1D Segmentation Network for 3D Seam Weld Grinding. Issue 1 (May 2021)

• Record Type: Journal Article
• Authors: Dong, Chunyu; Shi, Tiankai; Zhao, Qunfei; Huang, Xiaolin; Liu, Chao
• Is Part Of: Journal of physics. Volume 1924:Issue 1(2021)
• Publication Date: 2021-05
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1924/1/012002
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: With the development of deep learning, convolutional neural networks provide new solutions for obstacles in the industry. For seam weld grinding, traditional algorithms are neither convenient nor efficient. Meanwhile, previous methods are not robust for various shapes of the weld seam. In this paper, we propose a new algorithm based 1D convolutional neural network for 3D weld seam grinding. We test different loss functions for the 1D segmentation network and picked the best one for model training. Besides, we design various feature extracting blocks and make extensive experiments on the cloud point data set of the weld seam. The best combination of the loss function and feature extractor is generated for weld seam prediction. With the open operation on the 3D map and the elimination of abnormal points, we obtain a robust prediction grinding trail for the robot controller.