Toward fully automated CAD–CAE integration through design feature recognition and small language models

Abstract Automated CAD–CAE integration, which links computer-aided design (CAD) and computer-aided engineering (CAE), is one of the key strategies for reducing repetitive tasks in the design process. However, setting analysis parameters and assigning boundary conditions for variously designed shapes require expert knowledge and precise recognition of topological elements, remaining a major challenge to full automation. To address these issues, this study proposes an automated integration framework. The framework utilizes a small language model (SLM) and prompt engineering to extract analysis and validation parameters from unstructured documents. Additionally, boundary faces are selected through design feature recognition. The parameter extraction performance was evaluated using four language models developed by different providers and trained at different scales. The evaluation showed consistent extraction accuracy across all models. Boundary condition assignment was verified using CAD models including cup-anemometer and snap-fit hook features, achieving 100% accuracy in all experiments. The effectiveness of automation was assessed by comparing the number of manual inputs and the total analysis time. The experimental results showed reductions of 97.06% in manual inputs and 63.92% in analysis time per CAD model. These experimental results demonstrate the effectiveness of a framework that enables the entire analysis process to be executed without human intervention. The study provides a practical solution to the challenges of parameter setting and boundary condition assignment. Furthermore, the proposed method presents an innovative approach that can be generally applied to performance analysis of complex and diverse CAD models and experimentally verifies the benefits achievable through full automation.