Title: Influence of cutting parameters in hard turning 40× steel with self-driven rotary tool on surface roughness using genetic programming method and artificial ecosystem-based optimisation

Authors: Nguyen Van Trung; Duong Xuan Bien; Dao Van Duong; Hoang Thi Dieu

Addresses: Faculty of Mechatronics and Electronics, Lac Hong University, Dong Nai, Vietnam ' Advanced Technology Center, Le Quy Don Technical University, Hanoi, Vietnam ' Faculty of Mechanical Technology, HCMC University of Industry and Trade, Dong Nai, Vietnam ' Faculty of Mechanical Engineering, Nam Dinh University of Technology Education, Vietnam

Abstract: This paper focuses on developing a roughness prediction model based on genetic programming (GP) method and evaluates the influence of cutting parameters (CP) on surface roughness (SR) of 40X steel after heat treatment in rotary tool hard turning process. Different GP models are considered, and the best model is selected for comparison with the multi-variables regression analysis (MRA) model. Next, the optimal value of CP and their influence on SR are determined through artificial ecosystem-based optimisation algorithm. Two best models GP and MRA were used to investigate the effect of CP on SR value with R² index higher than 98%. The error value from GP (MSE = 0.014; MAPE = 4.75%) is much smaller than MRA (MSE = 0.045; MAPE = 8.3%). Furthermore, research results show the superiority of GP over MRA in considering the mutual relationship between the input variables for the objective function. [Submitted 9 July 2023; Accepted 20 March 2024]

Keywords: hard turning; surface roughness; multi-variables regression; genetic programming; GP; artificial ecosystem.

DOI: 10.1504/IJMR.2024.140288

International Journal of Manufacturing Research, 2024 Vol.19 No.2, pp.211 - 238

Received: 09 Jul 2023
Accepted: 20 Mar 2024
Published online: 01 Aug 2024 *

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