Tool Wear Condition Monitoring in Milling Process Based on Current Sensors
Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. This is typically accomplished using indirect TCM methods that synthesize the information collected from one or more sensors to estimate tool condition based on machine learning approaches...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9096351/ |
| _version_ | 1818736483754835968 |
|---|---|
| author | Yuqing Zhou Weifang Sun |
| author_facet | Yuqing Zhou Weifang Sun |
| author_sort | Yuqing Zhou |
| collection | DOAJ |
| description | Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. This is typically accomplished using indirect TCM methods that synthesize the information collected from one or more sensors to estimate tool condition based on machine learning approaches. Among the many sensor types available for conducting TCM, motor current sensors offer numerous advantages, in that they are inexpensive, easily installed, and have no effect on the milling process. Accordingly, this study proposes a new TCM method employing a few appropriate current sensor signal features based on the time, frequency, and time - frequency domains of the signals and an advanced monitoring model based on an improved kernel extreme learning machine (KELM). The selected multi-domain features are strongly correlated with tool wear condition and overcome the loss of useful information related to tool condition when employing a single domain. The improved KELM employs a two-layer network structure and an angle kernel function that includes no hyperparameter, which overcome the drawbacks of KELM in terms of the difficulty of learning the features of complex nonlinear data and avoiding the need for preselecting the kernel function and its hyperparameter. The performance of the proposed method is verified by its application to the benchmark NASA milling dataset and separate TCM experiments in comparison with existing TCM methods. The results indicate that the proposed TCM method achieves excellent monitoring performance using only a few key signal features of current sensors. |
| first_indexed | 2024-12-18T00:37:52Z |
| format | Article |
| id | doaj.art-e87d75620b9f4244a5307f42a441a746 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:37:52Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e87d75620b9f4244a5307f42a441a7462022-12-21T21:26:57ZengIEEEIEEE Access2169-35362020-01-018954919550210.1109/ACCESS.2020.29955869096351Tool Wear Condition Monitoring in Milling Process Based on Current SensorsYuqing Zhou0https://orcid.org/0000-0002-8580-5427Weifang Sun1https://orcid.org/0000-0002-4181-1606College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, ChinaCollege of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, ChinaAccurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. This is typically accomplished using indirect TCM methods that synthesize the information collected from one or more sensors to estimate tool condition based on machine learning approaches. Among the many sensor types available for conducting TCM, motor current sensors offer numerous advantages, in that they are inexpensive, easily installed, and have no effect on the milling process. Accordingly, this study proposes a new TCM method employing a few appropriate current sensor signal features based on the time, frequency, and time - frequency domains of the signals and an advanced monitoring model based on an improved kernel extreme learning machine (KELM). The selected multi-domain features are strongly correlated with tool wear condition and overcome the loss of useful information related to tool condition when employing a single domain. The improved KELM employs a two-layer network structure and an angle kernel function that includes no hyperparameter, which overcome the drawbacks of KELM in terms of the difficulty of learning the features of complex nonlinear data and avoiding the need for preselecting the kernel function and its hyperparameter. The performance of the proposed method is verified by its application to the benchmark NASA milling dataset and separate TCM experiments in comparison with existing TCM methods. The results indicate that the proposed TCM method achieves excellent monitoring performance using only a few key signal features of current sensors.https://ieeexplore.ieee.org/document/9096351/Tool condition monitoring (TCM)milling processcurrent sensorkernel extreme learning machine (KELM)angle kernel |
| spellingShingle | Yuqing Zhou Weifang Sun Tool Wear Condition Monitoring in Milling Process Based on Current Sensors IEEE Access Tool condition monitoring (TCM) milling process current sensor kernel extreme learning machine (KELM) angle kernel |
| title | Tool Wear Condition Monitoring in Milling Process Based on Current Sensors |
| title_full | Tool Wear Condition Monitoring in Milling Process Based on Current Sensors |
| title_fullStr | Tool Wear Condition Monitoring in Milling Process Based on Current Sensors |
| title_full_unstemmed | Tool Wear Condition Monitoring in Milling Process Based on Current Sensors |
| title_short | Tool Wear Condition Monitoring in Milling Process Based on Current Sensors |
| title_sort | tool wear condition monitoring in milling process based on current sensors |
| topic | Tool condition monitoring (TCM) milling process current sensor kernel extreme learning machine (KELM) angle kernel |
| url | https://ieeexplore.ieee.org/document/9096351/ |
| work_keys_str_mv | AT yuqingzhou toolwearconditionmonitoringinmillingprocessbasedoncurrentsensors AT weifangsun toolwearconditionmonitoringinmillingprocessbasedoncurrentsensors |