Tool Wear Monitoring for Complex Part Milling Based on Deep Learning
Tool wear monitoring is necessary for cost reduction and productivity improvement in the machining industry. Machine learning has been proven to be an effective means of tool wear monitoring. Feature engineering is the core of the machining learning model. In complex parts milling, cutting condition...
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doaj-40672ff468ac483d83bfa51596de93df2020-11-25T03:41:08ZengMDPI AGApplied Sciences2076-34172020-10-01106916691610.3390/app10196916Tool Wear Monitoring for Complex Part Milling Based on Deep LearningXiaodong Zhang0Ce Han1Ming Luo2Dinghua Zhang3Key Laboratory of High Performance Manufacturing for Aero Engine (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi’an 710072, ChinaKey Laboratory of High Performance Manufacturing for Aero Engine (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi’an 710072, ChinaKey Laboratory of High Performance Manufacturing for Aero Engine (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi’an 710072, ChinaKey Laboratory of High Performance Manufacturing for Aero Engine (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi’an 710072, ChinaTool wear monitoring is necessary for cost reduction and productivity improvement in the machining industry. Machine learning has been proven to be an effective means of tool wear monitoring. Feature engineering is the core of the machining learning model. In complex parts milling, cutting conditions are time-varying due to the variable engagement between cutting tool and the complex geometric features of the workpiece. In such cases, the features for accurate tool wear monitoring are tricky to select. Besides, usually few sensors are available in an actual machining situation. This causes a high correlation between the hand-designed features, leading to the low accuracy and weak generalization ability of the machine learning model. This paper presents a tool wear monitoring method for complex part milling based on deep learning. The features are pre-selected based on cutting force model and wavelet packet decomposition. The pre-selected cutting forces, cutting vibration and cutting condition features are input to a deep autoencoder for dimension reduction. Then, a deep multi-layer perceptron is developed to estimate the tool wear. The dataset is obtained with a carefully designed varying cutting depth milling experiment. The proposed method works well, with an error of 8.2% on testing samples, which shows an obvious advantage over the classic machine learning method.https://www.mdpi.com/2076-3417/10/19/6916tool wear monitoringmillingcomplex partdeep learningautoencoderdeep multi-layer perceptron |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaodong Zhang Ce Han Ming Luo Dinghua Zhang |
spellingShingle |
Xiaodong Zhang Ce Han Ming Luo Dinghua Zhang Tool Wear Monitoring for Complex Part Milling Based on Deep Learning Applied Sciences tool wear monitoring milling complex part deep learning autoencoder deep multi-layer perceptron |
author_facet |
Xiaodong Zhang Ce Han Ming Luo Dinghua Zhang |
author_sort |
Xiaodong Zhang |
title |
Tool Wear Monitoring for Complex Part Milling Based on Deep Learning |
title_short |
Tool Wear Monitoring for Complex Part Milling Based on Deep Learning |
title_full |
Tool Wear Monitoring for Complex Part Milling Based on Deep Learning |
title_fullStr |
Tool Wear Monitoring for Complex Part Milling Based on Deep Learning |
title_full_unstemmed |
Tool Wear Monitoring for Complex Part Milling Based on Deep Learning |
title_sort |
tool wear monitoring for complex part milling based on deep learning |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2020-10-01 |
description |
Tool wear monitoring is necessary for cost reduction and productivity improvement in the machining industry. Machine learning has been proven to be an effective means of tool wear monitoring. Feature engineering is the core of the machining learning model. In complex parts milling, cutting conditions are time-varying due to the variable engagement between cutting tool and the complex geometric features of the workpiece. In such cases, the features for accurate tool wear monitoring are tricky to select. Besides, usually few sensors are available in an actual machining situation. This causes a high correlation between the hand-designed features, leading to the low accuracy and weak generalization ability of the machine learning model. This paper presents a tool wear monitoring method for complex part milling based on deep learning. The features are pre-selected based on cutting force model and wavelet packet decomposition. The pre-selected cutting forces, cutting vibration and cutting condition features are input to a deep autoencoder for dimension reduction. Then, a deep multi-layer perceptron is developed to estimate the tool wear. The dataset is obtained with a carefully designed varying cutting depth milling experiment. The proposed method works well, with an error of 8.2% on testing samples, which shows an obvious advantage over the classic machine learning method. |
topic |
tool wear monitoring milling complex part deep learning autoencoder deep multi-layer perceptron |
url |
https://www.mdpi.com/2076-3417/10/19/6916 |
work_keys_str_mv |
AT xiaodongzhang toolwearmonitoringforcomplexpartmillingbasedondeeplearning AT cehan toolwearmonitoringforcomplexpartmillingbasedondeeplearning AT mingluo toolwearmonitoringforcomplexpartmillingbasedondeeplearning AT dinghuazhang toolwearmonitoringforcomplexpartmillingbasedondeeplearning |
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