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<p>Aiming at the current problems of low accuracy and insufficient intelligence in the insulation layer inspection of large free-form workpieces, this paper ingeniously proposes an analysis method for insulation layer quality detection based on digital twin model. Specifically, the core of the proposed method is divided into two stages. First, the improved PointNet +  + algorithm is utilized to segment the point cloud model of the workpiece at different process periods to obtain the point cloud data of its constant region. Secondly, the Principal Component Analysis (PCA) and K-Dimensional Tree Iterative Closest Point (KD-ICP) algorithms are utilized to obtain the rigid body transformation matrix, and the rigid body transformation matrix is used to complete the overall point cloud registration of the workpiece. Finally, the method of this paper is experimentally validated and achieves the detection analysis of the quality of the insulation layer.</p>

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A novel quality inspection method based on free-form surface digital twin model

  • 期刊论文

  • 发表日期：

    2025年3月24日

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  • Tian-Feng Qi,
  • Hai-Rong Fang,
  • Yu-Lin Liu,
  • Yu-Fei Chen,
  • Hao-Qian Wang,
  • Yu-Fan He

摘要

Aiming at the current problems of low accuracy and insufficient intelligence in the insulation layer inspection of large free-form workpieces, this paper ingeniously proposes an analysis method for insulation layer quality detection based on digital twin model. Specifically, the core of the proposed method is divided into two stages. First, the improved PointNet +  + algorithm is utilized to segment the point cloud model of the workpiece at different process periods to obtain the point cloud data of its constant region. Secondly, the Principal Component Analysis (PCA) and K-Dimensional Tree Iterative Closest Point (KD-ICP) algorithms are utilized to obtain the rigid body transformation matrix, and the rigid body transformation matrix is used to complete the overall point cloud registration of the workpiece. Finally, the method of this paper is experimentally validated and achieves the detection analysis of the quality of the insulation layer.