the tool rake face due to the adhesion materials. When the built-up edge along the tool nose arc was uneven, the actual undeformed chip thickness along the tool nose arc was also uneven. Then, the uneven undeformed chip thickness led to transverse micro tears perpendicular to the cutting direction [34]. These micro tears on the surface morphology confirmed the formation of built-up edge in the cutting process.

Surface roughness is determined by the physical factors and theoretical residual area, which is proportional to the feed rate and tool geometry. As shown in Figure 8, the main physical factors significantly influencing the surface roughness included micro burrs and micro bulges induced by the ploughing effect and material plastic side flow, along with micro tears caused by the built-up edge [35]. When the feed rate was minimal, the ploughing effect and BUE formation were severe in the cutting process. This caused uneven scratch marks and many micro tears on the machined surface morphology, resulting in a large surface roughness. However, since the feed rate increased to larger than 0.2 mm, these physical factors were weakened, but the theoretical residual area became significant in terms of the surface morphology and was a dominant factor for the surface roughness. The large theoretical residual area induced a large surface roughness. The minimum surface roughness was obtained with a feed rate of 0.15 mm/r.

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(**b**)

side flow on surface morphology. **Figure 8.** Enlarged view of surface morphology: (**a**) micro tears on surface morphology; (**b**) material side flow on surface morphology. **Figure 8.** Enlarged view of surface morphology: (**a**) micro tears on surface morphology; (**b**) material side flow on surface morphology.

The variations of surface roughness and surface morphology with cutting speed are shown in Figure 9. It is shown that the surface roughness value gradually decreased with an increase in cutting speed. According to the surface morphology, the scratch marks on the machined surface decreased under the higher cutting speed, and a better surface roughness was obtained. This was probably due to the BUE formed in cutting process becoming the protrusion part in front of the tool tip. This led to the actual rake angle changing from a negative rake angle (equal to angle of tool chamfer) to a positive rake angle. This phenomenon improved the effective negative rake angle on the tool chamfer in the cutting process, which was equivalent to increasing the sharpness of the cutting edge. The sharper cutting edge could decrease the ploughing effect and material plastic side flow in the cutting process. Hence, more minor scratch marks were formed on the surface, as shown in Figure 8b. The variations of surface roughness and surface morphology with cutting speed are shown in Figure 9. It is shown that the surface roughness value gradually decreased with an increase in cutting speed. According to the surface morphology, the scratch marks on the machined surface decreased under the higher cutting speed, and a better surface roughness was obtained. This was probably due to the BUE formed in cutting process becoming the protrusion part in front of the tool tip. This led to the actual rake angle changing from a negative rake angle (equal to angle of tool chamfer) to a positive rake angle. This phenomenon improved the effective negative rake angle on the tool chamfer in the cutting process, which was equivalent to increasing the sharpness of the cutting edge. The sharper cutting edge could decrease the ploughing effect and material plastic side flow in the cutting process. Hence, more minor scratch marks were formed on the surface, as shown in Figure 8b.

**Figure 9.** Effect of cutting speed on surface quality: (**a**) effect of cutting speed on surface roughness; (**b**) surface morphology varies with different cutting speed. **Figure 9.** Effect of cutting speed on surface quality: (**a**) effect of cutting speed on surface roughness; (**b**) surface morphology varies with different cutting speed.

Figure 10 shows the surface roughness and surface morphology under different depths of cut. The surface roughness became larger with an increase in the depth of cut. The increasing rate was slow at first, before increasing when the depth of cut exceeded 0.3 mm. When the depth of cut was very small, the equivalent tool cutting edge angle was close to zero. The tool nose was helpful to squeeze the machined surface to be very flat, and a relatively low surface roughness was achieved. When the depth of cut reached 0.4 mm, on the basis of the surface morphology, it was found that the cutting marks became prominent, and scales appeared on the machined surface, which deteriorated the surface roughness. Figure 10 shows the surface roughness and surface morphology under different depths of cut. The surface roughness became larger with an increase in the depth of cut. The increasing rate was slow at first, before increasing when the depth of cut exceeded 0.3 mm. When the depth of cut was very small, the equivalent tool cutting edge angle was close to zero. The tool nose was helpful to squeeze the machined surface to be very flat, and a relatively low surface roughness was achieved. When the depth of cut reached 0.4 mm, on the basis of the surface morphology, it was found that the cutting marks became prominent, and scales appeared on the machined surface, which deteriorated the surface roughness.

**Figure 10.**  **Figure 10.**  The Tool Wear 3.4. The Tool Wear*

Figure 11 shows the rake face and flank face morphology of the PCBN cutting tool with a cutting distance of 4 m. Compared with the original tool morphology in Figure 1, despite the high hardness difference between the tool material and gray cast iron, there was noticeable tool wear according to the tool morphology. The tool wear on the rake face was much more severe than on the flank face. It was found that the PCBN tool wear mode mainly involved the rake face wear, including some micro notches and many adhesion materials. The distribution of adhesion materials on the rake face was beyond the tool surface range that actually took part in the cutting process. Due to the severe adhesion material and micro chipping, the tool chamfer at the rake face was worn, rendering its observation challenging. However, there was minimal adhesion material on the flank face with hardly any tool wear. The observed tool wear characterizations are consistent with the literature [19]. Figure 11 shows the rake face and flank face morphology of the PCBN cutting tool with a cutting distance of 4 m. Compared with the original tool morphology in Figure 1, despite the high hardness difference between the tool material and gray cast iron, there was noticeable tool wear according to the tool morphology. The tool wear on the rake face was much more severe than on the flank face. It was found that the PCBN tool wear mode mainly involved the rake face wear, including some micro notches and many adhesion materials. The distribution of adhesion materials on the rake face was beyond the tool surface range that actually took part in the cutting process. Due to the severe adhesion material and micro chipping, the tool chamfer at the rake face was worn, rendering its observation challenging. However, there was minimal adhesion material on the flank face with hardly any tool wear. The observed tool wear characterizations are consistent with the literature [19].

To confirm the composition of the adhesion material, an EDS analysis was performed, and the results are shown in Figure 12. It was found that, in addition to the elements of the PCBN tool, the main elements of the adhesion material constituted Fe, Si, Mn, etc. These elements corresponded to the composition of gray cast iron, confirming that the adhesion material was the workpiece material. These adhesion materials on the tool surface could easily generate the built-up edge phenomenon in the cutting process.

**Figure 11.** PCBN tool wear characteristics: (**a**) rake face; (**b**) flank face. **Figure 11.** PCBN tool wear characteristics: (**a**) rake face; (**b**) flank face. that the adhesion material was the workpiece material. These adhesion materials on the tool surface could easily generate the built-up edge phenomenon in the cutting process.

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**Figure 12.** EDS analysis of adhesion material. Figure 13 shows an enlarged view of the adhesion material on the rake face and flank face. It is revealed that the tool wear mechanism was mainly adhesion wear at the rake face and micro chipping at the tool chamfer. Under the action of the cutting force, the workpiece material and fragment chips were adhered and embedded on the tool surface, thus forming a bulge on the tool surface. These adhesion materials underwent serious friction and shear action with the continuous cutting process. Hence, some adhesion materials were fractured and fell off from tool surface. Simultaneously, this caused some tool Figure 13 shows an enlarged view of the adhesion material on the rake face and flank face. It is revealed that the tool wear mechanism was mainly adhesion wear at the rake face and micro chipping at the tool chamfer. Under the action of the cutting force, the workpiece material and fragment chips were adhered and embedded on the tool surface, thus forming a bulge on the tool surface. These adhesion materials underwent serious friction and shear action with the continuous cutting process. Hence, some adhesion materials were fractured and fell off from tool surface. Simultaneously, this caused some tool material to spall and drop off from the surface, and then micro notches were formed on the rake face, as shown in Figure 13a. This adhesion wear mechanism would continue periodically throughout the cutting process. A negative rake angle caused very serious stress distribution in the local zone on the tool chamfer. At the same time, the adhesion wear could induce some micro cracks or notches on the too chamfer, which could further develop into micro chipping under a large stress distribution. Figure 13 shows an enlarged view of the adhesion material on the rake face and flank face. It is revealed that the tool wear mechanism was mainly adhesion wear at the rake face and micro chipping at the tool chamfer. Under the action of the cutting force, the workpiece material and fragment chips were adhered and embedded on the tool surface, thus forming a bulge on the tool surface. These adhesion materials underwent serious friction and shear action with the continuous cutting process. Hence, some adhesion materials were fractured and fell off from tool surface. Simultaneously, this caused some tool material to spall and drop off from the surface, and then micro notches were formed on the rake face, as shown in Figure 13a. This adhesion wear mechanism would continue periodically throughout the cutting process. A negative rake angle caused very serious stress distribution in the local zone on the tool chamfer. At the same time, the adhesion wear could induce some micro cracks or notches on the too chamfer, which could further develop into micro chipping under a large stress distribution.

material to spall and drop off from the surface, and then micro notches were formed on the rake face, as shown in Figure 13a. This adhesion wear mechanism would continue periodically throughout the cutting process. A negative rake angle caused very serious stress distribution in the local zone on the tool chamfer. At the same time, the adhesion wear could induce some micro cracks or notches on the too chamfer, which could further

develop into micro chipping under a large stress distribution.

**Figure 13.** The adhesions on tool surface: (**a**) adhesion on rake face; (**b**) adhesion on clearance face. **Figure 13.** The adhesions on tool surface: (**a**) adhesion on rake face; (**b**) adhesion on clearance face.

#### **4. Conclusions 4. Conclusions**

This paper presented an experimental study on the drying turning of gray cast iron using a PCBN cutting tool. The following conclusions could be drawn: This paper presented an experimental study on the drying turning of gray cast iron using a PCBN cutting tool. The following conclusions could be drawn:

**Author Contributions:** Experiments and paper writing, G.Y. and X.W.; Measurement, J.S. and Z.W.; Data analysis, F.J. All authors have read and agreed to the published version of the manuscript. **Author Contributions:** Experiments and paper writing, G.Y. and X.W.; Measurement, J.S. and Z.W.; Data analysis, F.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 52075097 (W.Z.). **Funding:** This research was funded by the National Natural Science Foundation of China, grant number 52075097 (W.Z.).

**Institutional Review Board Statement:** Not applicable. **Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable. **Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable. **Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest. **Conflicts of Interest:** The authors declare no conflict of interest.

#### **References References**


**About the Editors**

**Michael Fromm** is a senior professor and former Head of the Institute of Clinical Physiology at the Charite—Universit ´ atsmedizin Berlin, Germany, where he still works. He has published ¨ more than 230 research articles, resulting in an h-index of 61. Through this, he has made seminal contributions to transport mechanisms and barrier functions of intestinal and renal epithelia in health and disease. For some claudins, he has discovered that they form channels selective for ions and/or water. Starting in 2006, he was the coordinator of the Deutsche Forschungsgemeinschaft (DFG) Research Unit "Molecular structure and function of the tight junction", which paved the way to a currently running DFG graduate school focusing on tight junction research. Within this, his lab focuses on protein prerequisites of tricellular tight junction water permeability.

**Susanne M. Krug** has studied Biochemistry and is now a Group Leader at the Institute of Clinical Physiology at the Charite—Universit ´ atsmedizin Berlin, Germany. She has made remarkable ¨ contributions to the understanding of the tricellular tight junction as a regulated passage site for macromolecules, especially in inflamed intestinal epithelium. Presently, she holds three grants of the Deutsche Forschungsgemeinschaft (DFG) and, based on >60 publications, has reached an h-index of 25. Her current research interests still deal with tricellular tight junctions, but also with interaction of immune cells and tight junction proteins in inflammatory bowel diseases.