计量学报2023，Vol.44Issue(12)：1834-1841,8.DOI:10.3969/j.issn.1000-1158.2023.12.08
镍基高温合金铣削刀具磨损预测
Tool Wear Prediction for Nickel-Based Superalloy Milling
杨莉1
作者信息
- 1. 四川工程职业技术学院 机电工程系, 四川 德阳 618000
- 折叠
摘要
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.关键词
刀具磨损/镍基高温合金/堆叠稀疏自动编码器/多传感器融合/深度学习方法Key words
tool wear/nickel-based superalloys/stacked sparse autoencoders/multi-sensor feature fusion/deep learning method分类
通用工业技术引用本文复制引用
杨莉..镍基高温合金铣削刀具磨损预测[J].计量学报,2023,44(12):1834-1841,8.
