Multi-Tempering and Cryogenic Treatment Processes for Enhanced Wear Resistance and Machining Performance of T42 High-Speed Tool Steel Using ICANN

Abstract

Cryogenic treatment is an advanced heat treatment technique used to improve the mechanical and wear characteristics of tool steels by reducing residual stresses, transforming retained austenite, and promoting carbide refinement. However, achieving consistent enhancement in wear resistance, hardness, and machining performance requires precise control of cryogenic treatment and multitempering parameters. This study proposes an ICANN-assisted optimization framework that integrates deep cryogenic treatment and multi-tempering processes to improve the performance of T42 high-speed steel (HSS) cutting tools. The proposed framework predicts and optimizes tool performance by considering critical machining parameters, including cutting speed, feed rate, and depth of cut, while evaluating key responses such as hardness, wear loss, surface roughness, material removal rate, and tool life. The ICANN model is developed using experimentally acquired machining and wear data obtained from cryogenically treated and tempered T42 HSS specimens. The performance of the proposed method is evaluated and compared with conventional artificial neural network (ANN), genetic algorithm (GA), and non-dominated sorting genetic algorithm-II (NSGA-II) approaches using MATLAB. The proposed ICANN framework achieves a hardness improvement of 4.97%, a prediction error of 0.025%, a minimum tool wear loss of 0.01 g, and a surface roughness of 1.10 μm, demonstrating improved prediction accuracy and machining performance compared with existing methods. The results indicate that the integration of ICANN with cryogenic treatment and multi-tempering provides an effective approach for optimizing T42 HSS tool performance by improving wear resistance, hardness, and machining efficiency.