Chalak Qazani, Mohammad Reza, Amini, Saeid, Pedrammehr, Siamak, Baraheni, Mohammad, and Suhail, Adeel H. (2023) A machine learning method for cutting parameter selection in rotary ultrasonic-assisted end grinding. International Journal of Advanced Manufacturing Technology, 126. pp. 1577-1591.

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Abstract

Recently, rotary ultrasonic-assisted end grinding (RUAEG) has been utilised since it can increment the pace of material elimination and reduce the thrust force. Assigning ultrasonic vibration to the tools was more significant than the other grinding parameters. However, there is no systematic way to choose the cutting parameters to reach efficient outcomes (lower surface roughness and thrust force). The present research employs an adaptive network-based fuzzy inference method to model the RUAEG outcomes, including surface roughness and thrust force, based on the cutting parameters. Moreover, a single objective genetic algorithm is employed to calculate the optimum hyperparameters of the developed adaptive network-based fuzzy inference to reach the highest efficiency. At the end of the process, the multi-objective genetic algorithm is applied to find the optimal machining parameters of RUAEG to achieve the lowest surface roughness and thrust force. The approach is developed using MATLAB with the practically extracted dataset by RUAEG and conventional end-grinding processes of silicon nitride. The optimal cutting parameters are recommended to reach the lowest surface roughness and thrust force. The recommended optimal cutting parameters were able to reach 0.5177 (μm) and 11.8103 (N) for surface roughness and thrust force using the RUAEG method.

Item ID:	86730
Item Type:	Article (Research - C1)
ISSN:	1433-3015
Copyright Information:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023
Date Deposited:	15 Oct 2025 02:12
FoR Codes:	40 ENGINEERING > 4014 Manufacturing engineering > 401406 Machining @ 30% 40 ENGINEERING > 4014 Manufacturing engineering > 401412 Precision engineering @ 30% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 40%
SEO Codes:	24 MANUFACTURING > 2412 Machinery and equipment > 241204 Industrial machinery and equipment @ 30% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 30% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 40%
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