**1. Introduction**

Major depressive disorder (MDD) is a common mood disorder that a ffects one's feelings, thoughts, and behavior. According to the DSM-5, for a diagnosis of MDD, five of the following symptoms need to be present for at least two weeks: depressed mood, reduced interest or pleasure, weight loss or reduced appetite, insomnia or hypersomnia, psychomotor agitation or retardation, fatigue or loss of energy, worthlessness or excessive guilt, impaired concentration or indecisiveness, and recurrent thoughts of death or suicidal ideation or attempts. Either 'depressed mood' or 'loss of interest or pleasure' is essential for a diagnosis [1]. The total number of people su ffering from MDD worldwide was estimated to be 322 million in 2015 and its prevalence increased by 18.4% between 2005 and 2015 [2]. Therefore, effective treatment of MDD merits intense consideration.

Whereas psychotherapy and antidepressant medication are e ffective in the majority of patients, approximately one third of patients do not respond to these therapies. In the Sequenced Treatment Alternatives to Relieve Depression (STAR\*D) trial, the cumulative remission rate of MDD patients after four successive treatments was 67% [3]. In line with this, a meta-analysis of 92 studies of the e ffectiveness of psychotherapy showed that 62% of patients no longer met the criteria of depression after treatment [4]. Failure to respond to a treatment algorithm of several steps is commonly referred to as treatment resistance, although there is still discussion about the exact definition of treatment refractoriness [5]. Treatment-resistant depression (TRD) is associated with more (comorbid) mental health disorders, a higher number of hospitalizations, and more suicide attempts, leading to higher treatment costs compared to non-TRD [6]. In addition, patients with TRD show a higher demand of healthcare resources and costs of health care compared to non-TRD patients [7]. Various alternative treatment options for TRD are currently being investigated, including vagal nerve stimulation [8], repetitive transcranial magnetic stimulation (rTMS) [9], and deep brain stimulation (DBS) [10].

The aim of this narrative review is to provide an overview of recent studies of the e ffectiveness of DBS in TRD with a special focus on the relationship between the targeted brain structures and clinical outcomes. Based on these findings, we discuss the importance of distinguishing between di fferent clinical phenotypes of depression that would allow for more personalized symptom-based treatment approaches, which may be a key factor in improving treatment outcomes.

### **2. Recent Insights on the Pathophysiology of Depression**

It is hypothesized that in depression, there is an imbalance in the limbic cortico-striatal-thalamo-cortical (CSTC) mood circuits [11,12], yet many aspects of circuitopathy in MDD remain largely unknown. Based on di fferent models [11,12], three main components of the CSTC mood circuits have been proposed (Figure 1). First, the ventral component is essential for recognizing emotions and initiating an adequate emotional and behavioral response. In this circuit, the amygdala, ventral striatum, ventral part of the anterior cingulate cortex, orbitofrontal cortex, ventrolateral prefrontal cortex, and downstream structures such as the hypothalamus and locus coeruleus are involved. Second, the dorsal component that regulates the emotional responses and requires cognitive processing. Here, the dorsolateral and dorsomedial prefrontal cortex, the dorsal part of the anterior cingulate cortex, and the hippocampus are involved. Third, a modulating region is present, although no consensus has been made about its precise anatomical organization and function. Some have suggested that this component consists of the thalamus and the rostral part of the anterior cingulate cortex [11–13]. As implied by Mayberg et al., the model of depression indicates that depression is associated with a decreased activity in dorsal limbic and neocortical regions and a relative increase in ventral paralimbic regions. Treatment of depression therefore requires the inhibition of the overactive ventral regions, resulting in the disinhibition of the underactive dorsal regions. To mediate this process, proper functioning of the rostral cingulate cortex is required [12]. These mood circuits overlap with the circuitry involved in compulsive traits; DBS of the ventral capsule/ventral striatum (VC/VS) in treatment resistant obsessive-compulsive disorder (OCD) patients has led to improvements in mood which prompted studying the application of DBS in TRD patients [14,15].

**Figure 1.** Schematic representation of emotional processing and its neurobiological base. Figure from Moonen et al. (2017) [16] with permission.

### *Expanding the Cortico-Striatal-Thalamo-Cortical Mood Circuits*

One region that is not included in the CSTC mood circuits and yet has been a region of interest for DBS targeting in depression for over a decade is the subgenual cingulate gyrus/cortex (SCG/SCC) [10]. This region has shown hyperactivity in untreated depressed patients [17], is part of the ventral component, and has projections to the amygdala, hippocampus, superior and medial temporal gyri, ventral striatum, mid- and posterior cingulate cortex, thalamus, hypothalamus, periaqueductal gray, and lateral habenula [18,19]. Furthermore, in recent years, it has become known that several other brain areas all belonging to the ventral component play a role in the pathophysiology of depression. Among these are the thalamic peduncles (THp) that interconnects with the prefrontal cortex including the orbitofrontal cortex (OFC) [20], the medial forebrain bundle (MFB) that projects to the frontal cortex, the NAcc and ventral striatum [21], and the ventral part of the anterior limb of the internal capsule (vALIC) which forms a homeostatic system with the MFB and the bed nucleus of the stria terminalis (BNST) [22] (Figure 2).

**Figure 2.** Cortico-striatal-thalamic-cortico mood circuits divided in a dorsal, ventral, and modulating compartment based on Alexander et al. [11], Mayberg et al. [12], and Moonen et al. [16] expanded with regions researched with deep brain stimulation (DBS) for treatment-resistant depression (TRD). DLPFC; dorsolateral prefrontal cortex, DMPFC; dorsomedial prefrontal cortex, ACC; anterior cingulate cortex, THp; thalamic peduncles, OFC; orbitofrontal cortex, VLPFC; ventrolateral prefrontal cortex, MFB; medial forebrain bundle, vALIC, ventral part of the anterior limb of the internal capsule, BNST; bed nucleus of the stria terminalis, SCG; subgenual cingulate gyrus, HPA axis; hypothalamic pituitary adrenal axis.

### **3. Deep Brain Stimulation for Treatment-Resistant Depression**

DBS is an invasive neuromodulation technique that is effective in managing clinical symptoms of neurological and psychiatric disorders, such as Parkinson's disease (PD) [23,24] and OCD [25]. At stimulation settings commonly used in clinical practice, DBS decreases the spontaneous firing of neuronal populations and activates axonal projections near the electrode [26]. This modulates pathological activity and replaces it with regular patterns of discharge with intervals of burst activity [27, 28]. More recent theories suggest that DBS destabilizes abnormal synchronous oscillatory activity within the basal ganglia circuitry improving hyperkinetic symptomology [24]. However, the exact mechanism(s) by which DBS normalizes electrical activity in the basal ganglia and exerts beneficial effects on PD symptoms remain unknown. In DBS for TRD, target selection has mostly been based on either neuroimaging studies or clinical observations of mood improvement following DBS in OCD [10,15,29]. For these reasons, the underlying mechanisms of action are poorly studied. DBS studies for TRD (Table 1) and the outcomes for selected brain targets (Table 2) are described below.

*J. Clin.*  **2020**,

*Med.*

 *9*, 2729

*J. Clin. Med.* **2020**, *9*, 2729

### *J. Clin. Med.* **2020**, *9*, 2729

*J.*

"-": not investigated; (d) ACC; (dorsal) anterior cingulate cortex, AG; amygdala, CES-D score; center for epidemiologic studies depression score, (d) CN; (dorsal) caudate nucleus, DMN; default mode network, DTI; deterministic diffusion tensor imaging, GP; globus pallidus, Hip; hippocampus, Hth; hypothalamus, MFB; medial forebrain bundle, OFC; Orbitofrontal cortex, (m/dl/dm/vm/vl) PFC (medial/dorsolateral/dorsomedial/ventromedial/ventrolateral) Prefrontal cortex, PGR; posterior gyrus rectus, PTN; pedunculopontine tegmental nucleus, Pt: putamen, RN; raphe nuclei, SN; substantia nigra, Th; thalamus, TRN; thalamic reticular nucleus, VP; ventral pallidum, VS; ventral striatum, VTA; ventral tegmental area.

### *3.1. Subgenual Cingulate Gyrus*/*Cortex*

The first clinical trial of DBS of the SCG for TRD was performed in 2005 and included six patients with MDD [10]. The severity of depression was measured using the Hamilton Depression Rating Scale (HDRS) and the Montgomory Asberg Depression Rating Scale (MADRS). The HDRS has been the gold standard for the assessment of depression for years [51]. A clinical response is commonly defined as a decrease in the HDRS score of more than 50% compared to baseline, and clinical remission is defined as a decrease in the HDRS score to eight or less. After one month, two out of six patients met the criteria for response. At the end of the sixth month, a response was seen in four out of six patients, with three of the patients reaching remission or near remission. Preliminary observations with positron emission tomography (PET) showed a metabolic hyperactive SCG (Brodmann area 25, Cg 25) during depressive states. It was speculated that DBS would reduce this hyperactivity [17] (Table 2). The improvement in depression scores after DBS was thought to be due to e ffectively disrupting focal pathological activity in limbic-cortical circuits. After 3 months of stimulation of the subgenual cingulate region (CG25) in patients su ffering from TRD, local cerebral blood flow (CBF) was decreased in CG25 and the adjacent orbitofrontal cortex (Brodmann area 11). Moreover, after three and six months of stimulation, CBF was decreased in the hypothalamus, anterior insula, and medial frontal cortex of long-term responders, while CBF increased in the dorsolateral prefrontal cortex (dlPFC), dorsal anterior, posterior cingulate, and premotor and parietal regions (Table 2) [10]. In the di fferent open-label trials, response rates varied from 20 to 57% after 1 month, 33.3 to 87.5% after 6 months, and 29 to 62.5% after 12 months (Table 1) [10,33,49,52–61]. In a long term follow-up, Kennedy et al. (2011) reported response rates at 1, 2, and 3 years after DBS implantation in the SCC of TRD patients of 62.5%, 46,2%, and 75%, respectively [52] (Table 1). In a case series of DBS of the SCG in five TRD patients, a decrease in the score of the depression rating scale was only found in one of the five TRD patients. This patient turned out to be stimulated in the posterior gyrus rectus (PGR) based on single subject tractography results rather than the initially targeted CG25 [62]. A recent exploratory meta-analysis of four observational studies investigating DBS for TRD (Holtzheimer et al. 2012, Lozano et al. 2012, Puigdemont et al. 2012, and Kennedy et al. 2011) reported relatively large response and remission rates following DBS treatment: the twelve-month response and remission rates were 39.9% (95% CI = 28.4% to 52.8%) and 26.3% (95% CI = 13% to 45.9%). The included studies reported a significant decrease in depression scores between 3 and 6 months (Hedges' g = −0.27, *p* = 0.003), while no additional decrease was found between 6 and 12 months, suggesting that maximal antidepressant e ffects occur mostly within the first 6 months of treatment [63]. However, adverse events can occur, including worsening of depression, suicidal ideation, and seizures (Table 1). A study consisting of a double-blind active vs. sham stimulation phase of four weeks, followed by an open-label stimulation for up to 24 months, reported no significant di fferences between the active and sham stimulation of the SCG and no reduction in HDRS scores in the first four weeks. In the open-label phase, response rates were 37.5%, 43% and 23% after 6, 12 and 28 months, respectively. Remission rates were 12.5% and 14.2% at 6 and 12 months, respectively, and 33.3% at 24 and 28 months [64].

A randomized controlled trial (RCT) investigating DBS of the subcallosal cingulate, known as the BROADEN trial, was aborted prematurely. The study lasted six months, during which all patients should have received SCC implantation surgery. After six months, blinding would have been uncovered and both groups would have been o ffered open-label DBS for another six months. At the end of the first six months, responses of the treatment group and control group were predicted to be 40% and 18.5%, respectively. In this trial, the response rate was defined as more than or equal to a 40% decrease in MADRS scores from baseline. However, after six months, only 20% of patients (*n* = 12) in the treatment group showed a response versus 17% of patients (*n* = 5) in the control group. At that time, a futility analysis predicted the probability of a successful study outcome to be 17% or less leading to the funding for DBS electrodes for this study to be discontinued. The actual study was never published, but results were published and mentioned in Morishita et al. (2014) [65,66]. It has been postulated that the patients enrolled in the BROADEN trial had extreme and chronic depression with a mean duration of the current depressive episode of 12 years, nearly twice that of previous open-label studies. Therefore, these patients could have required a longer treatment period before significant results emerge. Long-term outcomes of SCG DBS in TRD patients for up to 8 years show that most patients have a sustained antidepressant response [60]. However, these results need to be interpreted carefully as the patient group consisted of both MDD and bipolar type-II disorder patients. Further comparison between high- and low frequency DBS in the SCG in TRD showed no significant di fference in e ffectiveness between the two groups and a 44.44% response rate at 13 months of stimulation [67].


Figure 3 shows the response surface plots related to MRR. Figure 3a, in particular, depicts the variation of MRR with pulse-on time and pulse-off time. It is seen from the graph that the productivity of the EDM process considerably increases with a combination of higher values of both input parameters. It does not apply to lower settings for T-on values, however, when increasing, T-off presents an adverse effect on MRR. Similarly, the increase in the MRR can be observed with a combination of higher T-on and higher flushing pressure DFP, as shown in Figure 3b. Figure 3c shows, in turn, that generally higher MRR can be obtained for a combination of higher DFP settings and lower T-off settings. As it can be also noted, increasing the value of T-off in process settings generally leads to a decrease in the productivity of the WEDM process of the Inconel 617 alloy.

**Figure 3.** Response surface 3D plots representing the MRR dependence on: (**a**) T-on and T-off, for DFP = 0.5 MPa; (**b**) T-on and DFP, for T-off = 10 μs; (**c**) T-off and DFP, for T-on = 0.9 μs.

#### *3.2. Evaluation of the Dimensional Accuracy of the Machining Process*

The width of the kerf KW formed in the EDM-machined work material by a passing wire can be a measure of the dimensional accuracy of the process [11,23]. Figure 4 shows exemplary samples of kerfs made in Inconel 617 material. The averaged values of measurements for kerfs obtained in different machining conditions (correspondingly to individual runs of the experiment) are included in the matrix of Table 4.

**Figure 4.** The photographs used in the measurements of kerf width KW: (**a**)—kerf with initial zone, (**b**)—kerf for run 1, (**c**)—kerf for run 5, hi—depth of initial zone.

The width of the kerf can be defined by the equation:

$$K\mathcal{W} = d\_{\mathcal{W}} + \mathcal{Z}G\_{\mathcal{s}} \tag{10}$$

where: *KW*—kerf width (mm), *dw*—diameter of wire (mm), *Gs*—lateral spark gap (mm).

As the value of wire diameter (*dw*) is constant, the change of the kerf width (KW) results in changes of width lateral spark gaps (*Gs*). The distance of lateral sparks gap is dependent on the energy of the discharges and affects the process accuracy. Figure 4 shows the photos of the kerfs made during the tests. It can be seen that the edges of the kerfs are fuzzed and measurement of the kerf width might be inaccurate. In the initial section of kerfs (Figure 4a), the machining process is rather unstable. Thus, the section was excluded from the measurements. Figure 4b shows the kerf of the run 1 with measurement lines, while Figure 4c shows the kerf for the run 5, which corresponds to the process conditions of the highest efficiency.

The adequate regression model was determined to quantify the effect of selected input parameters on this measured response of the WEDM process studied. Calculated values of regression coefficients for the individual terms of the determined response surface model for KW are listed in Table 7. According to the data contained in this Table (see the respective *p*-values), in addition to the parameter T-on of the linear terms, only the T-off 2 of the quadratic terms and T-on∗T-off of the interaction terms have a significant effect on KW. As also seen in the table, the values of the coefficient R-squared and the standard deviation of the residual component—S, obtained in respective ANOVA test, are at the proper level.

**Table 7.** Estimated coefficients of the regression model and summary statistics of adopted response surface full quadratic model for KW.

S = 0.00532291; PRESS = 0.00135; R-Sq = 93.36%; R-Sq (pred) = 36.72%; R-Sq (adj) = 81.41%.

The results of respective ANOVA test presented in Table 8 show the significance of the regression model for KW (obtained *p*-value < 0.05), as well as its adequacy, since the respective requirement of the F-test is met (calculated F-ratio equal to 24.85, significantly exceeds its tabulated value), all at 95% confidence level. It is also to be noted that *p*-value for "Lack of Fit" of 0.615 implies the non-significance of the source element, as desirable. Normal probability plot of the residuals for the KW process response is presented in Figure 5. The regularities in distribution of the residuals observed in the graph confirm that the performed analysis could be validated.

**Table 8.** ANOVA test results of response surface model for KW.

**Figure 5.** Normal probability plot of the residuals for KW.

Figure 6 shows the response surface 3D plots representing the KW dependence on the interacting input parameters. As generally seen, the interactions between the particular machining parameter are far more complicated than a simple rule of thumb, and their combined effect on the process response is ambiguous to much extent. Figure 6a, in particular, depicts the variation of KW with pulse-on time and pulse-off time. As can be seen in the figure, higher accuracy (i.e., smaller KW values) can basically be obtained for the combination of lower values of T-on and T-off, conversely to their effect on process productivity (the MRR response), discussed in the previous section. Figure 6b shows that the process accuracy will be higher (i.e., smaller value of KW) with a combination of smaller T-on and DFP, while a small share of the DFP parameter in the increase in the value of the measured response (KW) can be observed. The graph in Figure 6c in turn reveals that the machining accuracy increases (lower KW) for the extreme settings of the value of the T-off parameter (the lowest or the highest), as compared with its mid-settings, however, the range of observed changes in the value of this parameter is quite narrow. At the same time, a limited influence of the DFP parameter on values of the measured response can be noted.

**Figure 6.** Response surface 3D plots representing the KW dependence on: (**a**) T-on and T-off, for DFP = 0.5 MPa; (**b**) T-on and DFP, for T-off = 10 μs; (**c**) T-off and DFP, for T-on = 0.9 μs.

#### *3.3. Evaluation of Surface Roughness*

Many of the mechanical and physical properties of mechanical parts, such as corrosion resistance, friction, fatigue strength and loading capacity, depend on the roughness of the machined surfaces [11]. Thus, the surface roughness (SR) obtained in the WEDM process, and expressed by the average roughness height (Ra) parameter, was another machining response characteristic for which a regression model was determined. The respective values of coefficients (R-squared and S) for the developed full quadratic model, that determine the quality of its fitting, are included in Table 9, along with the appropriate coefficients related to the individual model terms. As can be concluded based on the results included in this table, solely the linear parameter of T-on is significant in controlling SR (Ra). The obtained results of ANOVA test for the probability aspect P and those related to the F-test (see Table 10) indicate the significance and the adequacy of the derived response surface model for Ra, at the 95% confidence level. In addition, a relevant normal probability plot of the residuals for this output characteristic (Figure 7) has been provided that validates this part of performed analysis.

Figure 8 contains the response surface 3D plots representing the SR dependence on the interacting input parameters. Thus, Figure 8a shows the variation of Ra parameter with the change of T-on and T-off. As seen from the graph, SR considerably decreases with the decrease in pulse duration (T-on), and in fact, regardless of changes in the T-off setting. A similar effect could be observed while analyzing the variability of the Ra with the change of T-on and DFP parameters (Figure 8b). A similar effect could be observed while analyzing the variability of SR with the change of T-on and DFP parameters (Figure 8b).

**Table 9.** Estimated regression coefficients and summary statistics of adopted response surface full quadratic model for Ra.

S = 0.135622; PRESS = 0.90955; R-Sq = 96.43%; R-Sq (pred) = 64.65%; R-Sq (adj) = 89.99%.

**Table 10.** ANOVA test results of response surface model for Ra.

**Figure 7.** Normal probability plot of the residuals for surface roughness parameter of Ra.

In this instance, Ra also drops down significantly with the decrease in T-on, whereas the influence of the latter parameter is irrelevant. As can be observed in turn in Figure 8c, the dependence of the output parameter Ra on T-off and DFP is vaguer. However, for that pair of interacting parameters, the lowest values of Ra will be obtained at the lowest possible settings of the values for T-off.

**Figure 8.** Response surface plots of Ra versus: (**a**) T-on and T-off, for DFP = 0.5 MPa; (**b**) T-on and DFP, for T-off = 10 μs; (**c**) T-off and DFP, for T-on = 0.9 μs.

The Ra parameter is an average value, and it is not sufficient to characterize the geometrical properties of the surface therefore the paper is supplemented by selected surface profilograms, material ratio curves and probability density functions, in order to show the operating features of obtained surfaces.

Figure 9 presents a profilogram of the surface after processing, in terms of the roughness and waviness. This presented structure applies to run 8 according to the Table 4. The roughness was filtered from the primary profile using a 0.8 mm cut off filter. Comparing the profiles shown at (a) and (b) in Figure 9, it can be seen that the roughness is the dominant element of the obtained surface structure, and from a practical point of view, the waviness in this structure is insignificant. Figure 10 shows the material ratio curves and the frequency density curve for the profiles presented in this Figure. Based on the above given curves, it can be concluded that the roughness profile unevenness structure is approximately symmetrical, i.e., the range of the peaks and valleys is comparable. The shape of material ratio curve indicates that the main cause of roughness is the EDM machining process is the characteristic of discharges. During the measurements, it was observed that the structure of unevenness slightly changes with the change of the process parameters, i.e., with certain process parameters peaks begin to dominate in the structure, and for other parameters, valley is the dominant element in the surface structure. Due to the fact that the observed changes are of very little importance for the rough machining cuts realized in this research, they have not been included.

On the other hand, by observing the waviness, and in particular, the material fraction curve and the amplitude density curve, it can be seen that the resulting waviness is influenced by several factors. These can be related to the process and the machine design features. As waviness is not the dominant factor in the surface structure, it can be neglected in the roughing cut process.

**Figure 9.** The surface roughness profile (**a**) and the surface waviness profile (**b**), as measured in parallel direction to the wire feed.

**Figure 10.** The material ratio curves and frequency density curves for the profiles measured in parallel direction to the wire feed: (**a**)—for the roughness, (**b**)—for the waviness.

Figures 11 and 12 show the surface structure in the direction perpendicular to the wire feed direction. Comparing these graphs with those in Figures 9 and 10, it can be seen that there are no significant differences in the roughness structure, while there are some differences in the waviness. They may result from wire vibrations during machining, whereas their influence on the surface structure effects is insignificant for rough machining.

**Figure 11.** The surface roughness profile (**a**) and the surface waviness profile (**b**), as measured in perpendicular direction to the wire feed.

**Figure 12.** The material ratio curves and frequency density curves for the profiles measured in parallel direction to the wire feed: (**a**)—for the roughness, (**b**)—for the waviness.

#### **4. Conclusions**

The effect of various control parameters such as pulse-on, pulse off time, and the dielectric flow pressure on WEDM performance measures, i.e., MRR, the cutting accuracy and SR of the Inconel 617 alloy material have been experimentally investigated. The response surface modelling scheme based on the RSM approach has shown its advantages in explaining the impact of each working parameter on the values of the resultant response characteristics.

The most important results of the experimental work can be summarized as follows:

Further research work will be focused on the investigation into the surface layer characteristics being constituted in wire EDM processes, realized under differentiated machining conditions.

**Author Contributions:** Conceptualization, S.D. and M.S.S.; methodology, S.D. and M.S.S.; software, S.D. and M.S.S.; validation, S.D. and M.S.S.; formal analysis, S.D. and M.S.S.; investigation, S.D. and M.S.S.; resources, S.D. and M.S.S.; data curation, S.D. and M.S.S.; writing—original draft preparation, S.D. and M.S.S.; writing—review and editing, S.D. and M.S.S.; visualization, S.D. and M.S.S.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

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

#### **References**

© 2020 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 (http://creativecommons.org/licenses/by/4.0/).

## **Experimental Study on the Influence of Machining Conditions on the Quality of Electrical Discharge Machined Surfaces of aluminum alloy Al5052**

#### **Angelos P. Markopoulos 1,\*, Emmanouil-Lazaros Papazoglou 1 and Panagiotis Karmiris-Obrata ´nski 1,2**

Received: 20 January 2020; Accepted: 27 February 2020; Published: 2 March 2020

**Abstract:** Although electrical discharge machining (EDM) is one of the first established non-conventional machining processes, it still finds many applications in the modern industry, due to its capability of machining any electrical conductive material in complex geometries with high dimensional accuracy. The current study presents an experimental investigation of ED machining aluminum alloy Al5052. A full-scale experimental work was carried out, with the pulse current and pulse-on time being the varying machining parameters. The polishing and etching of the perpendicular plane of the machined surfaces was followed by observations and measurements in optical microscope. The material removal rate (*MRR*), the surface roughness (SR), the average white layer thickness (*AWLT*), and the heat affected zone (HAZ) micro-hardness were calculated. Through znalysis of variance (ANOVA), conclusions were drawn about the influence of machining conditions on the EDM performances. Finally, semi empirical correlations of *MRR* and *AWLT* with the machining parameters were calculated and proposed.

**Keywords:** electrical discharge machining; surface roughness; white layer formation; heat affected zone; ANOVA; aluminum alloy Al5052

#### **1. Introduction**

Electrical discharge machining (EDM) is one of the most extensively used non-conventional machining processes, with many applications in the modern industrial environment. The main advantage of EDM is the capability of machining any electrical conductive material, regardless of its mechanical properties, e.g., strength and hardness, in complex geometries and with high dimensional accuracy. EDM finds a wide range of applications in the fields of die and mold manufacturing, aerospace, automotive industries, micro-electronics, and biomedical engineering [1]. EDM is a thermoelectric process, as the material erosion mechanism is the result of a series of discrete electrical discharges. These occur between the electrode and workpiece, which are immersed in a dielectric fluid [2]. The electrical energy turns into thermal, generating a plasma channel between the cathode and the anode. With plasma temperatures in the range of 8000 to 12,000 ◦C, the material is heated up to its melting and/or ablation point. When the pulsating current supply is turned off, the plasma channel breaks down, allowing the dielectric fluid to flush the molten and ablated material in the form of microscopic debris.

In contrast to the plethora of studies pertaining to EDM of steel workpieces, there are only a few works about machining aluminum and aluminum alloys. Keeping in mind that aluminum and its alloys are extensively used in the aerospace and automotive industry, and its use as a matrix in composite materials, it is, beyond the scientific interest, extremely useful to be studied as a workpiece material in EDM. Khan [3] studied and analyzed the wear of the electrode during EDM. Mild steel and aluminum were used as workpiece materials and copper and brass as the tool electrode. Among the conclusions was that during machining, the mild steel electrode undergoes more wear than in the machining aluminum. This is due to the higher thermal conductivity of aluminum that results in less energy dissipation to the electrode. Gatto et al. [4] verified the machinability of three aluminum alloys, namely Al2219 and Al7050, which are used in aeronautical applications, and Al7075, a common alloy for pre-series molds. Surface finish, dimensional and geometrical accuracy of the workpieces and the wear mechanism of the electrodes were evaluated. Radhika et al. [5] investigated the influence of pulse current (10–30 A), pulse on time (120–420 μs), and dielectric flushing pressure in EDM of aluminum hybrid composites by using the Taguchi method. The material removal rate (*MRR*), the tool wear ratio (TWR), and the surface roughness (SR) were calculated or measured. Imran et al. [6] studied the machining of Al6061 with EDM under a variety of pulse current (6–12 A) and pulse-on time (60–200 μs) conditions, using graphite electrode and two different dielectric mediums. *MRR*, TWR, surface integrity in terms of the average white layer thickness (*AWLT*), and the SR were calculated. Finally, Kandpal et al. [7] and Selvarajan et al. [8], through their review papers, show the increasing interest in machining metal matrix composites (MMC), including those with an aluminum matrix, with EDM.

The current study is an experimental investigation of the effect of machining parameters, namely the pulse current IP and the pulse on time *Ton*, on the machining of aluminum alloy Al5052 with EDM. The choice of the alloy is based on its properties, as it has very good corrosion resistance to seawater and industrial atmosphere, and is used for metal matrix composites fabrication. [9] Furthermore, the working conditions under EDM processing of this material have not been thoroughly investigated, yet. In the analysis, *MRR* is calculated, Ra and Rt are measured, as indications of the machined surface quality, and through microscopic observation, *AWLT* and White Layer (WL) morphology are estimated. Finally, after the experimental procedure, non-linear regression models are calculated and proposed, correlating the *MRR* and the *AWLT* with the machining parameters, namely, the pulse current and the pulse on time.

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

An ANGIETRON EMT 1.10 die sinking machine was used for the experiments. The workpieces were two rectangular blocks of aluminum alloy Al5052, and on each face, in successive positions, four experiments were conducted. The chemical composition of Al5052 alloy and its physical properties are presented in Table 1. A rectangular copper electrode with dimensions of 38 × 23 mm was used as the working tool, which was properly cleaned between experiments to avoid any depositions that may affect the results. A rectangular copper electrode with dimensions of 38 × 23 mm was used as the working tool, and was cleaned properly between experiments to avoid any depositions that could affect the results. Finally, a low viscosity hydrocarbon mineral oil (Castrol SE FLUID 180) was used as the dielectric fluid. Its low viscosity ensured sufficient cooling and debris flushing, even for narrow spark gaps. This dielectric fluid is recommended for machining with EDM all ferrous and non-ferrous metals.

**Table 1.** Chemical composition and physical properties of aluminum alloy Al5052.

A full-scale experiment was carried out, with varying parameters, the pulse current IP and the pulse-on time *Ton* at four levels for each one. The 16 sets of the experiments' parameters are analytically presented in Table 2. In all experiments, straight polarity was used, with 100 and 30 V as the open and close circuit voltage, respectively. Moreover, the dielectric fluid flushing pressure was constant for all the experiments, while the duty cycle was automatically adjusted by the machine, for optimized machining efficiency. The workpiece weight before and after machining, the total machining time, the ammeter indication of the mean current intensity ¯ IP, and the machining efficiency (feff) were transcribed for subsequent calculations, evaluation, and analysis. For a more accurate calculation of the real machining time tm, the "jumping cycles", namely the periodic movement of the servo head for facilitating the debris removal during machining, were considered. Furthermore, the average and the maximum surface roughness Ra and Rt, respectively, were measured, calculated as the mean from five measurements made on each machined surface. For the values of the machined surfaces, the proper stylus tip, and the cut-off wavelengths were adopted according to EN ISO 4287:1998. Specifically, the stylus tip was 10 μm, and the shortest and longest cut-off wavelength was 2.5 and 8 mm respectively. The measuring system Mitutoyo SV-4500 CNC was used.

Finally, the machined surfaces in their perpendicular plane were grinded, polished, and chemically attacked with etchant, which was composited by 92 ml distilled water, 6 ml nitric acid and 2 ml hydrofluoric acid, and immersion of samples for 20 s in the bath. The etched surfaces were observed in optical microscope and the *AWLT* was calculated, while observations about the WL quality and morphology were made. The *AWLT* was calculated by dividing the area of the WL with its corresponding length. At the end, the micro-hardness of the material, just below the WL, was measured in order to establish the existence of a heat affected zone (HAZ). The micro-hardness was calculated as the mean from six measurements at each specimen using the Vickers hardness test.

**Table 2.** Experimental parameters and results.

The *MRR* was calculated by using Equation (1):

$$MRR = \frac{W\_{\rm st} - W\_{\rm fin}}{\rho \cdot t\_{\rm m}},\tag{1}$$

with *MRR* the material removal rate in mm3/min, *Wst*., *Wfin* the workpiece weight before and after machining, respectively, in g, ρ the workpiece material density in g/mm3, and tm the machining time in min.

The duty cycle (η) is calculated by using Equation (2):

$$
\eta = \frac{\overline{I\_P}}{I\_P} \tag{2}
$$

with *¯IP* the ammeter indication of the mean current intensity in A, and *IP* the pulse current in A. The experimental results are also shown in Table 2.

#### **3. Results and Discussion**

#### *3.1. Material Removal Rate*

The results of Table 2 and the respective diagrams of Figure 1 indicate that the *MRR* is strongly depending on the nominal machining power *Pnom*, namely the pulse-on current *IP*, as the machining voltage is constant. In general, higher pulse current leads to higher *MRR*, while the way that the pulse-on time affects *MRR* is not clear, as can be seen in Figure 1a. Thus, the real interest focuses on the interaction of machining parameters, as it is presented in the interaction plot of *MRR*, see Figure 1b. For example, the *MRR* for *IP* = 15 A and *Ton* = 300 μs is lower than this for *IP* = 15 A and *Ton* = 100 μs. Moreover, the *MRR* for *IP* = 15 A is almost the same for *Ton* equal to 100, 200, and 500 μs. In contrast, for *IP* = 21 A the maximum *MRR* has been measured for *Ton* = 200 μs, while the *MRR* for 300 μs is higher than that for 100 μs; this is the exact opposite in relation with the 15 A pulse current. From the aforementioned, it can be concluded that the interaction between the machining parameters is far more complicated than a simple rule of thumb, like higher pulse current and/or pulse-on time leads to higher *MRR*.

One of the main interests about EDM, is the energy consumption, as indication of the machining efficiency and the economic feasibility of the process. The energy consumption is analog to the mean machining power, which is calculated by using Equation (3):

$$P\_{av} = P\_{nom} \cdot \eta = V\_P \cdot I\_P \cdot \eta\_\prime \tag{3}$$

with *Pav* the mean machining power in W, and *Pnom* the nominal machining power in W.

As it is presented clearer in Figure 2a, the higher mean machining power does not always lead to higher *MRR*. Moreover, for the same mean power, the *MRR* depends on the machining parameters combination. For example, for *MRR* of approximately 175 mm 3/min, using *Ton* = 100 μs, a mean power of 270 W is needed when for the same *MRR*, using *Ton* = 500 μs, 22.2% higher mean power is needed, i.e. 330 W. At the same time, for a mean power of 390 W, the *MRR* varies from 224 to 274 mm 3/min, i.e. a 22.3% increase. As a conclusion, for the removal of the same material quantity, the required energy depends on the machining parameter combination, namely the pulse-on current and the pulse-on time, as these are the major parameters which define the power density and the per-pulse energy during the machining.

The explanation of this "behavior" is given by understanding the material removal mechanism. There are two main factors which determine the process' efficiency in EDM: the debris concentration and the gassy environment in which the spark occurs. A portion of the discharge energy is spent on re-melting and evaporating the debris material, and as a result, the efficiency is decreased. The same result, i.e., efficiency decrease, comes by the fact that a portion of the discharge takes place in a gaseous environment, a phenomenon that Gostimirovic et al. [10] pointed out in their study, too. Thus, high discharge power, which creates high debris concentration, or/and high pulse-on time, which results in a gassier environment, can decrease the efficiency of the process, with the corresponding influence on the *MRR*.

As an overall conclusion, and as a rule of thumb, it can be said that the increase in the pulse-on current results in an increase in *MRR*, while the pulse-on time has a more fuzzy and minor effect on *MRR*. As the main effect plot indicates, and as the interaction plot confirms, higher *MRR* can be achieved with higher nominal machining power, namely, higher pulse-on current.

**Figure 1.** (**a**) Main effects plot for *MRR*. (**b**) Interaction plot for *MRR*.

**Figure 2.** (**a**) *MRR* vs. mean machining power. (**b**) *MRR* vs. *IP* and *Ton*.

From the experimental data and assuming a nonlinear relation, Equation (4) emerged, where *MRR* is expressed as function of *IP* and *Ton*:

$$MRR = 12.77 \cdot I\_p^{0.89} \cdot T\_{\text{ou}}^{0.038} \,\text{}^{\tag{4}}\tag{4}$$

with *MRR* in mm3/min, *IP* in A, and *Ton* in μs. The above equation indicates that the pulse current *IP* is a far more significant factor for *MRR* in contrast with the pulse-on time, as its exponent is about 20 times higher; this is confirmed in the diagram of Figure 1a.

#### *3.2. Surface Roughness*

The surface roughness depends on the geometrical characteristics of the produced craters, on the machined surface. The average craters' volume, among other parameters, as the material's thermal properties and dielectric liquid composition, depends on the discharge pulse energy. Pulse-on time and pulse current affect the crater's geometry in a different way. A simplified approach is that an increase in the pulse-on time allows the discharge channel to expand, and results in relatively big craters, while pulse current mainly reflected the depth of the produced craters [1].

The main effects plots for Ra and Rt, depicted in Figure 3a,b, respectively, indicate that the change in pulse current does not have a significant effect on Ra and Rt. This observation is confirmed by 2-samples-t test for the different pulse current values, which result in p values greater than 0.05, see Table 3. On the other hand, increasing the pulse-on time from 100 μs to 200, 300, and 500 μs results in a statistically significant change on Ra and Rt. Other changes in *Ton*, e.g., from 200 to 500 μs, do not affect in a statistically significant manner Ra and Rt.

It has to be mentioned that the surface roughness is a far more complicated phenomenon. Not only factors such as pulse-on time, pulse current, and material thermal properties should be taken into consideration, but also their interactions and overlapping.

**Table 3.** *p* Values of 2-samples-t test for pulse current and pulse-on time.

#### *3.3. White Layer and Heat A*ff*ected Zone's Micro-Hardness*

During the EDM process, the formatted craters represent only a percentage of the total melted material, as only a small amount of the material is ejected from the cavity, while the rest returns to the solid state, forming a re-solidified layer. Furthermore, as a small portion of the ablated-vaporized material has remained in close proximity to the workpiece surface, it reattaches on the surface after the end of the pulse, forming a layer known as the re-deposited layer. The re-deposited layer has a porous structure and may contain elements of the electrode material and by-products of the dielectric fluid [11]. Re-solidified and re-deposited layers are referred as the white layer.

Surface cracks are the result of extreme variations in temperature and pressure during EDM. The probability of crack formation depends on the pulse duration, current intensity, work material properties, and dielectric fluid. Although crack mechanisms are complicated, it can be said as a simplified general rule that the density of surface cracks increases with increasing the pulse energy [12]. Nevertheless, the formation and growth of cracks and micro-cracks are in major dependence with workpiece material thermos-physical properties.

In Figure 4a–d, the WL, as it is observed by microscopy imaging, is illustrated. The machining parameters are for constant pulse current *IP* = 18 A and different pulse-on time, namely *Ton* = 100, 200, 300. and 500 μs. Figure 5a–d depict characteristic details, in magnification, of each workpiece of Figure 4.

It is evident that the average white layer thickness and the white layer's morphological characteristics depend on the machining conditions, and the per pulse discharge energy. For *Ton* = 100 μs, depicted in Figures 4a and 5a, the WL has an extremely thin and discontinuous profile, having only a few small globule formations. For *Ton* = 200 μs, Figures 4b and 5b, the WL is still thin

and discontinuous, but larger globules appear, indicating the simultaneous existence of re-condensed (smaller globules) and incomplete evaporated (larger globules) material formations, observations which are in line with the study of Imran et al. [6]. For *Ton* = 300 μs, Figure 4c, the WL profile is thicker and almost continuous, with larger globule formation, and disintegration areas, Figure 5c. For *Ton* = 500 μs, the WL is thick, continuous, with large globules and overlapping formations, as shown in Figure 5d. Finally, for all machining parameters, porosity is observed. For shorter pulses, the porosity is limited, with finer cavities. For higher values of *Ton*, due to the larger amount of trapped air, the porosity becomes more intense, forming larger cavities, as shown in Figure 5.

**Figure 3.** Main effects plot for (**a**) Ra. (**b**) Rt.

**Figure 4.** WL for *IP* = 18 A and *Ton* (**a**) 100 μs. (**b**) 200 μs. (**c**) 300 μs. (**d**) 500 μs.

**Figure 5.** Details from the WL for *IP* = 18 A and *Ton* (**a**) 100 μs. (**b**) 200 μs. (**c**) 300 μs. (**d**) 500 μs.

In Figure 6, the melted and re-solidified layer is depicted, along with its characteristics features, namely smaller and larger pockmarks, and islets that have been formed by molten material fluxes. It has to be pointed out the absence of intense cracks and micro-cracks on the machined surface, a typical characteristic of machined surfaces with EDM. It can be explained and attributed to the aluminum high thermal conductivity. Aluminum has a typical thermal conductivity of 138 W/mK, while steel has the half, or even lower. Thus, the thermal energy in aluminum is diffused faster, resulting in the occurrence of lower gradients of temperature, which are one of the major reasons of cracks development.

**Figure 6.** Details of the machined surface for *IP* = 15 A and *Ton* 200 μs.

The *AWLT* and the micro-hardness of the material just beneath the WL in the HAZ were measured, and the results are presented in Table 4.

**Table 4.** Average white layer thickness and micro-hardness of the heat affected zone.

As the main effect plot for the *AWLT*, shown in Figure 7a, indicates, the pulse-on time Ton has the major effect on the *AWLT* and thus, the abovementioned microscopy observations are confirmed by the measurements. Considering a nonlinear regression between the *AWLT* and the machining parameters 12 and according to the experimental results, the *AWLT* can be expressed as function of *IP* and *Ton* through Equation (5):

$$AWLT = 0.743 \cdot I\_P^{0.286} \cdot T\_{\text{on}}^{0.52} \,\text{}^{\prime} \tag{5}$$

with *AWLT* in μm, *IP* in A, and *Ton* in μs. Finally, the micro-hardness of the material, in the HAZ, is decreased, up to 43 HV, indicating a 37% decrease.

The formation of a HAZ and the subsequent reduction of the material's micro-hardness is the result of the thermal energy flow into the workpiece. Therefore, the higher the inflow heat energy rate is, the higher effect it has on the HAZ formation. On the other hand, the formation of a WL can have a shielding effect on the HAZ formation, by protecting the bulk material from the dissipated heat. As a result, there is no clear correlation between the machining power and the material's micro-hardness, but only evidence proving the existence of the HAZ underneath the WL, as shown in Figure 7b.

**Figure 7.** (**a**) Main effect plot for *AWLT*. (**b**) Scatterplot of micro-hardness HV vs. mean machining power.

#### **4. Conclusions**

An experimental investigation of machining aluminum alloy, namely Al5052, was carried out in the current study. The studied machining parameters were the pulse current *IP* and the pulse-on time *Ton*. The *MRR*, the surface roughness (Ra, Rt), the *AWLT*, and the micro-hardness of the HAZ were measured and/or calculated. The most important emerged conclusions are:

**Author Contributions:** Conceptualization, A.P.M. and P.K.-O.; Data curation, E.-L.P.; Investigation, E.-L.P. and P.K.-O.; Methodology, A.P.M., E.-L.P. and P.K.-O.; Software, E.-L.P.; Supervision, A.P.M.; Validation, E.-L.P. and P.K.-O.; Writing – original draft, E.-L.P. and P.K.-O.; Writing – review & editing, A.P.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

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

#### **References**

© 2020 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 (http://creativecommons.org/licenses/by/4.0/).

## *Letter* **Surface Texture after Turning for Various Workpiece Rigidities**

**Michal Dobrzynski and Karolina Mietka \***

Department of Manufacturing and Production Engineering, Faculty of Mechanical Engineering, Gdansk University of Technology, 80-233 Gda ´nsk, Poland<EMAIL_ADDRESS>**\*** Correspondence<EMAIL_ADDRESS>Tel.: +48-58-3471-553

**Abstract:** In the paper, we present an analysis of the surface texture of turned parts with L/D (length/diameter) ratios of 6 and 12 and various rigidity values. The studies were carried out on samples made of S355JR steel and AISI 304 stainless steel. A detailed analysis of 2D surface profiles was carried out by using a large number of parameters that allowed us to distinguish significant differences in the surface microgeometry, which confirmed that determining surface characteristics from one height parameter (Ra—arithmetical mean height) is far from sufficient. The obtained results indicate significantly better roughness and waviness values of the AISI 304 steel surfaces in terms of its size, periodicity, and regularity. Therefore, the turning process of AISI 304 shafts with low rigidity allows one to be able to achieve better quality texture and have a positive effect on the general properties of a workpiece. In all tested samples, surface irregularities decreased along with the distance from the tailstock. The shafts with an L/D ratio of 12 had worse surfaces in the first two sections due to lower rigidity. The results received close to the three-jaw chuck, regardless of the L/D ratio and material type, demonstrated similar waviness and roughness parameters and profiles.

**Keywords:** CNC turning; rigidity; surface texture; profile parameters; AISI 304; S355JR

**Citation:** Dobrzynski, M.; Mietka, K. Surface Texture after Turning for Various Workpiece Rigidities. *Machines* **2021**, *9*, 9. https://doi.org/ 10.3390/machines9010009

Received: 10 October 2020 Accepted: 7 January 2021 Published: 12 January 2021

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

**Copyright:** © 2021 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/).

### **1. Introduction**

During the turning process of a workpiece, cutting forces that cause an elastic deformation of a machining system—which is formed by a machine tool, grip, fixture, and cutting tool (MGFT system)—arise. The values of deformation for individual elements of this system are not constant because they depend on the applied cutting parameters and other processing conditions that exert a variable system of dynamic forces. As a result of variable cutting forces, vibrations of the machining system that significantly affect the tool path and deformation of the workpiece occur. Vibrations in the machining process are an undesirable phenomenon. They cause a number of part workmanship errors.

Since the MGFT system is dynamic and spatial, the mechanical vibrations generated in this system are also spatial. The components of vibrations in the direction perpendicular to the machined surface significantly affect the surface texture (roughness, waviness, etc.) and the components in line with the cutting speed direction-errors in the longitudinal section, e.g., conicity and a lack of parallelism between external and internal surfaces.

Deflections of element during processing comprise one of the most important factors influencing the final state of a product and, thus, its high quality expected by customers. Therefore, it is very important to optimize of the machining process to improve the dimensional accuracy of the finished product. According to Flisiak et al. [1], modelling the deflection of a workpiece during machining in terms of optimizing the processing technology of flexible elements is a priority task.

The quality of a machined surface, given by the set of roughness characteristics (microgeometry), affects the basic exploitation characteristics of the machine components [2]; therefore, surface roughness is a frequent subject of research [3–6]. For example Xavior et al. [5] investigated the effect of cutting fluids on the surface roughness of an AISI 304 steel workpiece after turning, Kaladhar et al. [6] explored the optimization of surface

roughness and tool flank wear in the turning of AISI 304 austenitic stainless steel with a Chemical vapour deposition (CVD) coated tool, and Wagh et al. [7] conducted AISI 304 machinability studies using a Physical vapour deposition (PVD) system with cathodic arc evaporation (CAE) deposited onto AlCrN/TiAlN coated carbides (though they mainly analyzed the arithmetical mean height (Ra) parameter). The Ra parameter can get similar values for different tool geometries and machining principles. Different tool geometries and machining principles can lead to a surface with similar Ra parameter values. Only a detailed analysis of a 2D surface profile, described by a larger number of parameters, allows one to distinguish significant differences in surface microgeometry [2]. The authors of [8] presented the state of knowledge on the influence of surface roughness on the basic functional properties of a workpiece surface: substrate adhesion, fatigue strength, and tribological and corrosion properties. Relationships exist between 2D/3D surface roughness parameters and measurable indicators of surface functional characteristics. The authors argued that describing surface characteristics based on one height parameter (Ra), or even on several such parameters (Ra, root mean square height (Rq), maximum height of the profile (Rz), and total height of the profile (Rt)), is far from sufficient. Relationships between surface texture parameters and functional surface features should be investigated so that, on the basis of the first measurements, it is possible to predict the individual functional properties of manufactured parts.

The formation process of the geometric structure of the surface as a result of machining is complex and is influenced by many factors [9]. This process consists of the effect of individual elements of a machining system. In order to obtain specific surface texture parameters in the machining process, it is necessary to consider the kinematic and dynamic features characterizing the machine tool; the stereometry, dimensions, and properties of the tool material; and the physical properties and dimensions of the processed material.

The material qualities that influence the of surface texture are machinability (which is determined by the mechanical properties like hardness and strength), chemical composition, and material structure.

Another important feature is the rigidity of the material, which is defined as the force needed to deform an object. Material rigidity is often characterized by the Young- s modulus, a value that depends on the chemical composition, crystal structure, and phase composition of the microstructure. During longitudinal turning, rigidity is also influenced by the ratio of the workpiece length (L) to its diameter (D). In order to eliminate the influence of low workpiece rigidity, appropriate clamping is used. Liu et al. [10] implemented a finite difference (FD) analysis method for calculating the deformations of multi-diameter workpieces during cutting. The authors of [11] presented a mathematical model of the system of longitudinal turning and a mathematical model of the dynamic system of the machining of shafts with a low rigidity in the elastic-deformable state while considering factors interfering with and destabilizing the process of shaft machining. Benardos et al. [12] verified that the numerical method and development of an ANN (artificial neural network) model were based on data gathered from turning experiments conducted on a CNC lathe, thus allowing for a reduction of workpiece elastic deflections under cutting forces in turning.

The authors of [13] described a machining station, working together with a lathe, designed and constructed for the stabilization of the axis of low-rigidity parts in the process of machining. The basic element was a self-centering lunette with a hydraulic drive, allowing for part centering without any preliminary alignment. Swi´ ´ c et al. [14] presented an analysis of the possibility of increasing the accuracy and stability of the machining of low-rigidity shafts. A way of improving the accuracy of machining of shafts was to increase rigidity as a result of the oriented change of the elastic-deformable state through the application of a tensile force which, combined with the machining force, forms longitudinal–lateral strains.

#### **2. Materials and Methods**

The materials under investigation were in the form of Ø30 mm drawn bars, with length of 393 and 213 mm, made of S355JR steel and AISI 304 stainless steel, respectively. Machining processes were carried out on a TUR-50 CNC turning machine. A workholding method based on clamping shaft in a three-jaw chuck with a distance of 33 mm and a hydraulic pressure of 1.8 MPa supported by a revolving center (with a pressure of 1.2 MPa) (Figure 1). Every process was made with a coolant (SITALA D201.03 made by Houghton). The machining parameters in the turning operation were established as the following: a feed (f) of 0.20 mm and a cutting speed (vc) of 75 m/min. The cutting depth (ap) values for the S355JR and AISI 304 shafts were 0.7 and 0.8 mm, respectively. In the research, TWLNR 2525 M08 turning knives with turning inserts of the same geometry—WNMG 080408-MA MC6025 for S355JR and WNMG 080408-MA MC7025 for AISI 304 (both made by Mitsubishi Materials)—were applied. During investigation, two L/D (length/diameter) ratios of 6 and 12 of the turned shaft were considered (Table 1).

**Figure 1.** The view of the shafts with length/diameter (L/D) ratios of 6 (**a**) and 12 (**b**) during machining.

The surface texture validations were carried out with a Hommelwerk Standard 1000 surface roughness tester. During the measurements, the following parameters were adopted: evaluation length (ln) = 12.5 mm; cut off value (λc) = 2.5 mm; cut off ratio (λc/λs) = 300; and sampling interval = 1.5 μm. The stylus end was conical (taper angle of cone = 60◦) with a spherical tip (tip radius (rtip) = 2 μm). These values were adopted because the main objective was to assess the surface condition on the possibly greatest length that was limited by the maximum measuring length of the used device. The shafts with an L/D of 12 were measured in four sections and the shafts with an L/D of 6 were measured in two sections at the distances presented in Figure 2. At each section, three parallel-to-the-feeding-direction measurements with a step of 120◦ were done. The surface texture for shafts with different rigidities was validated using the maximum height of the waviness profile (Wz) parameter, as well as selected roughness parameters. An analysis based on the Ra parameter was insufficient to define the surface texture, so the investigation considered other parameters such as Rq, Rdq (the root mean square for the local slope dz/dx within the sampling length), Rz, and Rt. A detailed analysis of the 2D surface profiles of the samples made of S355JR steel and AISI 304 stainless steel using a large number of parameters enabled us to distinguish significant differences in the surface micro-geometry and measurable indicators of surface functional characteristics.

**Figure 2.** This the measurement positions for the shaft with L/D = 12 (**a**) and L/D = 6 (**b**).

#### **3. Results and Discussion**

Measuring the surface texture enabled a comparison of surface structures after turning AISI 304 and S355JR shafts with different L/D ratios. In comparing the profiles of the samples under investigation, significant changes in the appearance of the roughness (R) and waviness (W) profiles (the primary (P) profile was added for better visualization) could be noticed (Figures 3–6).

In the case of turning the S355JR steel, it could be seen that the surface waviness obtained higher values than when machining the AISI 304 stainless steel, and greater waviness periodicity occurred. In addition, the profiles had greater irregularity. This was due to the adoption of various workpiece rigidities (L/D ratios) and steel/stainless steel properties such as machinability. It was also noticeable that the greater the distance from the tailstock, the lower the surface waviness that appeared in all samples. This was due to the vibrations of the maximum amplitude that occurred at the beginning of machining, related to the initial wear of the blade and the distribution of machining forces. Similar phenomena were observed in the roughness profiles. The turning operations of S355JR steel shafts resulted in an increase in surface roughness, and the R profiles were more irregular (both in the horizontal and vertical directions). The surfaces farther from the tailstock showed decreasing profile micro-irregularities.

By comparing the shafts with L/D = 6 with those with L/D = 12, it could be concluded that the longer workpieces had greater waviness and surface roughness in the first two sections. This was due to the lower rigidity of the shaft in these places during machining. Meanwhile, the waviness and roughness of the surfaces of the shafts of the same material closest to the three-jaw chuck had similar shapes and resulting R and W parameter values.

When comparing the third section of the shaft with L/D = 12 with the first section of the shaft with L/D = 6, it could be seen that despite the similar distance from the three-jaw chuck, the surface waviness and roughness were greater for the shorter shafts due to the phenomena occurring at the beginning of the machining.

Based on the values of the maximum height of the Wz parameter, a significant increase in the surface waviness of the S355JR steel samples compared to the AISI 304 stainless steel samples was noted, and the maximum difference in the concerned cases was nearly 107% (Figure 7). The values of the Wz parameter were similar in the shafts on the chuck side, while they were significantly different at the end of the samples (when supported by the center) (Table 2). Shafts with L/D = 12 in the turning operation had more than twice the maximum height of the W profile in comparison with the shafts with L/D = 6 made of AISI 304 stainless steel, and these values were three times higher for workpieces made of S355JR steel. The increased height of the waviness profile from the center support caused

by vibrations of maximum amplitude at this point. This was also confirmed by the lower rigidity of the support from the center side.

**Figure 3.** R (roughness), W (waviness), and P (primary) profiles of the first machined sample (L/D = 12; AISI 304): (**a**) section 1, (**b**) section 2, (**c**) section 3, (**d**) section 4.

**Figure 4.** R, W, and P profiles of the third machined sample (L/D = 12; S355JR): (**a**) section 1, (**b**) section 2, (**c**) section 3, (**d**) section 4.

**Figure 5.** R, W, and P profiles of the sixth machined sample (L/D = 6; AISI 304): (**a**) section 1, (**b**) section 2.

**Figure 6.** R, W, and P profiles of the eighth machined sample (L/D = 6; S355JR): (**a**) section 1, (**b**) section 2.

**Figure 7.** The center and spread of a maximum height of the waviness profile (Wz) of the samples under investigation (the box plot presents the mean, the median, the interquartile range box, and the range of the data).

Despite the higher cutting resistance, the surface of the stainless steel workpieces after turning presented better quality, and significantly lower values of R parameters were received.

The irregularities of the parts after turning were evaluated by applying mean parameters, such as Rq (Table 3) and Ra (Table 4). The values of the Ra and Rq parameters in the case of the AISI 304 stainless steel samples were characterized by a relatively small dispersion of the obtained results. On the other hand, the machining of shafts with L/D = 12 made of S355JR steel resulted in an increase in the average roughness parameters, as well

as their dispersion (Figure 8). The observed individual maximum and minimum height values had a larger effect on Rq values than on Ra. In all tested cases, the Rq parameter values were higher than Ra by an average of 20.37%.

**Table 2.** Main statistics of the maximum height of the Wz parameter.

**Table 3.** Main statistics of the mean square height (Rq) parameter.

**Table 4.** Main statistics of the arithmetical mean height (Ra) parameter.

**Figure 8.** The center and spread of Ra (**a**) and Rq (**b**) (the box plot presents the mean, the median, the interquartile range box, and the range of the data).

The steepness of the surface could be numerically represented with the Rdq parameter, and the analysis of that parameter's values made it possible to assess the susceptibility of the tested surfaces to processing. The advantage of this parameter is its high sensitivity to extreme peaks of the profile; low values characterize smooth surfaces, and higher values characterize rough surfaces (Table 5). For all shafts made of AISI 304 stainless steel, the Rdq parameter was in the range of 0.077◦–0.091◦ with a relatively small dispersion (Figure 9). On the other hand, shafts made of S355JR steel, regardless of the L/D ratio, were characterized by large dispersions of the Rdq parameter, with values in the range of 0.099◦–0.157◦. In the case of section 4 (P#4), the highest value of the Rdq parameter was obtained for the S#3 shaft, and the greatest dispersion of the parameter occurred in section 2 (P#2) for S#4 shaft (Table 5).

**Table 5.** Main statistics of the Rdq parameter.

**Figure 9.** The center and spread of the Rdq parameter (the box plot presents the mean, the median, the interquartile range box, and the range of the data).

The values of the Rt in the considered cases were approximately 15.76% greater than the corresponding Rz values. For the S355JR steel shafts, the values of the Rz and Rt parameters were higher and had a greater dispersion. The highest Rz values, Rt values, and Rt spread were observed for the S#4/1 sample section. However, the largest spread of the Rz parameter was for the S#3/1 sample section. The main statistics of the Rz and Rt parameters of the shafts with L/D ratios of 12 and 6 are presented in Table 6.

**Table 6.** Main statistics of the total height of the profile (Rt) and maximum height of the profile (Rz) parameters.

All parameters had the greatest dispersions for shafts with L/D = 12 made of S355JR steel; this phenomenon was caused by the instability of the machining process, which was also visible in the R and W profiles. The occurring waviness was caused by vibration in the MGFT system, so it is likely that the cutting edge was operating under uneven conditions, which could also have affected the surface texture. Furthermore, the samples were turned from a drawn bar, which, when processed, could obtain non-uniform properties. The AISI 304 stainless steel obtained a significantly smaller spread of parameter values, regardless of the L/D ratio. The surface texture after the turning of stainless steel is periodic, which leads to positive effects on workpiece properties after machining. In all samples under investigation, surface irregularities decreased along with the distance from the tailstock. The shafts with an L/D ratio of 12 obtained worse surfaces in the first two sections, which resulted from their lower rigidity. Regardless of the L/D ratio, similar waviness and roughness profiles close to the three-jaw chuck were obtained. The Ra and Rq roughness

parameters had no differences due to the material type, whereas the Rz, Rt, and Rdq parameters had higher values for the S355JR steel.

#### **4. Conclusions**

The low rigidity of workpieces in relation to the rigid parts of a machine tool hinders the cutting process due to the generated vibrations. The properties of a workpiece material crucially affect the accuracy of execution.

The above-discussed data were also related to better properties of the AISI 304 material, which is characterized by a better machinability. The roughness parameters were found to improve with distance from the tailstock due to the phenomena that occurred at the beginning of the turning, the vibrations of the maximum amplitude at the point of the support of the center, and the lower rigidity of this support compared to the clamping in the chuck. For the same reasons, the shafts with an L/D ratio of 6 obtained better surface textures. In summarizing the results, it can be stated that the rigidity of a workpiece has a large impact on the geometric structure of a surface. Generally, analyses based on the Ra parameter are insufficient to define surface texture, so this paper considered other parameters as well.

**Author Contributions:** Conceptualization, M.D. and K.M.; formal analysis, M.D. and K.M.; investigation, M.D. and K.M.; methodology, M.D. and K.M.; writing—original draft, M.D. and K.M.; writing—review and editing, M.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors wish to thank the firm GloBart for providing machine tool and raw material used in the study.

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

#### **References**

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Author details

Firmansyah1 , Abdul Rashid Abdul Aziz2 \*, Morgan Raymond Heikal1,3, Ezrann Zharif Zainal Abidin1 and Naveenchandran Panchatcharam4

\*Address all correspondence to<EMAIL_ADDRESS>
1 Centre for Automotive Research and Electric Mobility, Universiti Teknologi Petronas, Malaysia


Another important finding was that the thermochromic effect is not dependent on the type of fabric. Two other natural fabrics (silk and cotton) plus ten synthetic fabrics, when dyed with MBI or DBI, also showed the same type of thermochromicity [7]. Noteworthy is the fact that IND-dyed fabrics showed very little, if any, thermochromic effects. This mirrors the behavior of IND-dyed wool mentioned earlier. Again, it was seen that strong thermochromic effects on a wide variety of fabrics are only seen with the two brominated indigoids.

#### *1.6. Analysis of Dyed Carbon Nanotubes Using Transmission Electron Microscopy*

Taken together, the reflectance spectra and colorimetric analyses suggest that both MBI and DBI, after dispersion on fabrics in their *leuco* forms and aerial oxidation, exist predominantly in two physically distinct types of dye aggregates, one that gives rise to a red color, and the other, a blue color. It was envisioned that they might be viewed using transmission electron microscopy (TEM). However, the dimensions of a fabric fiber are so much greater than that of a possible submicroscopic dye aggregate, it was impossible to analyze a dyed fabric directly. Recourse was made to use carbon nanotubes as a fabric surrogate. Carbon nanotubes are tiny strand-like pieces of tubular graphite, about five nanometers in diameter by about three micrometers in length. Since it had been established above that the thermochromic effect is not substrate-specific, it was hoped that the *leuco* form of MBI could be deposited on carbon nanotubes in the same way as on fabrics, and that whatever happened on the dyed fabrics in hot water would also happen on the dyed nanotubes in hot water. The black nanotubes were vat-dyed with MBI in the usual manner, giving nanotubes which were still black, but which showed a metallic luster [7]. The dyed nanotubes showed dark spots clinging to the surface of the nanotubes, and the spots contained bromine atoms, as determined using energy dispersive X-ray analysis (Figure 3). Therefore, the spots were considered to be dye aggregates, containing dozens or hundreds of individual dye molecules. The aggregates were measured to give a size distribution plot that showed them to be mostly 12 nanometers in diameter, plus a lesser amount with a diameter of 27 nanometers. These dyed nanotubes were heated in water, causing disappearance of the larger aggregates, and appearance of a new set of aggregates only seven nanometers in diameter. Thus far, these results were consonant with the reflectance spectra and colorimetric analyses, which suggested the presence of two dye aggregates, a red type and a blue type. After the TEM analysis, it appeared that the red aggregates were the larger type, while the blue aggregates were the smaller type. If that were the case, then nanotubes dyed with DBI should give the opposite behavior, where smaller aggregates should be converted to larger ones upon heating in water. This turned out to be just the case: DBI-dyed nanotubes before heating showed aggregates of mostly seven nanometers in diameter, while after heating, they became mostly 17 nanometers and larger [12]. The reflectance, colorimetric and TEM analyses summarized above all strongly suggest that size redistribution of dye aggregates is the cause of the thermochromic effects detailed here.

**Figure 3.** MBI-dyed carbon nanotubes [7].

#### **2. Materials and Methods**

#### *2.1. Solvents*

Water was distilled. Ethanol (≥99.5%), 1-butanol (≥99.4%) and toluene (≥99.5%) were obtained from Sigma-Aldrich.

#### *2.2. Treatment of the Dyed Fabric with a Solvent*

MBI-dyed wool was cut into squares of approximately 1 in. X 1in., and immersed completely in one of the solvents above, which had been pre-heated to 80 ◦C, for 20 min. The fabric was removed from the solvent, allowed to dry completely, and analyzed using colorimetry.

#### *2.3. Colorimetric Analysis*

In the CIELAB color space [14], a color is characterized by three attributes: the lightness, *L*\*, the hue, which is given by two values, *a\** and *b\**, and the saturation, which is indicated by the absolute values of *a\** and *b\**. The *L*\* value varies from 0 (black) to 100 (white), while the *a\** value is positive for redness and negative for greenness, and the *b\** value is positive for yellowness and negative for blueness. High absolute values of *a\** and *b\** signify a highly saturated or vivid color, while low absolute values of these mean a color towards gray. The Δ*E*\*ab value [14] is a measure of the difference between two colors, and is the Euclidean distance between two points in the three-dimensional color space. The formula for Δ*E*\*ab is the square root of the sum of the squares of the changes in *L*\*, *a*\* and *b*\* values. CIELAB data were collected with a Konica Minolta Color Reader CR-10 tristimulus colorimeter, with a 10 mm/8 mm aperture/viewing area, 8◦ illumination angle with CIE standard illuminant D65 and CIE 10◦ viewing angle with diffuse viewing. All samples were backed with an opaque white background. At least six different areas of the front and back of the fabric were analyzed, to give an average reading that had a very small standard deviation.

#### **3. Results and Discussion**

We now continue our study to answer several questions about the thermochromic effect: Does the dyed fabric change color over time at room temperature in the absence of water? Does the color-changed fabric revert to its original color over time? Is water a necessary component of the thermochromic effect, or would it happen with just heat? If water is necessary, is it the only solvent that causes a color change? If other solvents are effective at causing the color change, is there a dependence on a fundamental property of the solvent, such as molecular polarity?

Enough time has passed since the production of the dyed fabrics and their heated counterparts to answer the first two questions posed above. Dyed fabrics stored for at least five years under ambient conditions did not change from their original colors, as judged by colorimetric analysis. The same is true for the dyed fabrics which had been heated in water. Thus, heat is a requirement for the color change, or perhaps the combination of water with heat is the requirement.

We proceeded by using wool dyed with MBI under standard conditions [12]. Three different samples of MBI-dyed wool were employed, each dyed under identical conditions. Figure 4 shows a typical sample. The *L*\* values ranged from 28.1 to 29.1, *a*\* values from 24.4 to 26.9 and *b*\* values from −24.2 to −25.6, with standard deviations of 0.5, 1.4 and 0.7, respectively. Small pieces of this dyed fabric were immersed in four solvents, according to the conditions shown in Table 2. The piece was removed from the solvent and allowed to dry at room temperature, and analyzed using colorimetry. The Δ*E*\*ab value refers to difference in color before and after heating. All solvents except water dissolved some of the dye from the fabric, as evidenced by a blue or violet color change to the solvent during heating.

**Figure 4.** Wool dyed with MBI according to a standardized procedure [12].

**Table 2.** Effect of four solvents and heat on the color of MBI-dyed wool.

For water (entry 1), 20 min at 80 ◦C was required for the fabric to exhibit its maximum color change; after 10 min, the Δ*E*\*ab value was only 5.5, while further heating after 20 min did not change Δ*E*\*ab. After 20 min at 80 ◦C, the lightness component *L*\* had not changed appreciably. However, the hue components *a*\* and *b*\* both lessened in magnitude; since the red-green *a*\* showed greater diminishment, the bluing of the fabric is due to loss of redness rather than gain of blueness (Figure 5). Three other solvents, ethanol, 1-butanol and toluene (entries 2, 3 and 4, respectively), were tested under the same conditions as water. Ethanol had an even greater effect on *a*\* than water: the *a*\* value decreased to 7.4 from its original value of 26.9, a drop of 72%. This decrease was the main determinant of the large Δ*E*\*ab seen. 1-Butanol was less effective in causing a color change: all three color components decreased to a small extent, giving a small Δ*E\**ab value and a barely perceptible bluing of the color. The non-polar solvent toluene was completely ineffective at causing a color change after 20 min at 80 ◦C: the changes in the color components were within the standard deviations of the three values (see above), so, taking this into account, the Δ*E\**ab value is actually zero. Further heating in boiling toluene for 100 min (entry 5) was required for a barely perceptible color change.

**Figure 5.** MBI-dyed wool from Table 2 before (left) and after (right) heating in water, ethanol and 1-butanol. (**a**) water, 80 ◦C; (**b**) ethanol, 80 ◦C; (**c**) 1-butanol, 80 ◦C; (**d**) water, r.t.

Table 2 also shows that heat alone is ineffective at causing a color change. Heating the dyed fabric at 100 ◦C for 20 min (entry 6) or 60 min (entry 7) caused no color change, as the changes in the three color components were within the standard deviation limits.

Treatment with water at room temperature for an extended period of time (entry 8) does cause an appreciable color change, but apparently the change is of a different nature than all the others: the change in the *L*\* value is the main determinant of Δ*E\**ab, and indicates significant lightening of the dyed fabric. Over such a long time period, it is possible that a chemical change to either the dye or fabric had occurred, causing the observed color change.

To summarize, both a solvent and heat are required for a color change, which appears to be irreversible. Water is the most polar of the four solvents, as measured by its dipole moment. However, ethanol was the most effective at causing the color change. Toluene is the least polar, and was ineffective at causing a color change. The more polar solvents would be expected to interact strongly with the polar surface of wool, and also with MBI through hydrogen-bond donation and acceptance. On the other hand, weakly polar solvents such as toluene would be expected to interact with MBI mainly through dispersion forces, but not strongly with the wool the MBI is adsorbed to. The solvent- and heat-induced change in the size of wool-adsorbed dye aggregates appears to involve the interaction of a polar solvent with both the wool and MBI.

#### **4. Conclusions**

The data presented here indicate that the size change of wool-adsorbed dye aggregates depends on the interaction of the solvent with both the fabric surface and the aggregates adsorbed to that surface. The sizes and shapes of molecular aggregates deposited when the reduced *leuco* form comes in contact with wool during the vatting process are expected to be different than the sizes and shapes of the oxidized dye aggregates. This is because the *leuco* form of MBI is so different in chemical structure from its oxidized colored form (Scheme 1). Thus, the colored dye aggregates resulting from exposure of the *leuco* form to oxygen in the air may be in a metastable state. The interaction of a solvent with both the dye aggregates and the fabric they are adsorbed to, at an elevated temperature, appears to be essential for the change in size of the adsorbed dye aggregates to their final, more stable sizes. The detailed mechanism of the size change, and why it occurs at all, will be the subject of further investigation.

It is possible that dyers in ancient times knew of these color changes caused by the application of both heat and water. The recipe for purple dyeing was lost and the subject of speculation for over 500 years, until the secret was discovered relatively recently [15,16]. In a study of a Coptic textile dating from the second or third century AD, which is a fragment of a purple medallion and is now in the *Österreichisches Museum für angewandte Kunst* in Vienna, it was found that the major dye, 79%, was 6-bromoindigo [17,18]. Since the color is purple rather than blue, it seems likely that the dyeing was performed without heating of the fabric in water after the final aerial oxidation step.

**Author Contributions:** Conceptualization, K.R., O.L., S.K., L.M. and C.C.; methodology, K.R., T.E. and T.I.; formal analysis, T.E. and T.I.; investigation, K.R., T.E. and T.I.; writing—original draft preparation, K.R., T.E., T.I. and C.C.; writing—review and editing, K.R., T.E., T.I., O.L., S.K., L.M. and C.C.; supervision, K.R.; project administration, K.R.; funding acquisition, K.R. and S.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by a grant from the Research Foundation of the City University of New York.

**Data Availability Statement:** The data presented in this study are available in this article.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

#### **References**

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