Resistance spot welding (RSW) is a widely used welding technology in automotive manufacturing, and weld nugget quality is closely related to the quality of the vehicle body. Offline random checks are largely relied on the quality inspection of weld nuggets, but they have low efficiency and high cost. To address this issue, this paper proposes a deep learning model for RSW weld nugget classification, named the offset-transformer hierarchical model (OFTFHC), which is based on the point cloud data of its appearance shape. OFTFHC uses a hierarchical network structure to gradually expand the receptive field. A local feature module is introduced to extract local features from the point cloud, effectively enabling the recognition of the fine structural features of the resistance spot weld point cloud. A residual ratio module, which is based on and uses max and average functions for feature enhancement, is designed to adapt to the complex spatial structure of the point cloud. The offset-transformer structure is used to learn global context features, thereby enhancing the global feature extraction capability. Through classification experiments on RSW weld nuggets across 5 categories with a total of 1050 samples, OFTFHC achieved an average accuracy of 80.6 %, outperforming existing models. This demonstrates the effectiveness and superiority of the method, making it highly suitable for weld nugget quality control in automotive automation production lines.

中文翻译：

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基于点云的电阻点焊质量分类的偏置变压器分层模型


电阻点焊（RSW）是汽车制造中广泛应用的焊接技术，焊核质量与车身质量密切相关。离线抽查主要依靠焊核质量检验，但效率低、成本高。为了解决这个问题，本文提出了一种基于其外观形状的点云数据的RSW焊核分类深度学习模型，称为偏移变换器分层模型（OFTFHC）。 OFTFHC采用分层网络结构逐步扩大感受野。引入局部特征模块从点云中提取局部特征，有效实现电阻点焊点云精细结构特征的识别。残差率模块基于并使用最大和平均函数进行特征增强，旨在适应点云复杂的空间结构。使用offset-transformer结构来学习全局上下文特征，从而增强全局特征提取能力。通过对 5 个类别、总共 1050 个样本的 RSW 焊核进行分类实验，OFTFHC 的平均准确率达到 80.6%，优于现有模型。这证明了该方法的有效性和优越性，使其非常适合汽车自动化生产线的焊核质量控制。