Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network
Stamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/1/262 |
| _version_ | 1797542572338446336 |
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| author | Chih-Yung Huang Zaky Dzulfikri |
| author_facet | Chih-Yung Huang Zaky Dzulfikri |
| author_sort | Chih-Yung Huang |
| collection | DOAJ |
| description | Stamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of the process; therefore, effective monitoring of the tool health condition is essential for detecting stamping defects. In this study, vibration measurement was used to monitor the stamping process and tool health. A system was developed for capturing signals in the stamping process, and each stamping cycle was selected through template matching. A one-dimensional (1D) convolutional neural network (CNN) was developed to classify the tool wear condition. The results revealed that the 1D CNN architecture a yielded a high accuracy (>99%) and fast adaptability among different models. |
| first_indexed | 2024-03-10T13:32:28Z |
| format | Article |
| id | doaj.art-f47794622cbe489a90be270d4e079b15 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:32:28Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f47794622cbe489a90be270d4e079b152023-11-21T07:51:58ZengMDPI AGSensors1424-82202021-01-0121126210.3390/s21010262Stamping Monitoring by Using an Adaptive 1D Convolutional Neural NetworkChih-Yung Huang0Zaky Dzulfikri1Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung 41170, TaiwanDepartment of Mechanical Engineering, National Chin-Yi University of Technology, Taichung 41170, TaiwanStamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of the process; therefore, effective monitoring of the tool health condition is essential for detecting stamping defects. In this study, vibration measurement was used to monitor the stamping process and tool health. A system was developed for capturing signals in the stamping process, and each stamping cycle was selected through template matching. A one-dimensional (1D) convolutional neural network (CNN) was developed to classify the tool wear condition. The results revealed that the 1D CNN architecture a yielded a high accuracy (>99%) and fast adaptability among different models.https://www.mdpi.com/1424-8220/21/1/262stamping processvibrationspectrum densityone-dimensional convolutional neural networkclassification |
| spellingShingle | Chih-Yung Huang Zaky Dzulfikri Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network Sensors stamping process vibration spectrum density one-dimensional convolutional neural network classification |
| title | Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network |
| title_full | Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network |
| title_fullStr | Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network |
| title_full_unstemmed | Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network |
| title_short | Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network |
| title_sort | stamping monitoring by using an adaptive 1d convolutional neural network |
| topic | stamping process vibration spectrum density one-dimensional convolutional neural network classification |
| url | https://www.mdpi.com/1424-8220/21/1/262 |
| work_keys_str_mv | AT chihyunghuang stampingmonitoringbyusinganadaptive1dconvolutionalneuralnetwork AT zakydzulfikri stampingmonitoringbyusinganadaptive1dconvolutionalneuralnetwork |