Measurement, modelling and optimization of the average temperature at the tool work interface for turning of AISI 1040 steel using ANN-GA methodology
Author:
Publisher
IOP Publishing
Subject
General Engineering
Link
https://iopscience.iop.org/article/10.1088/2631-8695/ac1958/pdf
Reference34 articles.
1. The influence of minimum quantity of lubrication (MQL) on cutting temperature, chip and dimensional accuracy in turning AISI-1040 steel;Dhar;J. Mater. Process. Technol.,2006
2. The effects of cryogenic cooling on chips and cutting forces in turning AISI 1040 and AISI 4320 steels;Dhar;Proc. Inst. Mech. Eng. Part B J. Eng. Manuf.,2002
3. Multi-objective optimization of steel AISI 1040 dry turning using genetic algorithm;Vukelic,2021
4. Optimization of fractal dimension of turned AISI 1040 steel surface considering different cutting conditions: fractal dimension of turned steel surface;Mukhopadhyay;International Journal of Surface Engineering and Interdisciplinary Materials Science (IJSEIMS),2019
5. Key improvements in the machining of difficult-to-cut aerospace superalloys;Ezugwu;Int. J. Mach. Tools Manuf,2005
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1. Objective optimization of drilling of hybrid aluminium metal matrix composites using ANN NSGA- II hybrid approach;International Journal on Interactive Design and Manufacturing (IJIDeM);2024-09-06
2. A Novel Hybrid RSM-ANN Model for Surface Roughness Prediction in Turning of Al 6061 Alloy;Journal of Advanced Manufacturing Systems;2024-07-24
3. The use of machine learning and metaheuristic algorithm for wear performance optimization of AISI 1040 steel and investigation of corrosion resistance;Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology;2022-04-18
4. A Simplistic Regression-Based Genetic Algorithm Optimization of Tool-Work Interface Temperature;ENG T-POL;2022
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