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

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

Li, Yingguang, Mou, Wenping, Li, Jingjing, Liu, Changqing and Gao, Xiaoyu ORCID: 0000-0001-5625-3654 (2020) An automatic and accurate method for tool wear inspection using grayscale image probability algorithm based on bayesian Inference. Robotics and Computer-Integrated Manufacturing, 68:102079. ISSN 0736-5845 (doi:https://doi.org/10.1016/j.rcim.2020.102079)

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Digital Manufacturing, Tool Wear, Automatic inspection, Bayesian Inference, Grayscale image
Subjects: T Technology > TS Manufactures
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Design, Manufacturing and Innovative Products Research Theme
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 20 Oct 2020 18:14
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/29853

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