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A Novel Machine Learning-Based Optimizing Multipass Milling Parameters for Enhanced Manufacturing Efficiency
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Author(s): Aditi Sharma (Institute of Engineering and Technology, Lucknow, India), Hari Banda (Villa College, Maldives), N. Dhamodharan (Dr. Mahalingam College of Engineering and Technology, Pollachi, India), J. Ramya (St. Joseph's College of Engineering, Chennai, India), Priya Shirley Muller (Sathyabama Institute of Science and Technology, Chennai, India)and M. D. Rajkamal (Velammal Institute of Technology, Chennai, India)
Copyright: 2024
Pages: 22
Source title:
Metaheuristics Algorithm and Optimization of Engineering and Complex Systems
Source Author(s)/Editor(s): Thanigaivelan R. (AKT Memorial College of Engineering and Technology, India), Suchithra M. (SRM Institute of Science and Technology, India), Kaliappan S. (KCG College of Technology, India)and Mothilal T. (KCG College of Technology, India)
DOI: 10.4018/979-8-3693-3314-3.ch009
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Abstract
The present research studies the optimization of multipass milling parameters for AISI 304 stainless steel, adopting a systematic experimental technique based on the Taguchi L9 array design. The research methodically adjusts cutting speed, feed rate, and depth of cut, documenting their impacts on surface roughness. Experimental data, obtained with a Mitutoyo portable surface tester, are the foundation for training machine learning models. The linear regression (LR) model, trained using 1200 measurements, produces a prediction equation with a remarkable accuracy of 92.335%, offering insights into the linear correlations between machining parameters and surface roughness. Concurrently, an artificial neural network (ANN) model, exhibiting 100% accuracy, captures non-linear patterns inherent in the milling process. The actual vs. anticipated values table for the LR model further demonstrate its predictive powers.
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