Milling Cutter Wear Prediction Based on Feature Extraction and LongShort-term Memory Neural Networks
The wear of milling cutters during high-speed milling processes affects the quality of the work-piece, productivity and manufacturing costs. Accurate tool wear prediction can therefore optimize production decisions and avoid losses due to tool wear. To improve the prediction precision, this paper pr...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2021-01-01
|
| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.04.100 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849224891589459968 |
|---|---|
| author | ZHOU Chengpeng WANG Weijun HOU Zhicheng FENG Wei |
| author_facet | ZHOU Chengpeng WANG Weijun HOU Zhicheng FENG Wei |
| author_sort | ZHOU Chengpeng |
| collection | DOAJ |
| description | The wear of milling cutters during high-speed milling processes affects the quality of the work-piece, productivity and manufacturing costs. Accurate tool wear prediction can therefore optimize production decisions and avoid losses due to tool wear. To improve the prediction precision, this paper proposes a method for predicting the wear of milling tools based on feature extraction and long short-term memory neural networks. By extracting the time domain features, frequency domain features and time-frequency domain features based on time series data generated during industrial processes, and using long short-term memory neural networks to learn the complex mapping relationship between the extracted features and the wear amount, it infer the future wear of the milling cutter. Experimental results show that this method reduces mean square error by 67.07% and 41.31% compared with the SVR model and the BPNN model respectively, and reduces mean square error by 77.09% and mean absolute percentage error by 5.685% compared with the CNN, enables a more accurate and reliable milling cutter wear prediction. |
| format | Article |
| id | doaj-art-8e4c81ded685481fab51723fe032b29d |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-8e4c81ded685481fab51723fe032b29d2025-08-25T06:50:05ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272021-01-0138596582317412Milling Cutter Wear Prediction Based on Feature Extraction and LongShort-term Memory Neural NetworksZHOU ChengpengWANG WeijunHOU ZhichengFENG WeiThe wear of milling cutters during high-speed milling processes affects the quality of the work-piece, productivity and manufacturing costs. Accurate tool wear prediction can therefore optimize production decisions and avoid losses due to tool wear. To improve the prediction precision, this paper proposes a method for predicting the wear of milling tools based on feature extraction and long short-term memory neural networks. By extracting the time domain features, frequency domain features and time-frequency domain features based on time series data generated during industrial processes, and using long short-term memory neural networks to learn the complex mapping relationship between the extracted features and the wear amount, it infer the future wear of the milling cutter. Experimental results show that this method reduces mean square error by 67.07% and 41.31% compared with the SVR model and the BPNN model respectively, and reduces mean square error by 77.09% and mean absolute percentage error by 5.685% compared with the CNN, enables a more accurate and reliable milling cutter wear prediction.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.04.100prediction controllong short-term memory neural network(LSTM)tool weartime series datafeature extraction |
| spellingShingle | ZHOU Chengpeng WANG Weijun HOU Zhicheng FENG Wei Milling Cutter Wear Prediction Based on Feature Extraction and LongShort-term Memory Neural Networks Kongzhi Yu Xinxi Jishu prediction control long short-term memory neural network(LSTM) tool wear time series data feature extraction |
| title | Milling Cutter Wear Prediction Based on Feature Extraction and LongShort-term Memory Neural Networks |
| title_full | Milling Cutter Wear Prediction Based on Feature Extraction and LongShort-term Memory Neural Networks |
| title_fullStr | Milling Cutter Wear Prediction Based on Feature Extraction and LongShort-term Memory Neural Networks |
| title_full_unstemmed | Milling Cutter Wear Prediction Based on Feature Extraction and LongShort-term Memory Neural Networks |
| title_short | Milling Cutter Wear Prediction Based on Feature Extraction and LongShort-term Memory Neural Networks |
| title_sort | milling cutter wear prediction based on feature extraction and longshort term memory neural networks |
| topic | prediction control long short-term memory neural network(LSTM) tool wear time series data feature extraction |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.04.100 |
| work_keys_str_mv | AT zhouchengpeng millingcutterwearpredictionbasedonfeatureextractionandlongshorttermmemoryneuralnetworks AT wangweijun millingcutterwearpredictionbasedonfeatureextractionandlongshorttermmemoryneuralnetworks AT houzhicheng millingcutterwearpredictionbasedonfeatureextractionandlongshorttermmemoryneuralnetworks AT fengwei millingcutterwearpredictionbasedonfeatureextractionandlongshorttermmemoryneuralnetworks |