Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model
| Autor: | Kamran Javed, Xiang Li, Rafael Gouriveau, Noureddine Zerhouni |
|---|---|
| Přispěvatelé: | Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS), Singapore Institute of Manufacturing Technology (SIMTech) |
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Engineering Downtime Cutting tool Ensemble forecasting business.industry [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS] Context (language use) 02 engineering and technology Industrial and Manufacturing Engineering [SPI.AUTO]Engineering Sciences [physics]/Automatic Reliability engineering 020901 industrial engineering & automation Artificial Intelligence Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Numerical control Prognostics 020201 artificial intelligence & image processing Tool wear business Software |
| Zdroj: | Journal of Intelligent Manufacturing Journal of Intelligent Manufacturing, 2016, pp, pp.1-18 |
| ISSN: | 1572-8145 0956-5515 |
| DOI: | 10.1007/s10845-016-1221-2 |
| Popis: | International audience; In a high speed milling operation the cutting tool acts as a backbone of machining process, which requires timely replacement to avoid loss of costly workpiece or machine downtime. To this aim, prognostics is applied for predicting tool wear and estimating its life span to replace the cutting tool before failure. However, the life span of cutting tools varies between minutes or hours, therefore time is critical for tool condition monitoring. Moreover, complex nature of manufacturing process requires models that can accurately predict tool degradation and provide confidence for decisions. In this context, a data-driven connectionist approach is proposed for tool condition monitoring application. In brief, an ensemble of Summation Wavelet-Extreme Learning Machine models is proposed with incremental learning scheme. The proposed approach is validated on cutting force measurements data from Computer Numerical Control machine. Results clearly show the significance of our proposition. |
| Databáze: | OpenAIRE |
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