Research Article Analysis and Optimization of Cutting Tool Coating Effects on Surface Roughness and Cutting Forces on Turning of AA 6061 Alloy Mahir Akg¨ un 1 and Fuat Kara 2 1 Department of Machine and Metal Technology, Aksaray University, Aksaray, Turkey 2 Department of Mechanical Engineering, Duzce University, Duzce, Turkey Correspondence should be addressed to Fuat Kara; fuatkara@duzce.edu.tr Received 18 August 2021; Revised 15 October 2021; Accepted 29 October 2021; Published 18 November 2021 Academic Editor: Tomasz Trzepieci´ nski Copyright©2021MahirAkg¨ unandFuatKara.isisanopenaccessarticledistributedundertheCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e present work has been focused on cutting force (Fc) and analysis of machined surface in turning of AA 6061 alloy with uncoatedandPVD-TiB 2 coated cutting inserts. Turning tests have been conducted on a CNC turning under dry cutting conditions based on Taguchi L 18 (2 1 × 3 3 ) array. Kistler 9257A type dynamometer and equipment have been used in measuring the main cutting force (Fc) in turning experiments. Analysis of variance (ANOVA) has been applied to define the effect levels of the turning parameters on Fc and Ra. Moreover, the mathematical models for Fc and Ra have been developed via linear and quadratic regression models. e results indicated that the best performance in terms of Fc and Ra was obtained at an uncoated insert, cutting speed of 350 m/min, feed rate of 0.1 mm/rev, and depth of cut of 1 mm. Moreover, the feed rate is the most influential parameter on Ra and Fc, with 64.28% and 54.9%, respectively. e developed mathematical models for cutting force (Fc) and surface roughness (Ra) present reliable results with coefficients of determination (R 2 ) of 96.04% and 92.15%, respectively. 1. Introduction In our world, where global warming has been felt quite a lot in recent years, the need for intelligent methods and light materials is increasing. CO 2 emission is an important criterion in terms of environmental pollution. e gaseous wastes of the environ- ment are largely dependent on the CO 2 emissions produced by the transport industry [1]. e use of lightweight materials for many automotive components is increasing in the automotive industry, where CO 2 emissions are considered. Fuel savings are achieved by reducing weight by using light materials. An ex- ample of lightweight materials is the AA 6061 alloy. e main alloying element of this material is magnesium and silicon. Moreover, the density of this alloy of 2.63g/cm3 makes it important for applications where the strength-to-weight ratio is considered in the automotive, aircraft, and aerospace industries. AA 6061 alloy is also preferred more than other aluminium series because of their properties such as strength, formability, weldability, corrosion resistance, and low cost [2]. e desire to obtain products with high efficiency and quality in production sectors causes an increase in com- petitiveness. In this context, optimization techniques are used to improve the manufacturing process [3–7]. Especially with the optimization of the input parameters in machining, the costs can be reduced by saving time, energy con- sumption, and scrap. ere are many studies in the literature on the Taguchi method, which is an optimization technique [8–10]. is method reduces production and testing costs by reducing the number of trials [11]. ere are many studies on the determination of the machinability of different aluminium series in the literature. In their study, Rajeswari and Amirthagadeswaran machined a 7075 Al material produced at different SiC reinforcement ratios on a milling machine. ey investigated machining properties to obtain the minimum surface roughness, cut- ting force, tool wear, and maximum metal removal rate using RSM-based grey relational analysis in the spindle speed, feed rate, depth of cut, and material percentage weight Hindawi Advances in Materials Science and Engineering Volume 2021, Article ID 6498261, 12 pages https://doi.org/10.1155/2021/6498261