Research on Surface Wear Detection Method for Hot Rolling Work Roll Based on Texture Features and Ensemble Learning

doi:10.3901/JME.260178

Abstract

Abstract: Precise determination of hot rolling work roll surface wear conditions is critical for optimizing mill performance. To address the imaging challenges posed by large-sized roll surfaces, a vision acquisition system integrating dual line-scan cameras with customized linear light sources was designed, allowing roll surface images to be reliably captured during the roll changing process. A template matching algorithm based on Constrained Dynamic Time Warping is proposed to achieve automated image acquisition. Wear conditions are effectively characterized by a four-dimensional Tamura texture feature vector consisting of coarseness, contrast, linearity, and regularity. Furthermore, the classification of typical wear morphologies is achieved with 95% accuracy by employing an extremely randomized trees ensemble learning model. Finally, an intelligent roll surface wear detection platform was developed, providing a novel approach for the intelligent maintenance and process optimization of hot rolling work rolls.

Key words: hot rolling work rolls, surface wear assessment, machine vision, texture feature extraction, extreme random trees

CLC Number:

TG335

HU Qiwei, SUN Yongji, ZHANG Jianchao, REN Xinyi, GAO Huimin, JI Ce, HUANG Huagui. Research on Surface Wear Detection Method for Hot Rolling Work Roll Based on Texture Features and Ensemble Learning[J]. Journal of Mechanical Engineering, 2025, 62(6): 111-120.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260178

http://www.cjmenet.com.cn/EN/Y2025/V62/I6/111

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