**1. Introduction**

The use of duplex stainless steels (DSSs) in industries that benefit from the set of proprieties usually presented by these alloys, namely, high corrosion resistance and good mechanical properties, has deeply increased [1]. Thus, DSSs became appealing for many different applications in a wide range of industrial sectors [2–4], with these having better overall properties than most stainless steels, and even sometimes being presented as a viable alternative to some Ni-based alloys [5,6].

The machining process is heavily employed in the fabrication of high-quality and precision parts. Thus, the machining optimization of these alloys is quite appealing; however, the research about this topic is quite scarce. There are some authors that use techniques such as the Taguchi method, which is commonly employed in the optimization of machining processes [7–10]. These studies enable the identification of the machining parameters on the process, enabling optimization of production quality [11], material removal rate [12] and even generated cutting forces [13]. Another common method that can be employed to obtain information regarding the impact of a certain parameter is a multiple regression analysis [14,15], or response surface methodology (RSM), coupled with

**Citation:** Sousa, V.F.C.; Silva, F.J.G.; Alexandre, R.; Pinto, G.; Baptista, A.; Fecheira, J.S. Investigations on the Wear Performance of Coated Tools in Machining UNS S32101 Duplex Stainless Steel. *Metals* **2022**, *12*, 896. https://doi.org/10.3390/met12060896

Academic Editor: Pavel Krakhmalev

Received: 26 March 2022 Accepted: 20 May 2022 Published: 25 May 2022

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

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

an analysis of variance (ANOVA) [6]. These methods allow for the creation of predictive techniques that can be applied not only to surface roughness, as seen in [14], but to other parameters, such as the lowering of cutting forces or improving the material removal rate. This can be advantageous, as it avoids the execution of expensive and time-consuming machining tests.

Although parameter optimization is very important, from a process improvement standpoint, the tool being used is equally important, as it directly impacts process efficiency. Most machining tools for turning and milling, nowadays, are coated tools or coated carbide inserts [16,17]. These coatings have a wide range of benefits for machining applications that are well documented, such as an improved surface finish, tool longevity, reduction in cutting forces [18], lowering the coefficient of friction [19] and greatly improving the wear resistance of coated tools and surfaces [20–22]. Tool coatings are usually obtained either by physical vapor deposition (PVD) or by chemical vapor deposition (CVD). Each of the processes produces different kinds of coatings with different properties and structures, as they use different methods to achieve the deposited films [23–26]. There are many studies on the comparison of coatings for the machining of various alloys, especially hardto-machine alloys, with comparisons between PVD and CVD coatings being commonly made as researchers try to find advantages of one type of coating over the other and, also, try to optimize/improve the machining process by using coated tools [27–29]. These studies are very useful, as the coating's properties greatly affect the machining performance, with the coating's mechanical properties, structure, microstructure and residual stresses having a great influence on the machining process, stressing the importance of correct coating selection [30–33].

Cutting forces that are generated during the process provide valuable information regarding the overall process' state, thus enabling machining process optimization. This means that cutting force data provide a way to monitor the process or identify certain aspects that can be improved in the machining process. The knowledge of these cutting forces can be used to monitor tool behavior [34,35] and give information on optimal machining parameters [36,37], thus enabling the improvement of the machining process, either by parameter optimization, by improving tool life or even by helping with the development of new machining tool technology [38,39]. There are different methods for determining cutting forces, either by using a direct or indirect approach [40–43]. Further regarding machining process optimization, the analysis of the wear mechanisms that cutting tools present also brings many advantages, enabling machining process optimization by improving tool life, creating new geometries more adequate for the machining of certain materials and even optimizing the coatings of these tools by providing information on the right coating to use for a certain application. Analysis of these wear mechanisms provides valuable information that can be used for parameter adjustment as well [44]. There are studies made in this regard, focused on DSS, with common wear mechanisms being registered, such as abrasion and adhesive wear, due to the high strength of the material and high friction values reached during machining of these alloys [45–47]. Knowledge about tool wear behavior is highly important [48], as it provides information on what is occurring during machining, enabling for a decision-making process that is focused on improving the process. Either by implementing a new cooling method or adjusting machining parameters, this knowledge also enables the selection of the right tool for the job (prioritizing tool life, surface roughness and overall process efficiency), considering tool geometry and coating [45,49–51]

In this work, the wear behavior and machining performance of milling tools used in finishing operations of the UNS S32101 DSS alloy is presented. Four tools with differing geometries and coatings were used, namely, TiAlN, TiAlSiN and AlCrN coatings. Two of the tested tools were coated with AlCrN, one having two flutes and the other four flutes, as this coating is recommended for the machining of DSS alloys. The machining parameters were also varied, testing two values of cutting length and three values of feed rate. The influence of these coatings, tool geometries and machining parameter variations on the wear behavior and production quality of the tools was assessed. This assessment was performed by subjecting the tools to SEM analysis, measuring flank wear (VB) and identifying the sustained wear mechanisms. The machined surface roughness was also evaluated to characterize this influence. The obtained results are presented in this manuscript, with one section dedicated to each of the performed analyses (surface roughness assessment, flank wear measurement and wear mechanism analysis). The authors hope that with these results, the gap regarding the machining of these alloys can be somewhat filled, contributing information that could be relevant when it comes to the optimization of the machining of these alloys.
