Prediction of flank wear and surface roughness by recurrent neural network in turning process

W. K., Lee and Abdullah, M. D. and P., Ong and Abdullah, H.Z. and Teo, Teo (2021) Prediction of flank wear and surface roughness by recurrent neural network in turning process. Journal of Advanced Manufacturing Technology (JAMT), 15 (1).

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Abstract

Tool wear and surface roughness plays a significant role for proper planning and control of machining parameters to maintain product quality in order to achieve sustainable manufacturing. The machining process is complex, thus it is very difficult to develop a comprehensive model. This study proposes an innovative model of flank wear and surface roughness prediction for turning of AISI 1040 steel based on a recurrent neural network (RNN). In this study, the flank wear and surface roughness was measured during turning at different cutting parameters. Full factorial experimental design applied aims to increase the confidence limit and reliability of the experimental data. The input variables for the proposed RNN network were cutting speed, feed rate, depth of cut and the homogeneity extracted from the surface texture images obtained by using grey level co-occurrence matrix. The result shows that the accuracy of the flank wear and surface roughness prediction using RNN can reach as high as 97.05% and 96.58%, respectively.

Item Type:	Article
Uncontrolled Keywords:	Prediction; tool wear; surface roughness; turning process
Subjects:	T Technology > TS Manufactures > TS155-194 Production management. Operations management
Depositing User:	Mr. Abdul Rahim Mat Radzuan
Date Deposited:	16 Nov 2021 04:29
Last Modified:	16 Nov 2021 04:29
URI:	http://eprints.uthm.edu.my/id/eprint/2947

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