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Minimizing residual stresses in AISI 1045 steel through optimization of cutting parameters: A particle swarm optimization approach

Publication Type : Journal Article

Publisher : SAGE Publications

Source : Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering

Url : https://doi.org/10.1177/09544089251318779

Campus : Coimbatore

School : School of Engineering

Department : Mechanical Engineering

Year : 2025

Abstract : This study deals with the effect of machining parameters on residual stresses caused during the end milling of AISI 1045 steel. A new tool insert was used for each trial, and residual stresses were measured on the machined surface after a single pass. From this analysis, it could be found that the increase of residual stresses becomes more tensile in nature because cutting speed and feed rate also go up along with increasing temperatures but compressive stress in cuts that require a larger depth. According to Taguchi's design of experiments, optimized cut parameters will show a decrease in the occurrence of residual stresses. Regression analysis showed that the cutting parameters explained 84% of the residual stress variation, with the feed rate being the most significant parameter ( P-value = 0.004). Optimization was done using particle swarm optimization which resulted in the optimal values which are as follows. A spindle speed of 710 r/min, feed rate, of 80 mm/min, and depth of cut, of 0.2 mm, which leads to a minimum residual stress of 203.73 MPa (compressive). This work can be considered as a framework for the prediction and optimization of machining parameters.

Cite this Research Publication : Kalimuthu Prakash Marimuthu, Gudlanarva Sri Kurumurthy, Mohanavelu Thenarasu, Mannepu Venkata Roshan, Minimizing residual stresses in AISI 1045 steel through optimization of cutting parameters: A particle swarm optimization approach, Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, SAGE Publications, 2025, https://doi.org/10.1177/09544089251318779

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