International Journal of Industrial Engineering & Production Research September 2021 Vol. 32, No. 3: 1-13 DOI: 10.22068/ijiepr.32.3.1 Prediction of Surface Roughness Using a Novel Approach M Kaladhar 1* , VVSSS Chakravarthy 2 & PSR Chowdary 3 Received 11 January 2021; Revised 24 March 2021; Accepted 22 August 2021; © Iran University of Science and Technology 2021 ABSTRACT Surface quality is a technical prerequisite in the field of manufacturing industries and can be treated as a quality index for machined parts. Attainment of appropriate surface finish plays a key role during functional performance of machined part. The machining parameters typically influence it. Consequently, a highly focused task is to enumerate the good relation between surface roughness (Ra) and machining parameters. In the current work, response surface methodology (RSM) based regression models and flower pollination algorithm (FPA) based sparse data model were developed to predict the minimum value of surface roughness. The model is developed for hard turning of AISI 4340 steel (35 HRC) using a single nanolayer of TiSiN-TiAlN PVD-coated cutting insert. The results obtained from this approach had good harmony with experimental results, as the standard deviation of the estimated values was simply 0.0804 (for whole) and 0.0289 (for below 1 µm Ra). Compared with RSM models, the proposed FPA based model showed a minuscule percentage of mean absolute error. The model obtained asubstantial correlation coefficient value of 99.75% among the other model’s values. The behavior of machining parameters and its interaction against surface roughness in the developed models were discussed with Pareto chart. It was observed that the feed rate was highly significant parameter in swaying machining surface roughness. In inference, the FPA sparse data model is better than the RSM- based regression models for prognosis of surface roughness in hard turning of AISI 4340 steel (35 HRC). The model developed using FPA based sparse data for surface roughness during hard turning operation in the current work is not reported to the best of author’s knowledge. This model disclosed a more dependable estimation over the multiple regression models. KEYWORDS: Hard turning; Surface roughness; Regression; Flower pollination algorithm. 1. Introduction1 Hardening of steels frequently associates with medium and high carbon steels that undergo heat treatment and quenching processes to increase the hardness. Hardened steels have good wear, corrosion resistance and can also resist high pressure and shock. Components made form Corresponding author: M Kaladhar * kaladhar.m@raghuenggcollege.in 1. Department of Mechanical Engineering, Raghu Engineering College, Visakhapatnam, Andhra Pradesh, India. 2. Department of Electronics and Communication Engineering, Raghu Institute of Technology, Visakhapatnam, Andhra Pradesh, India. 3. Department of Electronics and Communication Engineering, Raghu Institute of Technology, Visakhapatnam, Andhra Pradesh, India. hardened steel are applied in various areas includes automobile, aerospace, transportation and energy. AISI 4340 is medium carbon steel with high strength used in various applications such as aircraft landing gear, power transmission gears and shafts, heavy duty shafts, spindles, pins, chucks, axles, etc. Machining of hardened steels has become cost-effective and prevalently used in manufacturing various components as stated above due to its advantages over the grinding process. For instance, provision of shorter lead times, eco-friendly nature and around 60% of reduction in machining time etc. made this popular[1-3]. Though it offers high accurate machine components, there certain obstacles such as tooling cost, unwanted residual stresses and formation of dark and white layer which further affects the surface quality of hardened RESEARCH PAPER [ DOI: 10.22068/ijiepr.32.3.4 ] [ Downloaded from ijiepr.iust.ac.ir on 2022-03-15 ] 1 / 13