3D point cloud analysis for surface quality inspection: A steel parts use case

Ntoulmperis, Michalis; Catti, Paolo; Discepolo, Silvia; Kamp, Wilhelm van de; Castellini, Paolo; Nikolakis, Nikolaos; Alexopoulos, Kosmas

doi:10.1016/j.procir.2024.01.074

Procedia CIRP

2024

DOI: 10.1016/j.procir.2024.01.074

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3D point cloud analysis for surface quality inspection: A steel parts use case

Michalis Ntoulmperis,

Paolo Catti,

Silvia Discepolo

et al.

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Research on the Wear State Detection and Identification Method of Huller Rollers Based on Point Cloud Data

Wu,

Jin,

Liu

et al. 2024

Coatings

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Throughout the huller shelling process, the rubber rollers progressively deteriorate. The velocity of the rubber rollers decreases as the distance between the rollers rises. These modifications significantly influence the rate at which rice hulling occurs. Hence, the implementation of real-time online detection is crucial for maintaining the operational efficiency of the huller. Currently, the prevailing inspection methods include manual inspection, 2D vision inspection, deep learning methods, and machine vision methods. Nevertheless, these conventional techniques lack the ability to provide detailed information about the faulty components, making it challenging to conduct comprehensive defect identification in three dimensions. To address this issue, point cloud technology has been incorporated into the overall detection of the working condition of the huller. Specifically, the Random Sample Consensus segmentation algorithm and the adaptive boundary extraction algorithm have been developed to identify abnormal wear on the rubber rollers by analyzing the point cloud data on their surface. A solution technique has been developed for the huller to compensate for the speed of the rubber rollers and calculate the mean values of their radii. Additionally, a numerical simulation algorithm is proposed to address the dynamic change in the roller spacing detection. The results show that point cloud data can be utilized to achieve real-time and precise correction of anomalous wear patterns on the surface of rubber rollers.

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Research on the Wear State Detection and Identification Method of Huller Rollers Based on Point Cloud Data

Wu,

Jin,

Liu

et al. 2024

Coatings

View full text Add to dashboard Cite

show abstract

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3D point cloud analysis for surface quality inspection: A steel parts use case

Cited by 1 publication

References 20 publications

Research on the Wear State Detection and Identification Method of Huller Rollers Based on Point Cloud Data

Research on the Wear State Detection and Identification Method of Huller Rollers Based on Point Cloud Data

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