Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume 104, Issue 5-8, Pages 1953-1966Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00170-019-03919-4
Keywords
Multiple abnormal conditions' detection; Complex machining process; Deep forest; Feature selection; Multi-signal process information
Funding
- National Natural Science Foundation of China [51705015]
- Equipment Pre-Research Program of China [41423010301]
- National Defense Fundamental Research Foundation of China [JCKY2016601C006]
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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.
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