**1. Introduction**

Due to their high mechanical and anti-corrosion properties, low density, and good formability, the 5xxx series of Al alloys is widely used in many branches of industry, particularly in the automotive industry [1–6]. Their chemical composition mainly includes Al and Mg (up to 7 wt.% Mg), along with secondary additions of Mn and trace amounts of Cr [7]. Their corrosion resistance arises from a complex combination of factors, but it strongly depends on the microstructural features of the alloy [8–10]. Several studies have reported the role of intermetallic particles in the corrosion resistance of Al alloys, and these particles can be organized by their size and electrochemical behavior with respect to the Al matrix [11–16]. If the particles are relatively large (a few μm), they are classified as coarse particles, but nanoprecipitates also often form in Al-Mg alloys. Due to their various electrochemical properties (anodic/cathodic), they can either suppress matrix dissolution or have only a small influence on the corrosion of Al alloys [17,18]. Particles rich in Zn or Si (such as MgZn2 or Mg2Si) are prone to dissolution [19]. Generally, Fe-rich particles, i.e., Aln (Fe, Mn), are nobler with respect to the Al matrix and decrease the localized corrosion resistance of Al alloys depending on the content of other impurities [20–22]. The local corrosion attack around intermetallic particles has been explained in terms of various particle behaviors in corrosive media: particle fall-out, selective particle dissolution or particle dealloying [19,23]. Recent works on the microstructure of alloys have revealed different types of coarse particles in Al-Mg alloys, depending on the additional alloying elements.

The second type of local attack also occurs at small precipitates formed in 5xxx series Al alloys. When the Mg content exceeds 3.0 wt.% and alloys are exposed to temperatures of 50–225 ◦C, Mg atoms preferentially diffuse from the supersaturated solid solution (α) to grain boundaries (GBs). Then, the variety of Mg-rich phases changes from metastable

**Citation:** Dobkowska, A.; Sotniczuk, A.; Bazarnik, P.; Mizera, J.; Garbacz, H. Corrosion Behavior of Cold-Formed AA5754 Alloy Sheets. *Materials* **2021**, *14*, 394. https:// doi.org/10.3390/ma14020394

Received: 29 December 2020 Accepted: 11 January 2021 Published: 14 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/).

(β and β) to equilibrium β (Al3Mg2) precipitates at GBs (sensitization) [24–26]. Al3Mg2, which behaves as an anode with respect to the Al matrix, plays a predominant role in the corrosion resistance of Al-Mg alloys [19,27–29]; however, due to its small size (~100 nm or smaller), it is difficult to characterize this phase in situ [30,31]. The quantitative detection of the dissolution of β precipitates regarding the corrosion behavior of 5xxx alloys has been thoroughly studied by Guan et al. [25]. Vignesh et al. [32] analyzed the susceptibility to intergranular corrosion (IGC) of AA5083 and showed that the alloy's susceptibility to IGC may be reduced by grain structure refinement, dispersion, and the partial dissolution of secondary Mg2Al3 in the matrix. This has been confirmed in another study [33], which showed that the pitting, IGC and exfoliation all depended on the precipitate distribution. The study suggested that grain refinement can effectively enhance the IGC resistance of the commercial AA5083-H111. Moreover, the selective dissolution of Al3Mg2 phase in AA5083 also determines its resistance to stress corrosion cracking [34]. In 5xxx Al alloys, pitting is explained in terms of the formation of a defective oxide film [35] and is strongly dependent on the NaCl concentration and pH of the solution [36–38].

Since the 5xxx alloys do not respond to age hardening, previous reports regarding the electrochemical behavior of 5xxx alloys have also focused on post-processing heat-treatment and/or plastic deformation [39–41]. As stated by Neetu et al. [42], the subsequent annealing of the AA5080 produced by multi-axial forging at room temperature drastically improved its corrosion resistance. The homogenization heat treatment of AA5083-O plates after casting had the optimal effect on the overall corrosion resistance of the material [40]. Various plastically-deformed (i.e., friction stir processing, accumulative roll-bonding) Al-Mg alloys have shown better corrosion resistance compared with their parent material [43,44]. In contrast, extruded AA5083 showed similar overall corrosion resistance compared with conventional Al-Mg alloys [45].

Clearly, the corrosion behavior of Al-Mg alloys is complex and requires more studies to distinguish the dependence between the formation of various kinds of localized corrosion and their mutual interactions. Although Al-Mg alloys display fairly good corrosion resistance [46,47], subjecting them to additional cold-forming to produce semi-products or final elements may change the material's properties and compromise their electrochemical behavior. Therefore, the optimization of the corrosion performance of commercially applicable Al-Mg alloys is of critical importance in terms of using these alloys for structurally lightweight devices and when plastic deformation is necessary to obtain the desired shape of a design element (i.e., bending). For the needs of this project, we produced part of a construct using AA5457, which has an optimal combination of mechanical properties and is lightweight, commercially available, and prone to plastic deformation. In this work, Al-Mg type AA5754 alloy was used to produce the outer components of an experimental prototype of a capsule that allows for teleperformance assessment tests and telerehabilitation of sensory diseases (i.e., hearing, sight, smell, balance). The main goal of this study was to analyze how cold-working can change corrosion resistance of AA5754 (O/H111 according to [48]).

#### **2. Materials**

A schematic of the construction design is given in Figure 1a. To prepare a final element in the form of an adjustable angle plate, a 4 mm-thick sheet made of AA5754 (chemical composition given in Table 1) was cut into strips and subsequently bent to 90◦ (Figure 1). The main focus was to determine the influence of microstructural changes induced by bending on the corrosion behavior of the original AA5754 sheet. To accomplish this, the corrosion behavior in two specified areas of the final element was measured: in the undeformed area located at the arm of the bent Sheet (**A**) and at the bent area (**B**), as shown in Figure 1b.

**Figure 1.** The outer design of the transferable capsule used for teleperformance assessment tests and telerehabilitation: (**a**), schematic of the outer elements, and (**b**), locations of corrosion tests.

**Table 1.** The chemical composition of 5754 alloy in mass% [48].


**1. Introduction**

Robots have been used in applications such as industry, medicine, agriculture, space, education, underwater exploration and many others. Manufacturing processes in industries have

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

increased considerably the use of robotic arms to automate repetitive and tedious tasks performed under difficult conditions for workers. Moreover, the use of mobile robots in industries also improves the efficiency and accelerates the production process. Mobile robots are equipped with sensors to analyze and interpret information about the environment during navigation [1]. Some applications of mobile robots in industry are as follows:

This chapter describes, in Section 2, the global strategy used to design, implement and program the palletizer robot. The robot implementation and the A-start algorithm are explained in Section 3. Experimental results are presented in Section 4. Finally, Section 5 includes the

Design and Implementation of a Demonstrative Palletizer Robot with Navigation…

http://dx.doi.org/10.5772/intechopen.72872

169

The palletizer robot proposed here moves a box from place A to place B while navigating and avoiding collisions. To attain this goal, the main tasks involved and tackled here are as

In general, the strategy programmed and performed by the palletizer robot is described in the block diagram of **Figure 1**. Both programming strategies DaNI and NXT are combined, but the control of the overall task is programmed on the mobile robot DaNI. The action "*compute trajectory to the box*" in the diagram uses the A-star algorithm and receives a predefined map of the environment with all static obstacles on it. This action is programmed on the DaNI robot, and it is performed in two stages; first, the robot moves to the box position and, second, the robot moves to the final pallet. Once the robot performs the first stage and arrives to the box position, the action "*set gripper ready*" involves the configuration and positioning of the gripper to take the box. This action was programmed on the NXT Lego. Once sensors indicate that the gripper has taken the box, the second stage of the trajectory is performed and the robot moves to the "*go to final pallet*" action. The robot locates the gripper and leaves the box carefully on the pallet. Then, the robot goes back to the initial position and the same process starts again if more boxes must be moved. Finally, dynamic obstacles are detected using the sonar sensor during robot mobile navigation and the robot stops if an obstacle is found in its path, and it continues its trajectory when the obstacle is not detected

The mobile robot used in this project is the robotic platform called NI LabVIEW robotics Starter Kit®, described in [8], also known as DaNI 2.0, developed by NI. This mobile robot was designed to develop and run algorithms in real time for autonomous system applications

Each wheel of the robot is connected to a DC motor which provides the traction force and stabilization wheel for balancing the robot; the kinematic model and the representation of robot position used in this work is the same as the one presented in [5]. The sbRIO-9632 card

and can be programmed on two different languages: LabView or C.

**3.** Performing the robot trajectory and robot interacting with the dynamic obstacles

conclusion and outlines future work.

follows:

anymore.

**2.1. Robot model description**

**2. Global strategy for palletizer robot navigation**

**2.** Path planning strategy based on an A-star algorithm

**1.** Perception of the environment by using sonar and contact sensors

The palletizing of objects (essentially boxes) in the industry is the process to accommodate boxes on a pallet that is usually performed by fixed robotic arms [3, 4]. In cases when the destination is not fixed, mobile robots are also used to place boxes to a destination. For instance, magnetic strip-guided robots transport the merchandise successfully, albeit only following a linear path. Therefore, one of the best solutions for palletizing objects from an origin to a destination involves the use of robotic arms mounted on mobile robots.

The palletizing task requires a path planning strategy which consists in finding an obstacle-free path for mobile robot navigation from one place to another. Many path planning strategies can be found in the literature for various applications, ranging from video game programming to outdoor autonomous navigation of robots. Path planning methods are based on simplifying the searching area to a 2D matrix in which each element represents a reduced square area of the navigation area (that will be interpreted as a cell) [5]. Thus, each cell can be navigable or not depending on the obstacles on it, and a resulting path is obtained if a set of adjacent navigable cells from the origin to destination is found.

The aim of implementing a Box Palletizing Robot is to encourage young people to explore robotic issues as modular tasks that require design, mathematical modeling, programming and some interest and creativity. In this context, many robotic kits and prototypes have been introduced by different companies such as Vex [6], Arduino, Lego, Zowi from BQ [7], to name but a few. However, in this chapter, we present a palletizer robot that combines two robotic kits: the mobile robot platform Dani from National Instruments (NI) and the Lego Mindstorms NXT 2.0 8547 model used to build the robotic arm. Both robotic kits require basic, medium and high levels of knowledge in line with the final purposes. In our case, we will describe the design, programming and synchronization of both kits.

Additionally, the path planning strategy used in this project is based on the A-star algorithm and basic strategies to control the robotic arm. The characterization of the robotic arm is based on a parallel structure, and it has been built using the LEGO NXT kit. To improve the compatibility between the robotic arm and the robot mobile, the LEGO NXT is programmed on LabVIEW [8], a trademark software of NI, to use the starter kit, which is a robot also distributed by NI. An Ethernet connection is used for communication between the PC and the mobile robot, while a Bluetooth connection is used for communication with the robotic arm [9].

This chapter describes, in Section 2, the global strategy used to design, implement and program the palletizer robot. The robot implementation and the A-start algorithm are explained in Section 3. Experimental results are presented in Section 4. Finally, Section 5 includes the conclusion and outlines future work.


**5. Conclusions**

In this study, a whole spectroscopic approach involving FORS and SERS spectroscopies was employed in order to identify different synthetic dyes used for the manufacturing of *Tholu Bommalu* leather puppets from the 1970s. The two techniques were directly applied on sampling stubbons, used for the extraction of the dyes from the objects of interest. This approach resulted successful for the identification of coloring molecules in some parts of the puppets: indeed, the data separately obtained from the two techniques reciprocally confirmed the attributions (Malachite Green and Crystal/Methyl Violet for the D816-7 sample, Rhodamine B for D817-2). In other cases (D816-4, D817-4) the use of SERS after FORS analyses provided further data and allowed to draw a richer picture on the composition of the materials: for the D816-4 sample, Rhodamine B was found in probable mixture with Eosin Y, while for the D817-4 sample, a combination of an azo dye for the yellow part and Crystal/Methyl Violet for the dark decorations was hypothesized. Finally, in the case of some areas (the D816-5, D816-9, and D816-15 samples), the analyses provided preliminary hypotheses about the colorants, which are likely present in mixture. The achieved results are of paramount importance in the perspective of selecting the proper methodology for a consequent separative analysis, which could confirm the results obtained from the spectroscopic approach and solve the doubts regarding the whole composition of complex mixtures of dyes. Moreover, the reported experiments confirmed the transition from natural dyes to synthetic ones in the manufacturing of *Tholu Bommalu* puppets.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/heritage4030101/s1, Figure S1: (a) Visible light reflectance spectrum obtained for the sample D816-4 with the corresponding first derivative (red lines highlight the inflection points) and (b) related apparent absorption spectrum (background subtracted); Figure S2: (a) Visible light reflectance spectrum obtained for the sample D816-4 with the corresponding first derivative (red lines highlight the inflection points) and (b) related apparent absorption spectrum (background subtracted); Figure S3: (a) Visible light reflectance spectrum obtained for the sample D816-5 with the corresponding first derivative (red lines highlight the inflection points) and (b) related apparent absorption spectrum (background subtracted); Figure S4: (a) Visible light reflectance spectrum obtained for the sample D816-15 with the corresponding first derivative (red lines highlight the inflection points) and (b) related apparent absorption spectrum (background subtracted); Figure S5: (a) Visible light reflectance spectrum obtained for the sample D817-4 with the corresponding first derivative (red lines highlight the inflection points) and (b) related apparent absorption spectrum (background subtracted); Figure S6: Conventional Raman spectra obtained for the samples in the range 350–1050 cm−1 (top) and in the range 1050-1750 cm−1 (bottom); Figure S7: SERS spectrum obtained for D816-9 sample.

**Author Contributions:** Conceptualization: A.C., I.S., A.N., B.G. and M.B.; investigation: A.C. (Raman and SERS spectra, interpretation of FORS spectra), A.B. (FORS spectra), A.N. (interpretation of FORS spectra); resources, P.P. and M.B.; main writing—preparation of original draft, A.C., I.S. and G.D.; writing—review and editing, A.C., I.S., G.D. and F.R.; supervision: R.C., P.P. and M.B. All authors have read and agreed to the published version of the manuscript.

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

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We are grateful to the International Puppets Museum "Antonio Pasqualino" in Palermo, Sicily, Italy, for the granted access to the historical objects and the collaboration on this research.

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


*Article* **Does the Obesity Problem Increase Environmental Degradation? Macroeconomic and Social Evidence from the European Countries**

**Matheus Koengkan 1 and José Alberto Fuinhas 2,\***

**Abstract:** The macroeconomic effect of the obesity epidemic on environmental degradation was examined for panel data from thirty-one European countries from 1991 to 2016. The quantile via moments model (QVM) was used to realize our empirical investigation. The empirical results indicate that the obesity epidemic, electricity consumption, and urbanisation encourage environmental degradation by increasing CO2 emissions, while economic growth decreases them. Moreover, we identify that the obesity epidemic raises the environmental degradation problem in three ways. First, the obesity epidemic is caused by the increased consumption of processed foods from multinational food corporations. The increase in food production will positively impact energy consumption from non-renewable energy sources. Second, obesity reduces physical and outdoor activities, increasing the intensive use of home appliances and motorized transportation and screen-viewing leisure activities, consequently increasing energy consumption from non-renewable energy sources. A third possible way can be related indirectly to economic growth, globalization, and urbanisation. This empirical investigation will contribute to the literature and for policymakers and governments. Therefore, this investigation will encourage the development of initiatives to mitigate the obesity problem in European countries and accelerate the energy transition process. Finally, this investigation will open a new topic in the literature regarding the correlation between the obesity epidemic and environmental degradation.

**Keywords:** CO2 emissions; energy consumption; environmental degradation; European region; food production; health problem; macroeconomics; obesity


**2. Modernizing maize technologies**

There are 12 key technologies to modernize production and post-harvest management of maize in Africa. These technologies include: (1) drought-tolerant maize varieties to strengthen the resilience of food production, (2) imazapyr-resistant maize varieties that withstand parasitic striga weeds, (3) golden maize varieties with provitamin A biofortification for improved human nutrition, (4) a streamlined licensing mechanism for commercial multiplication of hybrid maize varieties, (5) information and communication technology (ICT) platforms offering ready access to digital information, (6) contracted farm mechanization services, (7) better fertilizer blends together with top-dressed nitrogen for improved nutrient supply, (8) rotating and intercropping with nitrogen-fixing grain legumes to improve soil health, (9) applying herbicides for pre- and post-emergent weed control, (10) controlling the biological invasion of fall armyworm (FAW) through integrated practices, and (11) countering aflatoxin contamination through atoxic competitors, (12) improved post-harvest handling. Further details on each of these 12 technologies follow.