期刊
JOURNAL OF INTELLIGENT MANUFACTURING
卷 23, 期 3, 页码 869-882出版社
SPRINGER
DOI: 10.1007/s10845-010-0443-y
关键词
Tool wear; Turning processes; Monitoring; Neuro-fuzzy inference system; Transductive reasoning
资金
- Spanish Ministry of Science and Innovation [DPI2008-01978, CIT-420000-2008-13]
Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.
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