Next Article in Journal
OPF/PMMA Cage System as an Alternative Approach for the Treatment of Vertebral Corpectomy
Next Article in Special Issue
A Qualitative Tool Condition Monitoring Framework Using Convolution Neural Network and Transfer Learning
Previous Article in Journal
Pomegranate: Nutraceutical with Promising Benefits on Human Health
Previous Article in Special Issue
Semi-Active Magnetorheological Damper Device for Chatter Mitigation during Milling of Thin-Floor Components
Article

Tool Wear Monitoring for Complex Part Milling Based on Deep Learning

by 1, 1, 1,2,* and 1,2
1
Key Laboratory of High Performance Manufacturing for Aero Engine, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, China
2
Engineering Research Center of Advanced Manufacturing Technology for Aero Engine, Ministry of Education, Northwestern Polytechnical University, Xi’an 710072, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(19), 6916; https://doi.org/10.3390/app10196916
Received: 1 September 2020 / Revised: 22 September 2020 / Accepted: 29 September 2020 / Published: 2 October 2020
(This article belongs to the Special Issue Machining Dynamics and Parameters Process Optimization)
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. View Full-Text
Keywords: tool wear monitoring; milling; complex part; deep learning; autoencoder; deep multi-layer perceptron tool wear monitoring; milling; complex part; deep learning; autoencoder; deep multi-layer perceptron
Show Figures

Figure 1

MDPI and ACS Style

Zhang, X.; Han, C.; Luo, M.; Zhang, D. Tool Wear Monitoring for Complex Part Milling Based on Deep Learning. Appl. Sci. 2020, 10, 6916. https://doi.org/10.3390/app10196916

AMA Style

Zhang X, Han C, Luo M, Zhang D. Tool Wear Monitoring for Complex Part Milling Based on Deep Learning. Applied Sciences. 2020; 10(19):6916. https://doi.org/10.3390/app10196916

Chicago/Turabian Style

Zhang, Xiaodong, Ce Han, Ming Luo, and Dinghua Zhang. 2020. "Tool Wear Monitoring for Complex Part Milling Based on Deep Learning" Applied Sciences 10, no. 19: 6916. https://doi.org/10.3390/app10196916

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop