System for Tool-Wear Condition Monitoring in CNC Machines under Variations of Cutting Parameter Based on Fusion Stray Flux-Current Processing

Abstract

[EN] The computer numerical control (CNC) machine has recently taken a fundamental role in the manufacturing industry, which is primordial for the economic development of many countries. Current high quality production standards, along with the requirement for maximum economic benefits demand the use of tool condition monitoring (TCM) systems able to monitor and diagnose cutting tool wearing. Current TCM methodologies mainly rely on vibration signals, cutting force signals, acoustic emission (AE) signals, which have the common drawback of requiring the in-stallation of sensors near the working area, a factor that limits their application under practical terms. Moreover, as machining processes require the optimal tuning of cutting parameters, novel methodologies must be able to perform the diagnosis under a variation of cutting parameters. This paper proposes a novel non-invasive method capable to automatically diagnose cutting tool wearing in CNC machines under the variation of cutting speed, and feed rate cutting parameters. The proposal relies on the sensor information fusion of spindle-motor stray flux and current signals by means of statistical and non-statistical time-domain parameters, which are then reduced by means of a linear discriminant analysis (LDA), and then a feed-forward neural network is used to automatically classify the level of wear on the cutting tool. The proposal is validated with a Fanuc Oi mate Computer Numeric Control (CNC) turning machine for three different cutting tool wearing levels and different cutting speed, and feed rate values.