Sensor fusion and the application of artificial intelligence to identify tool wear in turning operations

Abstract This paper aims to develop an effective sensor fusion model for turning processes the detection of tool wear. Fusion sensors’ data combined with novelty algorithm and learning vector quantisation (LVQ) neural networks is used detect wear present diagnostic prognostic information. To reduce number sensors required in monitoring system support fusion, ASPS approach (Automated Sensor Signal Processing Selection System) select most appropriate signal processing methods design condition system. The experimental results show that proposed has demonstrated its efficacy implementation solution turning. prove sensitive sensory characteristic features use AI have been successful prediction capability complexity systems development a using self-learning method.