Performance Analysis of Graph Neural Network (GNN) for Manufacturing Feature Recognition Problem

This scientific paper presents a comprehensive performance analysis of Graph Neural Networks (GNNs) for the task of manufacturing feature recognition. The manufacturing industry heavily relies on accurate identification and classification of various features in order to ensure efficient production processes and quality control. Traditional methods for feature recognition often suffer from limitations in handling complex manufacturing datasets with intricate interdependencies. In this study, we investigate the effectiveness of GNNs in addressing these challenges by leveraging their ability to capture and model graph-structured data. We propose a novel framework that employs GNNs to recognize manufacturing features based on their spatial and relational characteristics. Extensive experiments are conducted using real-world manufacturing datasets, and the results demonstrate the superior performance of GNNs compared to traditional approaches. Furthermore, we analyze the impact of different GNN architectures, hyperparameters, and training strategies on the recognition accuracy and computational efficiency. Our findings shed light on the potential of GNNs as a powerful tool for manufacturing feature recognition, providing valuable insights for researchers and practitioners in the field.