What’s in a Name? Evaluating Assembly-Part Semantic Knowledge in Language Models Through User-Provided Names in Computer Aided Design Files

Meltzer, Peter C.; Lambourne, Joseph G.; Grandi, Daniele

doi:10.1115/1.4062454

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…However, no example can be found in cases where multiple system architectures are explored and analysed at the same time (e.g., Suh et al, 2010;Panarotto et al, 2022). Another area which has recently been tackled by LLM-focused research has been CAD and geometry information (e.g., Meltzer et al, 2024). However, current applications has focused on how to improve the searchability of CAD filed in PDM repositories through LLMs.…”

Section: Current Applications Of Llms In Design Activitiesmentioning

confidence: 99%

Large language models in complex system design

Gomez,

Krus,

Panarotto

et al. 2024

Proc. Des. Soc.

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This paper investigates the use of Large Language Models (LLMs) in engineering complex systems, demonstrating how they can support designers on detail design phases. Two aerospace cases, a system architecture definition and a CAD model generation activities are studied. The research reveals LLMs' challenges and opportunities to support designers, and future research areas to further improve their application in engineering tasks. It emphasizes the new paradigm of LLMs support compared to traditional Machine Learning techniques, as they can successfully perform tasks with just a few examples.

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Section: Current Applications Of Llms In Design Activitiesmentioning

confidence: 99%

Large language models in complex system design

Gomez,

Krus,

Panarotto

et al. 2024

Proc. Des. Soc.

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Multi-Modal Machine Learning in Engineering Design: A Review and Future Directions

Song,

Zhou,

Ahmed

2023

Journal of Computing and Information Science in Engineering

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In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of multiple data modalities has the potential to reshape various applications. This paper presents a comprehensive overview of the current state, advancements, and challenges of MMML within the sphere of engineering design. The review begins with a deep dive into five fundamental concepts of MMML:multi-modal information representation, fusion, alignment, translation, and co-learning. Following this, we explore the cutting-edge applications of MMML, placing a particular emphasis on tasks pertinent to engineering design, such as cross-modal synthesis, multi-modal prediction, and cross-modal information retrieval. Through this comprehensive overview, we highlight the inherent challenges in adopting MMML in engineering design, and proffer potential directions for future research. To spur on the continued evolution of MMML in engineering design, we advocate for concentrated efforts to construct extensive multi-modal design datasets, develop effective data-driven MMML techniques tailored to design applications, and enhance the scalability and interpretability of MMML models. MMML models, as the next generation of intelligent design tools, hold a promising future to impact how products are designed.

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Evaluating Large Language Models for Material Selection

Grandi,

Jain,

Groom

et al. 2024

Journal of Computing and Information Science in Engineering

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Material selection is a crucial step in conceptual design due to its significant impact on the functionality, aesthetics, manufacturability, and sustainability impact of the final product. This study investigates the use of Large Language Models (LLMs) for material selection in the product design process and compares the performance of LLMs against expert choices for various design scenarios. By collecting a dataset of expert material preferences, the study provides a basis for evaluating how well LLMs can align with expert recommendations through prompt engineering and hyperparameter tuning. The divergence between LLM and expert recommendations is measured across different model configurations, prompt strategies, and temperature settings. This approach allows for a detailed analysis of factors influencing the LLMs' effectiveness in recommending materials. The results from this study highlight two failure modes, and identify parallel prompting as a useful prompt-engineering method when using LLMs for material selection. The findings further suggest that, while LLMs can provide valuable assistance, their recommendations often vary significantly from those of human experts. This discrepancy underscores the need for further research into how LLMs can be better tailored to replicate expert decision-making in material selection. This work contributes to the growing body of knowledge on how LLMs can be integrated into the design process, offering insights into their current limitations and potential for future improvements.

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What’s in a Name? Evaluating Assembly-Part Semantic Knowledge in Language Models Through User-Provided Names in Computer Aided Design Files

Cited by 4 publications

References 23 publications

Large language models in complex system design

Large language models in complex system design

Multi-Modal Machine Learning in Engineering Design: A Review and Future Directions

Evaluating Large Language Models for Material Selection

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