Experimental Insights and ANN-Based Surface Roughness Prediction through analysis of Machined Surface Quality of Al2024/SiCp Composites

¹ College of Engineering, Department of Chemical Engineering, University of Bahrain, Bahrain
² Department of Mechanical Engineering, Hindustan Institute of Technology & Science, Padur, Chennai 603103, Tamil Nadu, India.
³ Department of Aeronautical Engineering, Hindustan Institute of Technology & Science, Padur, Chennai 603103.
⁴ Department of Mechanical Engineering, CVR College of Engineering, Vastunagar, Mangalpalli (V), Ibrahimpatnam (M), Rangareddy (D), Telangana, 501510, India.
⁵ Department of Mechatronics Engineering, KCG College of Technology, Chennai 600097, Tamil Nadu, India.
⁶ Department of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu 602105, India.

^* Corresponding author: ramyamaranan@yahoo.com

Abstract

This present research deals with optimizing machining parameters and surface quality improvement of Al2024/SiCp composites which are important materials used in the aerospace industry. The optimal quartet of factors was investigated to achieve the best outcomes using Taguchi design approach and includes cutting speed of 105 m/min, feed rate of 0.15 mm/rev, and depth of cut of 0.35 mm with a minimal level of roughness of 0.9 μm. An ANN model has been trained and validated, and a high level of predictive accuracy with an overall accuracy of 100% after 195 epochs has been achieved. The results indicated that systematic experimentation and the application of advanced modeling approaches, including the beneficial configuration of parameters and validated ANN model, can help to achieve a superior surface quality meeting the requirements of the aerospace industry. As a result, manufacturers can benefit from the proposed solutions to optimize their production practices, enhance the performance of components, and contribute to the field of aerospace engineering.