MC Classifier: A Classifier for 3D Mechanical Components Based on Geometric Prior Using Graph Neural Network and Attention
Source abstract
Abstract
With an increasing number of mechanical components produced in the production pipeline, the need to classify past and new elements efficiently has been increasing. However, past methods of classifying elements have relied on traditional methods that take a long time and lack extensibility. By incorporating specific geometric local features, graph neural networks, and attention between point clouds to include local and global features, we propose a new framework called Mechanical Component Classifier (MC Classifier), which can classify components efficiently with a fast inference time and can be easily extended to classify other elements. We benchmark the performance of MC Classifier against state-of-the-art models and demonstrate its competitive potential in 3D mechanical component classification. Our findings suggest that MC Classifier has significant potential to advance 3D mechanical component classification. Our findings show that MC Classifier has significant potential to advance 3D mechanical component classification and efficient and affordable methods to streamline industrial pipelines.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Manufacturing Process and Optimization · Industrial Vision Systems and Defect Detection · Image Processing and 3D Reconstruction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94704, USA
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Funding
National Natural Science Foundation of China
References (38)
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