SFRGNN-DA: An enhanced graph neural network with domain adaptation for feature recognition in structural parts machining

Abstract

Optimizing the recognition of machining features in structural parts is vital for enhancing the efficiency of NC machining planning and ensuring quality control. However, the inherent complexity and stringent precision requirements of these parts often render existing feature recognition methods inadequate for accurately identifying model features. To address this challenge, a novel graph neural network model (SFRGNN) is introduced. The methodology begins with a specialized feature extraction module that captures both geometric and topological properties of the parts, providing a comprehensive basis for further analysis. Following this, SFRGNN integrates a graph neural network with a Spatial Self-Attention (SSA) module, a configuration designed to enhance the extraction of high-level semantic information crucial for accurately distinguishing machining features. This network architecture allows SFRGNN to interpret complex feature relationships with improved precision. Additionally, an enhanced domain adaptation module (DA) is incorporated to improve SFRGNN’s generalization capabilities and performance in machining feature recognition. Numerous experiments on different data sets confirmed that SFRGNN achieved excellent accuracy in identifying real-world structural part features and demonstrated enhanced performance, which will be helpful for subsequent process planning for part features in real-world scenarios.