Neural Networks Tool Condition Monitoring in Single-point Dressing Operations

Abstract

Cognitive modeling of tool wear progress is employed to obtain a dependable trend of tool wear curves for optimal utilization of tool life and productivity improvement, while preserving the surface integrity of the ground parts. This paper describes a method to characterize the dresser wear condition utilizing vibration signals by applying a cognitive paradigm, such as Artificial Neural Networks (ANNs). Dressing tests with a single-point dresser were performed in a surface grinding machine and tool wear measurements taken along the experiments. The results show that ANN processing offers an effective method for the monitoring of grinding wheel wear based on vibration signal analysis.