Large language models (LLMs) such as the Generative Pre-Trained Transformers (GPT) use transformer models and are pre-trained using massive cross-domain datasets. LLMs can perform a variety of natural language processing (NLP) and natural language generation (NLG) tasks. The fast-evolving LLMs have shown revolutionary potential in many research fields. Design and manufacturing represent critical research domains where knowledge has always been deeply embedded in engineering designers, manufacturing engineers, and technicians.
Questions such as what types of and how much information and knowledge LLMs can extract from engineering documents and publications; how LLMs can help design concept generation and engineering problem solving; what the best techniques and practices are to adapt LLMs in design and manufacturing are urgent for the research community to answer. This special issue will bring together state-of-the-art research to this exciting topic.
THE SCOPE OF THIS ISSUE INCLUDES BUT IS NOT LIMITED TO:
- Multimodal LLMs for design and manufacturing
- Data curation/cleaning/analysis/ visualization using LLMs
- Information and knowledge extraction and management from engineering documents using LLMs
- Investigate and improve trust and fairness in human-AI collaboration based on LLMs
- Adapting language and vision transformers (ViTs) to design and manufacturing such as domain (design and manufacturing)-guided architectural innovations/ modifications of LLM and ViTs.
- Overcoming challenges when fine-tuning and deploying LLMs for engineering applications
- Investigate how LLMs can surface embedded, tacit knowledge from technical manuals and experts (expertise modelling)
- Employing LLMs in the context of specific design tasks to offer relevant suggestions or automation (context-aware design assistance)
- Research on how LLMs can aid in initial stages of design by generating conceptual sketches or proposals as well as how LLMs can support sustainable and environmentally friendly design practices
- Developing standardized tests or benchmarks for assessing the effectiveness of LLMs in design and manufacturing settings
Special Issue Publication Dates
Paper submission deadline: April 1, 2024
Initial review completed: June 1, 2024
Publication date: February 2025
Papers should be submitted electronically to the journal through the ASME Journal Tool. If you already have an account, log in as an author and select Submit Paper. If you do not have an account, you can create one here. Once at the Paper Submittal page, select the Journal of Computing and Information Science in Engineering, and then select the Special Issue on Large Language Models in Design and Manufacturing.
Papers received after April 1, 2024, may still be considered for the special issue, if time and space permits. Early submissions to this special issue once accepted will be published online first. Authors are encouraged to provide a GitHub link of their machine learning model code and training dataset to guarantee the reproducibility of the reported machine learning approaches.
Yaoyao Fiona Zhao, McGill University, Canada (firstname.lastname@example.org)
Evangelos Niforatos, Delft University of Technology, The Netherlands (email@example.com)
Tonya Custis, Autodesk Research, USA (firstname.lastname@example.org)
Yan Lu, National Institute of Standards and Technology, USA (email@example.com)
Jianxi Luo, Singapore University of Technology and Design, Singapore (firstname.lastname@example.org)