A Robust 3D Fixed-Area Quality Inspection Framework for Production Lines

Introducing deep learning methods into the quality inspection of production lines can reduce labor and improve efficiency, with great potential for the development of manufacturing systems. However, in specific closed production-line environments, robust and high-quality 3D fixed-area quality inspection is a common and challenging problem due to improper assembly, high data resolution, pose perturbation, and other reasons. In this article, we propose a robust 3D fixed-area quality inspection framework for production lines consisting of two steps: recursive segmentation and one-class classification. First, a Focal Segmentation Module (FSM) is proposed to gradually focus on the areas to be inspected by recursively segmenting the downsampled low-resolution point cloud, thereby ensuring efficient high-resolution segmentation. Moreover, Local Reference Frame (LRF)-based rotation-invariant local feature extraction is introduced to improve the robustness of the proposed method to pose variations. Second, a uniquely designed Semi-Nested Point Cloud Autoencoder (SN-PAE) is proposed to improve data imbalance and hard-to-classify samples. Particularly, we first introduce rotation-invariant feature extraction to a point cloud autoencoder to learn descriptive latent variables, then measure the latent variables using a semi-nested Latent Autoencoding Module (LAM). This avoids unreliable chamfer distance measurement and makes SN-PAE a more robust measurement method. In addition, we implement a set of experiments using solder joints as an example. Compared with PointNet++, the memory usage of recursive segmentation is reduced by 92%, and the time cost is reduced by 97.5%. The recall of SN-PAE on unaligned samples exceeds that of competitors by nearly 30% in the classification stage. The results demonstrate the feasibility and effectiveness of the proposed framework.