Orthogonal array and artificial neural network approach for sustainable cutting optimization machining of 17-4 PH steel under CNC wet turning operations

Vivek John; Saurabh Aggarwal; Deepak Gupta; Harishchander Anandaram; Kapil Joshi

doi:10.52756/ijerr.2024.v38.006

Authors

Vivek John Department of Mechanical Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, India https://orcid.org/0000-0002-9042-5920
Saurabh Aggarwal Department of Mechanical Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, India https://orcid.org/0000-0002-9042-5920
Deepak Gupta Department of Mechanical Engineering, Graphic Era Hill University, Dehradun, India; Adjunct Professor, Graphic Era Deemed to be University, Dehradun, India https://orcid.org/0000-0003-4383-2256
Harishchander Anandaram Amrita School of Artificial Intelligence, Coimbatore, Amrita Vishwa Vidyapeetham, India https://orcid.org/0000-0003-2993-5304
Kapil Joshi Department of Computer Science Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, India https://orcid.org/0000-0003-1097-8347

DOI:

https://doi.org/10.52756/ijerr.2024.v38.006

Keywords:

Orthogonal array, artificial neural network approach, sustainable cutting optimization machining, 17-4 PH steel, CNC Wet Turning operations

Abstract

Sustainable manufacturing strives to increase output while minimizing resource usage, costs, and environmental impact. Tool longevity is crucial, considering material, power, and resource consumption. Challenges like chip removal, heat, and friction necessitate effective solutions. Cutting fluids, like rice bran oil, reduce temperatures, friction, and enhance tool life and surface quality. Research focuses on using rice bran oil with a TiAlN tipped tool to minimize friction and cutting forces. Taguchi's robust design and L-9 orthogonal array reduce experimental trials. Controllable factors include speed, feed, depth of cut, and rice bran oil flow rate, with surface roughness as the performance metric. Taguchi analysis improves turning performance, particularly with 17-4 PH stainless steel. Rice bran oil reduces frictional forces, enabling improved surface quality across all samples, promoting sustainability. Surface roughness (Ra) is notably influenced by feed rate and rice bran oil, contributing 58.06% and 49.5%, respectively. This research underscores the potential of optimizing cutting fluid and process parameters to enhance sustainability in manufacturing.