An online belt wear monitoring method for abrasive belt grinding under varying grinding parameters
| Autor: | Jianyong Li, Yueming Liu, Meng Nie, Wenxi Wang, Can Cheng |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Imagination
Belt grinding 0209 industrial biotechnology Materials science Training set Strategy and Management media_common.quotation_subject Abrasive Mechanical engineering 02 engineering and technology Management Science and Operations Research 021001 nanoscience & nanotechnology Industrial and Manufacturing Engineering Grinding 020901 industrial engineering & automation Monitoring methods 0210 nano-technology media_common |
| Zdroj: | Journal of Manufacturing Processes. 50:80-89 |
| ISSN: | 1526-6125 |
| Popis: | Abrasive belt grinding has attracted attention in recent years in both industry and academia due to the rapid development of abrasive belts; however, online monitoring of abrasive belt wear under varying grinding parameters is challenging. In this paper, the multi-sensor fusion of sound and current signals is used to resolve the abovementioned problem. First, the characteristics of the grinding sound and current are investigated in the time-domain and frequency-domain, and then their differences under various belt wear states and grinding parameters are discussed; based on the investigation, several features are extracted that indicate the belt wear state. The abovementioned discussion demonstrated that grinding sound signals have an abundance of information at high frequencies (6–20 kHz); in contrast, grinding current signals contain information at low frequencies. Furthermore, the grinding sound and current signals have different sensitivities to the grinding parameters and belt wear. Finally, a Bayesian network is proposed to identify the wear state; moreover, its adaptability under changing parameters and the effect of multi-sensor fusion are discussed. The results show that the accuracy reaches 100% with enough training data; additionally, when the training data only covers a limited range of grinding parameters, the fusion of the sound and current substantially improves the accuracy of the prediction results from 86% to 95%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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