Enhancing FEA Preprocessing Efficiency through a Multi AI Model Strategy

Finite Element Analysis (FEA) plays a vital role in accelerating product development and reducing overall engineering costs. Accurate simulation of powertrain components requires extensive preprocessing of solid models. Detailed contact definitions and bolt representations are required to properly evaluate screw joints. Pipe joints present similar challenges, as they often incorporate various types of nuts and connectors. As a result, engineers spend a significant amount of time manually modelling these joints. Automating the modelling process is difficult due to the high variability in bolt sizes and pipe joint designs. This paper proposes AI-based models to automatically classify these components and extract their distinctive geometric features. Because bolts and pipe joints have wide feature variations, developing a generalized AI model requires a large dataset which is often hard to obtain. To address this limitation, this paper explores strategies for generating smaller, specialized AI models tailored to each unique joint type. This study describes the development of deep learning neural network models capable of learning distinguishing features of different joint configurations from limited training data. A multilayer graphical neural network architecture is employed, enabling efficient extraction and representation of these unique geometric characteristics. The proposed models can robustly generalize different joint types while maintaining high classification accuracy and reliable feature detection despite constrained training data availability.

Automated workflows have been implemented in commercially available preprocessing tools to validate the proposed AI methodology and demonstrate its effectiveness in practical engineering applications. The integration of AI model automation in preprocessing tools significantly reduces engineering effort and repetitive manual preprocessing steps. This work highlights the potential of hybrid AI models to accelerate CAE workflows and provides scalable framework for future automation in FEA.