Arabian Journal for Science and Engineering https://doi.org/10.1007/s13369-019-03783-0 RESEARCH ARTICLE - MECHANICAL ENGINEERING Cutting Forces, Surface Roughness and Tool Wear Quality Assessment Using ANN and PSO Approach During Machining of MDN431 with TiN/AlN-Coated Cutting Tool Pradeep V. Badiger 1 · Vijay Desai 2 · M. R. Ramesh 2 · B. K. Prajwala 3 · K. Raveendra 4 Received: 21 August 2018 / Accepted: 14 February 2019 © King Fahd University of Petroleum & Minerals 2019 Abstract The aim of this study was to improve the life and performance of tungsten carbide turning tool inserts coated with TiN/AlN multilayer thin films using physical vapor deposition technique. Quality characteristics of the coating are evaluated using Calo and VDI 3198 tests. Thickness of the coating is found to be 3.651 μm with adhesion quality of HF1. The performance of coated tool inserts is evaluated using cutting speed (59–118 m/min), feed rate (0.062–0.125 mm/rev) and depth of cut (0.2–0.4 mm) as process parameters in turning MDN431 steel. Experimental investigation has been carried out based on full factorial design, and regression analysis was used to analyze and build the mathematical models for cutting force and surface roughness. Multi-objective optimization of the process parameters has been done with the combination of desirability approach and MOPSO technique. Optimum machining condition for least cutting force and optimum surface roughness is found to be V c = 59 m/min, f = 0.063 mm/rev and a p = 0.2 mm. Cutting force and surface roughness are reduced by 9% in TiN/AlN-coated tools compared with the uncoated tool. To improve the CoD and capability of predictive regression models, ANN modeling has been adopted. ANN trained model and mathematical regression models are used to predict the results and predict the responses, which follow the experimental data with minimum absolute error. The predicted results are validated using ANN and regression analysis found with minimum error, and developed models are adequate for further usage. Tool wear was reduced by 105% in TiN/AlN-coated tools compared with the uncoated tool. Keywords Ti coating · Superalloy machining · PVD · PSO optimization · ANN modeling · Tool wear List of Symbols V c Cutting speed (rpm) f Feed rate (mm/rev) a p Depth of cut (mm) F x Feed force (N) F y Thrust force (N) F z Tangential force (N) R a Surface roughness (μm) V b Tool wear (μm) B Pradeep V. Badiger pvb.badiger@gmail.com 1 Department of Mechanical Engineering, Nitte Meenakshi Institute of Technology, Bangalore 560064, India 2 Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore 575025, India 3 Tata Consultancy Services, Bangalore 560066, India 4 Quality Department, Oerlikon Balzers Coating India Pvt. Ltd., Bangalore 560099, India 1 Introduction In the industrial manufacturing sector for better productiv- ity and reducing the capital investment, any manufacturing process needs to be optimized and standardized. Among manufacturing processes, turning has been reported as a flexible machining process. All the manufacturing processes need to be optimized, and optimization techniques are used to improve the machining process by proper selection of cutting parameters. Machinability characteristics considered for the improvisation are tool wear, machined surface tex- ture, surface roughness characteristics and cutting forces [1–9]. Incoloy, Inconel and other superalloys are hard-to- machine materials which require proper selection of cutting tool combination. Thin-film coatings on cutting tools devel- oped using physical and chemical vapor deposition provide solutions to machining of the hard-to-machine materials [10– 14]. Machinability characteristics were used to evaluate the performance of coated tools [1,6,7,15–18]. Optimization of 123