In-process complex machining condition monitoring based on deep forest and process information fusion
| Autor: | Zhiyuan Lu, Wei Dai, Jiahuan Sun, Meiqing Wang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Fusion Computer science Mechanical Engineering Real-time computing Condition monitoring 02 engineering and technology Work in process Industrial and Manufacturing Engineering Computer Science Applications 020901 industrial engineering & automation Machining Control and Systems Engineering Process information Software |
| Zdroj: | The International Journal of Advanced Manufacturing Technology. 104:1953-1966 |
| ISSN: | 1433-3015 0268-3768 |
| Popis: | Abnormal machining condition causes losses of quality for finished part. A machining condition monitoring system is considerably vital in the intelligent manufacturing process. Existing machining condition monitoring methods usually detect only one single abnormal condition under the same machining process, which is unrealistic and impractical for real complicated machining process. In this paper, a novel hybrid condition monitoring approach for multiple abnormal conditions’ detection of complicated machining process by using deep forest and multi-process information fusion is proposed. First, various process data are obtained from a triaxial accelerometer and a sound sensor mounted on the spindle of CNC. Then, the time domain, frequency domain, and time-frequency domain features extracted from the multiple sensory signals are simultaneously optimized to select a subset with key features by the lasso technique. Furthermore, deep forest is utilized as a condition classifier by using the selected features. Finally, cutting experiments are designed and conducted, and the results show that the proposed method can effectively detect the multiple abnormal conditions under the different machining parameters. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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