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Journal of Intelligent Manufacturing

Published in:

15-02-2023

Partitioned abrasive belt condition monitoring based on a unified coefficient and image processing

Authors: Xiaokang Huang, Xukai Ren, Huanwei Yu, Xiyong Du, Xianfeng Chen, Ze Chai, Xiaoqi Chen

Published in: Journal of Intelligent Manufacturing | Issue 2/2024

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Abstract

Abrasive belt condition (BC) monitoring is significant for achieving profile finishing precision and quality in grinding of difficult-to-machine materials like Inconel 718. While indirect signal-based BC monitoring methods are ineffective when varying grinding parameters, existing image-based direct monitoring methods currently suffer from a lack of: (i) a unified and quantitative definition of the belt condition; (ii) in situ tool-surface image capture and relevant feature extraction; and (iii) continuous monitoring of the entire belt conditions. This paper proposes a partitioned BC monitoring method that is adaptable to ever-changing grinding conditions. Based on the belt surface analysis, a unified BC coefficient is quantitatively defined by using two critical BC-dependent features, the average area and number of worn flats of abrasive grains per unit area. The belt surface image is in-situ captured from moving belts and is preprocessed to eliminate image defects in a unified form, then the entire belt is partitioned, and finally the image features are extracted by Gabor filter and K-means clustering. The proposed robust method which has a maximum relative repeatability error of 9.33%, and less computation was validated by the experimental results. This study provides an adaptable and efficient way for continuously monitoring the conditions of the entire belt and the grinding area.

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Title: Partitioned abrasive belt condition monitoring based on a unified coefficient and image processing
Authors: Xiaokang Huang
Xukai Ren
Huanwei Yu
Xiyong Du
Xianfeng Chen
Ze Chai
Xiaoqi Chen
Publication date: 15-02-2023
Publisher: Springer US
Published in: Journal of Intelligent Manufacturing / Issue 2/2024
Print ISSN: 0956-5515
Electronic ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-023-02083-7

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