Title: ANN-GA integrated acrylic milling optimisation for energy consumption, machining time and MRR

Authors: Shanta Saha; Mohammad Muhshin Aziz Khan; Ahmed Sayem; Md. Alamgir Hossen; Pankoj Nandi

Addresses: Department of Industrial and Production Engineering (IPE), Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh ' Department of Industrial and Production Engineering (IPE), Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh ' Department of Industrial and Production Engineering (IPE), Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh ' Department of Industrial and Production Engineering (IPE), Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh ' Department of Industrial and Production Engineering (IPE), Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh

Abstract: This study explores the application of the artificial neural network-genetic algorithm (ANN-GA) approach to optimise CNC milling parameters for acrylic machining, focusing on energy efficiency, material removal rate (MRR), and machining time. A set of experiments were conducted via full factorial design to investigate the impact of these parameters on the responses, with spindle speed demonstrating the most significant influence. The mathematical model was developed to predict responses using ANN, with the selected network, achieving the lowest mean square error of 0.0019. By integrating GA with ANN, multi-objective optimisation was achieved, minimising energy consumption and machining time while maximising MRR. The optimised solutions offered the best combinations of operating parameters to enhance overall performance in the given cutting condition. Validation through confirmation tests demonstrated the efficacy of the ANN-GA approach, with error margins below 5%. The optimised results showcase the effectiveness of the ANN-GA method, providing adaptable solutions for manufacturers to balance conflicting objectives in CNC milling. [Submitted 2 September 2024; Accepted 26 June 2025]

Keywords: acrylic plastic; CNC milling; multi-objective optimisation; ANN-GA.

DOI: 10.1504/IJMR.2024.149092

International Journal of Manufacturing Research, 2024 Vol.19 No.4, pp.455 - 480

Received: 02 Sep 2024
Accepted: 26 Jun 2025
Published online: 13 Oct 2025 *

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