Estimation the wear state of milling tools using a combined ensemble empirical mode decomposition and support vector machine method
Vibrational signals resulting from tool wear have non-linear and non-stationary features. It is also difficult to acquire large numbers of typically worn samples in practice. In this work, a method of predicting the wear of milling tools is proposed based on ensemble empirical mode decomposition (EE...
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| Format: | Article |
| Language: | English |
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The Japan Society of Mechanical Engineers
2018-06-01
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| Series: | Journal of Advanced Mechanical Design, Systems, and Manufacturing |
| Subjects: | |
| Online Access: | https://www.jstage.jst.go.jp/article/jamdsm/12/2/12_2018jamdsm0059/_pdf/-char/en |
| _version_ | 1828271602496700416 |
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| author | Chuangwen XU Yuzhen CHAI Huaiyuan LI Zhicheng SHI |
| author_facet | Chuangwen XU Yuzhen CHAI Huaiyuan LI Zhicheng SHI |
| author_sort | Chuangwen XU |
| collection | DOAJ |
| description | Vibrational signals resulting from tool wear have non-linear and non-stationary features. It is also difficult to acquire large numbers of typically worn samples in practice. In this work, a method of predicting the wear of milling tools is proposed based on ensemble empirical mode decomposition (EEMD) and the use of a support vector machine (SVM). The EEMD method is used to decompose the original non-stationary vibration acceleration signals into several stationary intrinsic mode functions (IMFs). The energies of the signals in these different frequency bands change when the tool is worn. Thus, the tool wear state can be identified by calculating the EEMD energies and energy entropies of the different vibrational signals. The correlation coefficients between the IMF components and original signal were calculated and wear-sensitive IMFs chosen. A SVM is then established by considering the energy features extracted from a number of wear-sensitive IMFs that contain primary information on tool wear. These are considered as the inputs to judge the wear state of the tool. The results show that the method is capable of predicting the wear state of the milling tool to good effect. Furthermore, the predictions made using an LS-SVM based on EEMD method are more accurate than those made using FFT, Wavelet analysis and EMD methods. |
| first_indexed | 2024-04-13T05:55:27Z |
| format | Article |
| id | doaj.art-f6b0a644ff2245a58808d2402a8f5fe4 |
| institution | Directory Open Access Journal |
| issn | 1881-3054 |
| language | English |
| last_indexed | 2024-04-13T05:55:27Z |
| publishDate | 2018-06-01 |
| publisher | The Japan Society of Mechanical Engineers |
| record_format | Article |
| series | Journal of Advanced Mechanical Design, Systems, and Manufacturing |
| spelling | doaj.art-f6b0a644ff2245a58808d2402a8f5fe42022-12-22T02:59:39ZengThe Japan Society of Mechanical EngineersJournal of Advanced Mechanical Design, Systems, and Manufacturing1881-30542018-06-01122JAMDSM0059JAMDSM005910.1299/jamdsm.2018jamdsm0059jamdsmEstimation the wear state of milling tools using a combined ensemble empirical mode decomposition and support vector machine methodChuangwen XU0Yuzhen CHAI1Huaiyuan LI2Zhicheng SHI3Lanzhou Institute of Technology, Provincial Key Laboratory for Green Cutting Technology and Application of Gansu ProvinceLanzhou Institute of Technology, Provincial Key Laboratory for Green Cutting Technology and Application of Gansu ProvinceLanzhou Institute of Technology, Provincial Key Laboratory for Green Cutting Technology and Application of Gansu ProvinceLanzhou Institute of Technology, Provincial Key Laboratory for Green Cutting Technology and Application of Gansu ProvinceVibrational signals resulting from tool wear have non-linear and non-stationary features. It is also difficult to acquire large numbers of typically worn samples in practice. In this work, a method of predicting the wear of milling tools is proposed based on ensemble empirical mode decomposition (EEMD) and the use of a support vector machine (SVM). The EEMD method is used to decompose the original non-stationary vibration acceleration signals into several stationary intrinsic mode functions (IMFs). The energies of the signals in these different frequency bands change when the tool is worn. Thus, the tool wear state can be identified by calculating the EEMD energies and energy entropies of the different vibrational signals. The correlation coefficients between the IMF components and original signal were calculated and wear-sensitive IMFs chosen. A SVM is then established by considering the energy features extracted from a number of wear-sensitive IMFs that contain primary information on tool wear. These are considered as the inputs to judge the wear state of the tool. The results show that the method is capable of predicting the wear state of the milling tool to good effect. Furthermore, the predictions made using an LS-SVM based on EEMD method are more accurate than those made using FFT, Wavelet analysis and EMD methods.https://www.jstage.jst.go.jp/article/jamdsm/12/2/12_2018jamdsm0059/_pdf/-char/encutting vibrationensemble empirical mode decompositionintrinsic mode functionenergy entropysupport vector machinetool wear prediction |
| spellingShingle | Chuangwen XU Yuzhen CHAI Huaiyuan LI Zhicheng SHI Estimation the wear state of milling tools using a combined ensemble empirical mode decomposition and support vector machine method Journal of Advanced Mechanical Design, Systems, and Manufacturing cutting vibration ensemble empirical mode decomposition intrinsic mode function energy entropy support vector machine tool wear prediction |
| title | Estimation the wear state of milling tools using a combined ensemble empirical mode decomposition and support vector machine method |
| title_full | Estimation the wear state of milling tools using a combined ensemble empirical mode decomposition and support vector machine method |
| title_fullStr | Estimation the wear state of milling tools using a combined ensemble empirical mode decomposition and support vector machine method |
| title_full_unstemmed | Estimation the wear state of milling tools using a combined ensemble empirical mode decomposition and support vector machine method |
| title_short | Estimation the wear state of milling tools using a combined ensemble empirical mode decomposition and support vector machine method |
| title_sort | estimation the wear state of milling tools using a combined ensemble empirical mode decomposition and support vector machine method |
| topic | cutting vibration ensemble empirical mode decomposition intrinsic mode function energy entropy support vector machine tool wear prediction |
| url | https://www.jstage.jst.go.jp/article/jamdsm/12/2/12_2018jamdsm0059/_pdf/-char/en |
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