An automatic and accurate method for tool wear inspection using grayscale image probability algorithm based on bayesian inference

Accurate, rapid and automated tool wear inspection is critical to manufacturing quality, cost efficiency in smart systems. However, manual of still a common industrial practice which inefficient, prone human errors not suitable for digitized manufacturing. Previously reported automatic methods were inaccurate because they only used the remaining worn boundary (i.e., partial-absence original boundary) approximate wear. The authors discovered association principle between change law cutting edge grayscale relative position boundary, was establish probability functions accurately reconstruct curved via Bayesian Inference. experiment results this paper proved higher accuracy than previous methods.