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

准确，快速和自动化的工具磨损检查对于智能制造系统中的制造质量，成本和效率至关重要。但是，手动检查工具磨损仍然是一种普遍的工业实践，效率低下，容易出现人为错误，不适合数字化制造。先前报道的自动工具磨损检查方法是不准确的，因为它们仅使用剩余的磨损边界（即部分不存在的原始工具边界）来近似工具磨损。作者发现切削刃灰度的变化规律与原始边界和磨损边界的相对位置之间的关联原理，用于建立概率函数，以通过贝叶斯推理准确地重建弯曲的原始工具边界。