This research introduces a novel approach to optimization and tolerance design, leveraging Large Language Models (LLMs) to bypass traditional methods like Taguchi's orthogonal arrays and SN ratios. The methodology, demonstrated through a "ramen optimization" case study (SIWC25), involves structuring language-based information, such as online reviews, into a quantifiable objective (e.g., a "QEU score"). LLMs then generate optimized designs by following specific prompts, effectively exploring a solution space without extensive experimentation. This framework has been extended to optimize physical products like folding umbrellas and social systems like community spaces, significantly reducing costs and development time compared to conventional techniques. AI
IMPACT LLMs are shown to significantly reduce the cost and time for optimization and design processes, potentially accelerating product development and innovation across various industries.
RANK_REASON The cluster describes a novel research methodology using LLMs for optimization and tolerance design, presented in a technical paper format.
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