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Text-to-image or 3D content generation has attracted attention in academia and industry because of its impressive performance in high-fidelity content generation with natural language commands. However, its widespread application in industrial computation-aided design (CAD) is not yet found. The main issue lies in the end-to-end generation through implicit probabilistic learning from datasets. This presents difficulties in precise, controllable, and transparent creations. Also, it is time-consuming and labor-intensive for designers to verify and customize the generated result to fit their demands. Furthermore, popular generative models offer limited support for early-stage engineering computations, leaving tedious and massive calculations to human engineers. To overcome these challenges, this paper proposes a method to apply large language model (LLM) to assist standard CAD design workflow on both engineering computation and precise 3D geometry generation. During the designing process, LLM can support the computation process with human guidance and generate accurate 3D shapes with the capability of sequential code representation and transformation. To verify the feasibility of the proposed method, a use case for a one-stage reduction gear system consisting of two gears will be given as a proof-of-concept demonstration. The result shows that the LLM can carry out the computation and get the result as expected with only a few lines of human guidance and feedback. Then, it can create CAD models with accurate parameters aligned to the computational results. It can cope with various requirements given by users and allow users to further modify the generation using the same CAD environment without much effort. Authors believe that this piece of work can shed light on utilizing the capability of LLMs for a certain part of task automation based on human beings’ intentions. With the rise of LLMs and relevant technologies, conventional CAD may shift to a more user-friendly AI-aided design (AIAD) diagram.
Deng et al. (Mon,) studied this question.