镍基高温合金铣削刀具磨损预测

doi:10.3969/j.issn.1000-1158.2023.12.08

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Abstract A new deep learning method based on stacked sparse autoencoders and multi-sensor feature fusion is proposed for milling tool wear prediction by building a nickel-based high temperature alloy milling experimental test platform and analysing tool wear variation patterns. Signal features are extracted in the time domain,frequency domain and time-frequency domain,and the optimal multi-sensor features are determined through correlation analysis,which is input to SSAE for deep feature learning. A tool wear prediction model is established using a bidirectional long-short term memory,and different experimental data sets of milling wear are applied to verify the prediction performance of the trained model. The prediction results show that the root-mean-square error is reduced by at least 9.6% compared to each of the known models, proving that the combination of multi-sensor feature fusion and deep learning methods can improve the prediction performance.

Key words： tool wear;nickel-based superalloys;stacked sparse autoencoders;multi-sensor feature fusion deep learning method

Received: 16 September 2022 Published: 27 December 2023

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TB931

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	YANG Li

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YANG Li. Tool Wear Prediction for Nickel-Based Superalloy Milling[J]. Acta Metrologica Sinica, 2023, 44(12): 1834-1841.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.12.08 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I12/1834

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