A Domain Adaptation of Large Language Models for Classifying Mechanical Assembly Components

TL;DR

This paper introduces a domain adaptation framework using fine-tuned large language models, specifically GPT-3.5 Turbo, to improve the classification of mechanical assembly functions, aiding early design decision-making.

Contribution

It presents a novel approach of fine-tuning LLMs for domain-specific functional classification in mechanical design, enhancing accuracy and consistency over manual methods.

Findings

Fine-tuned GPT-3.5 Turbo achieves high-quality functional data classification.

Domain adaptation improves semantic representation of mechanical parts.

Enhanced functional data supports better early-phase design exploration.

Abstract

The conceptual design phase represents a critical early stage in the product development process, where designers generate potential solutions that meet predefined design specifications based on functional requirements. Functional modeling, a foundational aspect of this phase, enables designers to reason about product functions before specific structural details are determined. A widely adopted approach to functional modeling is the Function-Behavior-Structure (FBS) framework, which supports the transformation of functional intent into behavioral and structural descriptions. However, the effectiveness of function-based design is often hindered by the lack of well-structured and comprehensive functional data. This scarcity can negatively impact early design decision-making and hinder the development of accurate behavioral models. Recent advances in Large Language Models (LLMs), such as those based on GPT architectures, offer a promising avenue to address this gap. LLMs have demonstrated significant capabilities in language understanding and natural language processing (NLP), making them suitable for automated classification tasks. This study proposes a novel LLM-based domain adaptation (DA) framework using fine-tuning for the automated classification of mechanical assembly parts' functions. By fine-tuning LLMs on domain-specific datasets, the traditionally manual and subjective process of function annotation can be improved in both accuracy and consistency. A case study demonstrates fine-tuning GPT-3.5 Turbo on data from the Oregon State Design Repository (OSDR), and evaluation on the A Big CAD (ABC) dataset shows that the domain-adapted LLM can generate high-quality functional data, enhancing the semantic representation of mechanical parts and supporting more effective design exploration in early-phase engineering.