engleski

A MODEL OF TOOL WEAR MONITORING SYSTEM FOR TURNING

Acquiring high-quality and timely information on the tool wear condition in real time, presents a necessary prerequisite for identification of tool wear degree, which significantly improves the stability and quality of the machining process. Defined in this paper is a model of tool wear monitoring system with special emphasis on the module for acquisition and processing of vibration acceleration signal by applying discrete wavelet transformations (DWT) in signal decomposition. The paper presents a model of the developed fuzzy system for tool wear classification. The system comprises three modules: module for data acquisition and processing, module for tool wear classification, and module for decision-making. The selected method for feature extraction is presented within the module for data classification and processing. The selected model for the fuzzy classifier and classification in experimental laboratory conditions is shown within data classification and clustering. The proposed model has been tested in longitudinal and transversal machining operations.

artificial intelligence ; feature extraction ; tool wear monitoring

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