TOOL WEAR STATE MONITORING BASED ON WAVELET PACKET BP＿ADABOOST ALGORITHM
Tools are the key parts in the process of NC milling machine. They are in high-speed processing for a long time and are prone to failure. Aiming at the problems of less tool wear state data,low diagnostic efficiency,high maintenance cost and lack of effective diagnostic methods during CNC machine to...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2019-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2019.06.004 |
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| author | ZHU Xiang XIE Feng |
| author_facet | ZHU Xiang XIE Feng |
| author_sort | ZHU Xiang |
| collection | DOAJ |
| description | Tools are the key parts in the process of NC milling machine. They are in high-speed processing for a long time and are prone to failure. Aiming at the problems of less tool wear state data,low diagnostic efficiency,high maintenance cost and lack of effective diagnostic methods during CNC machine tool processing,A method of extracting features by wavelet packet analysis and kernel principal component analysis,and using BP<sub> </sub>Ada Boost algorithm to diagnose tool wear state is proposed.The tool vibration signal and the cutting force signal are collected by installing an acceleration sensor on the machined workpiece of the numerical control machine tool and a force gauge on the workbench; Then the wavelet packet decomposition is performed on the signal to pass the signal through the low-pass filter and the high-pass filter of different dimensions,so that the conditional selection can be performed to form the energy value corresponding to the different frequency bands. The data after the dimension reduction of the kernel principal component analysis is taken as the characteristic parameter of the tool wear state; Finally,the eigenvectors are used to train and validate the BP <sub>A</sub>daBoost classification model. The experimental result shows that the BP <sub>A</sub>da Boost algorithm can effectively diagnose the wear state of the tool in CNC machine tools compared with the SVM algorithm. |
| format | Article |
| id | doaj-art-40eb9c4104924cd18ba9f15ea7b2108f |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2019-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-40eb9c4104924cd18ba9f15ea7b2108f2025-01-15T02:28:47ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692019-01-01411292129730606247TOOL WEAR STATE MONITORING BASED ON WAVELET PACKET BP＿ADABOOST ALGORITHMZHU XiangXIE FengTools are the key parts in the process of NC milling machine. They are in high-speed processing for a long time and are prone to failure. Aiming at the problems of less tool wear state data,low diagnostic efficiency,high maintenance cost and lack of effective diagnostic methods during CNC machine tool processing,A method of extracting features by wavelet packet analysis and kernel principal component analysis,and using BP<sub> </sub>Ada Boost algorithm to diagnose tool wear state is proposed.The tool vibration signal and the cutting force signal are collected by installing an acceleration sensor on the machined workpiece of the numerical control machine tool and a force gauge on the workbench; Then the wavelet packet decomposition is performed on the signal to pass the signal through the low-pass filter and the high-pass filter of different dimensions,so that the conditional selection can be performed to form the energy value corresponding to the different frequency bands. The data after the dimension reduction of the kernel principal component analysis is taken as the characteristic parameter of the tool wear state; Finally,the eigenvectors are used to train and validate the BP <sub>A</sub>daBoost classification model. The experimental result shows that the BP <sub>A</sub>da Boost algorithm can effectively diagnose the wear state of the tool in CNC machine tools compared with the SVM algorithm.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2019.06.004Tool wear stateCutting force signalAcceleration signalWavelet packet analysisKernel principal component analysis（KPCA） dimension reductionBPAda Boost |
| spellingShingle | ZHU Xiang XIE Feng TOOL WEAR STATE MONITORING BASED ON WAVELET PACKET BP＿ADABOOST ALGORITHM Jixie qiangdu Tool wear state Cutting force signal Acceleration signal Wavelet packet analysis Kernel principal component analysis（KPCA） dimension reduction BPAda Boost |
| title | TOOL WEAR STATE MONITORING BASED ON WAVELET PACKET BP＿ADABOOST ALGORITHM |
| title_full | TOOL WEAR STATE MONITORING BASED ON WAVELET PACKET BP＿ADABOOST ALGORITHM |
| title_fullStr | TOOL WEAR STATE MONITORING BASED ON WAVELET PACKET BP＿ADABOOST ALGORITHM |
| title_full_unstemmed | TOOL WEAR STATE MONITORING BASED ON WAVELET PACKET BP＿ADABOOST ALGORITHM |
| title_short | TOOL WEAR STATE MONITORING BASED ON WAVELET PACKET BP＿ADABOOST ALGORITHM |
| title_sort | tool wear state monitoring based on wavelet packet bp adaboost algorithm |
| topic | Tool wear state Cutting force signal Acceleration signal Wavelet packet analysis Kernel principal component analysis（KPCA） dimension reduction BPAda Boost |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2019.06.004 |
| work_keys_str_mv | AT zhuxiang toolwearstatemonitoringbasedonwaveletpacketbpadaboostalgorithm AT xiefeng toolwearstatemonitoringbasedonwaveletpacketbpadaboostalgorithm |