<p>In the process of intelligent manufacturing transformation driven by Industry 4.0, digital factories have gradually become the core carrier. As a typical application scenario of intelligent manufacturing, automotive welding assembly faces a technical bottleneck in constructing digital factories: the high-precision spatial positioning of process equipment. Welding scenarios are characterized by high complexity, a large number of process equipment, and dense layouts, making it difficult to obtain precise positioning information of key equipment such as manipulators through conventional methods. To address this issue, this paper proposes a 3D point cloud detection network for welding scenarios to achieve precise positioning of process equipment. We design a color feature extraction module to address the issue of weakened model generalization resulting from differences in painting among the process equipment of different types of robots. Meanwhile, an attention mechanism module based on shape-aware features is improved to enhance the capture of key structural features of process equipment. We also collected point cloud data from the real welding production line of an automobile brand, established a dataset for training and comprehensive verification, and carried out further experiments on the public dataset S3DIS to validate the effectiveness of the method. Experimental results show that the proposed method achieved mean average precision (mAP) scores of 89.5% and 53.1% on the welding point cloud dataset. Compared with the baseline model, mAP under different thresholds increased by 5.3 and 3 percentage points respectively, providing a reliable perceptual foundation for the real-to-virtual mapping of intelligent factories.</p>