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

Xiaokang Huang, Xukai Ren, Huanwei Yu, Xiyong Du, Xianfeng Chen, Ze Chai () and Xiaoqi Chen ()
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Xiaokang Huang: Shanghai Jiao Tong University
Xukai Ren: Shaoxing Key Laboratory of Special Equipment Intelligent Testing and Evaluation
Huanwei Yu: Shaoxing Key Laboratory of Special Equipment Intelligent Testing and Evaluation
Xiyong Du: Shaoxing Key Laboratory of Special Equipment Intelligent Testing and Evaluation
Xianfeng Chen: Shaoxing Key Laboratory of Special Equipment Intelligent Testing and Evaluation
Ze Chai: Shanghai Jiao Tong University
Xiaoqi Chen: Shanghai Jiao Tong University

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 2, No 25, 905-923

Abstract: 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.

Keywords: Abrasive belt grinding; Belt condition monitoring; Unified belt condition coefficient; Partitioned feature extraction; Image processing (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02083-7

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