Optimization of Tool Wear and Surface Roughness in Machining with Cryogenic Treated Tool Using Genetic Algorithm

Document Type : Original Article

Authors

1 Assistant Professor, Faculty of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.

2 Associate Professor, Faculty of Mechanical Engineering, Yazd University, Yazd, Iran.

3 PhD Student, Faculty of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.

Abstract

Cutting fluids are used to achieve various purposes such as better surface quality, less tool wear, and reduced cutting force. In this paper, the effect of cutting tool cryogenic treatment on tool wear and workpiece surface roughness in comparison with dry and conventional turning of AISI 304 steel was investigated. The Taguchi method was used to design the experiments. The signal-to-noise ratio method was used to analyze the results of the experiments. Genetic algorithm and regression were used to optimize and model tool wear and workpiece surface roughness, respectively. Regression was used to determine the relationship between cutting speed, feed rate, depth of cut, and machining conditions as independent variables with tool wear and surface roughness as the response variables. The results demonstrated that machining AISI 304 stainless steel with cryogenic treated tool reduced the surface roughness of the workpiece compared to dry and conventional turning. Increasing the amount of ETA phase was one of the reasons for reducing wear and increasing the hardness of the cryogenic treated tool. Optimal values for tool wear and workpiece surface roughness were 0.4 mm and 2.8 microns, respectively.

Keywords

Main Subjects

References

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Karafan Quarterly Scientific Journal
Volume 19, Issue 1 - Serial Number 57
Technical & Engineering
July 2022
Pages 565-581

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History

  • Receive Date: 02 October 2021
  • Revise Date: 12 November 2021
  • Accept Date: 21 November 2021

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