In the realm of industrial quality control, visual inspection plays a pivotal role in ensuring product precision and consistency. Moreover, it enables non-contact inspection, preventing the products from potential damage, and timely monitoring capabilities facilitate quick decision making. However, traditional methods, such as manual inspection using feeler gauges, are time-consuming, labor-intensive, and prone to human error. To address these limitations, this study proposes a deep learning-based visual inspection system for measuring gap spacing in high-precision equipment. Utilizing the DeepLSD algorithm, the system integrates traditional and deep learning techniques to enhance line segment detection, resulting in more robust and accurate inspection outcomes. Key performance improvements were realized, with the proposed system being a piece of deep learning-enabled high-precision mobile equipment for inspecting gap spacing in real-time. Through a comparative analysis with the traditional feeler gauge method, the proposed system demonstrated significant improvements in inspection time, accuracy, and user experience, while reducing workload. Experimental results validate the effectiveness and efficiency of the proposed approach, highlighting its potential for widespread application in industrial quality inspection activities.

Problems such as unreasonable processability or model defects generated in the design stage will lead to continuous rework during the manufacturing process, which greatly increases the manufacturing cost of the product. Through manufacturability analysis, the process designer can find design defects that are difficult to manufacture, impossible to manufacture, or have high manufacturing costs as early as possible, so as to reduce the number of round trips between design and process, and shorten the product development cycle. However, it is difficult for the current rule-based manufacturability analysis method to obtain professional knowledge and construct a complete manufacturability analysis rule repository. Therefore, a manufacturability analysis method based on a graph neural network is proposed. First, the attribute adjacency graph and UV gridding are combined to characterize the part model data, which can effectively represent the topological information and geometric information on the part model. At the same time, parameter information on the spherical coordinate system is used to ensure rotation and translation invariance; then, based on the graph representation of the part model, a hierarchical graph neural network is constructed, which is divided into three levels, edge, node, and graph, for encoding, information transmission and updating, and expanding the receptive field for better node classification to support manufacturability analysis. Finally, graph contrastive learning is used as a regularization technique in the pre-training stage to maximize the similarity of graph representations between different views to improve prediction performance. Manufacturability analysis tests were carried out on the constructed part model dataset, and the experimental results showed that the method has good performance.