Work

MC Classifier: A Classifier for 3D Mechanical Components Based on Geometric Prior Using Graph Neural Network and Attention

Year: 2025

Type: article

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: Applied Sciences

DOI: 10.3390/app15084399

Authors: Zipeng Lin, Zhenguo Nie

Institutions: University of California, Berkeley, Tsinghua University

Language: English

Topic: 3D Shape Modeling and Analysis

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China