Application of dynamic adaptive technology based on symbolic regression to identify modal parameters of chip sorter
Cantilever structure, which needs high-frequency rotating motion, is widely used in the field of chip manufacturing. The motion stability of high-frequency rotation motion of cantilever structure directly affects the production efficiency. The traditional dynamic analysis method is no longer applica...
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| Main Authors: | , , , |
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| Format: | article |
| Language: | EN |
| Published: |
JVE International
2021
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| Subjects: | |
| Online Access: | https://doaj.org/article/90d6b6a1c7b54610950f81e030b2184c |
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| Summary: | Cantilever structure, which needs high-frequency rotating motion, is widely used in the field of chip manufacturing. The motion stability of high-frequency rotation motion of cantilever structure directly affects the production efficiency. The traditional dynamic analysis method is no longer applicable to analyze the vibration of cantilever structure under high-frequency rotating motion. It is also urgent to control the high-frequency rotation motion of cantilever mechanism. In this paper, experiments are designed to collect strain signals of chip sorter’s cantilever under high-frequency operation, and modal parameters are extracted from time domain signals by symbolic regression algorithm. The results of modal parameter identification at high-frequency are selected as the samples, and the Gaussian process regression model of machine learning algorithm is used to train the samples. The prediction results can be used as the basis of structural stability research and vibration suppression. |
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