基于模糊神经网络与主成分分析的磨削表面粗糙度在线预测

doi:10.3969/j.issn.1000-1158.2022.11.01

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Abstract Based on acoustic emission and vibration signals, a surface roughness prediction method is proposed to improve the accuracy of workpiece surface roughness identification in grinding process by using the fuzzy neural network and the principal component analysis. Firstly, the acoustic emission and vibration signals are measured in the grinding process, and the relevant time-domain features, the frequency-domain features and the wavelet packet feature parameters are extracted from these signals. The extracted feature parameters are reduced and optimized by using principal component analysis. Then, the fuzzy neural network prediction model of surface roughness is constructed, and the signal feature parameters and the surface roughness are taken as the input and output of the prediction model. Finally, the model is used and trained to verify the prediction accuracy of surface roughness model. The experimental results show that five principal components of the acoustic emission and vibration signal feature parameters are reduced and obtained by using the principal component analysis (PCA) method. Based on the five principal components, the effect accuracy of the fuzzy neural network surface roughness prediction model can reach more than 91%. Compared with locally linear embedding and multidimensional scaling methods, the PCA method is better, and the prediction accuracy is higher.

Key words： metrology surface roughness on-line prediction PCA fuzzy neural network grinding

Received: 03 August 2021 Published: 14 November 2022

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TB92

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	CHI Yu-lun
	WU Yao-yu
	JIANG Huan
	YANG Lei

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CHI Yu-lun,WU Yao-yu,JIANG Huan, et al. On-line Prediction of Grinding Surface Roughness Based on Fuzzy Neural Network and Principal Component Analysis[J]. Acta Metrologica Sinica, 2022, 43(11): 1389-1397.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.11.01 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I11/1389

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