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Model Architecture Exploration Using Chatgpt for Specific Manufacturing Applications
Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-5950-4962
Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
2023 (English)In: ASME IDETC-CIE, 2023, Vol. 2Conference paper, Published paper (Refereed)
Abstract [en]

Selecting an appropriate machine learning model architecture for manufacturing tasks requires expertise in both computer science and manufacturing. However, integrating state-of-the-art machine learning models and manufacturing processes is often challenging due to the distance between these fields. OpenAI’s popular language model, ChatGPT, has the potential to bridge this gap.

This paper proposes guidelines and questions to explore model architecture options and extract valuable information from ChatGPT’s natural language processing capabilities. While ChatGPT is a powerful tool, it is important to verify any answers obtained against reliable sources before making any decisions. The guidelines compose a flowchart with four queries to give ChatGPT enough context and exisiting input data information. ChatGPT suggestions will be directed towards input processing, output, and architecture proposals. The last query produces keywords based on the chat for a background study on the topic.

A manufacturing case study was conducted to demonstrate the effectiveness of these guidelines. The study involved creating a model to forecast fixturing locations for welding processes in the automotive sector. After conducting four separate interviews with ChatGPT, the authors discuss the selection of architecture based on ChatGPT suggestions and contrast it with previous literature.

The proposed guidelines are expected to be useful in a variety of manufacturing contexts, as they offer a structured approach to exploring model architecture options using ChatGPT’s capabilities, ultimately leading to new and innovative applications of machine learning in this field.

Place, publisher, year, edition, pages
2023. Vol. 2
Keywords [en]
fixture layout, machine learning, ChatGPT, manufacturing planning, model exploration
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:liu:diva-199611DOI: 10.1115/DETC2023-116483ISBN: 978-0-7918-8729-5 (print)OAI: oai:DiVA.org:liu-199611DiVA, id: diva2:1819135
Conference
IDETC-CIE 43rd Computers and Information in Engineering
Funder
Vinnova, 2020-02974Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-13
In thesis
1. Machine Learning In Design Engineering and Manufacturing
Open this publication in new window or tab >>Machine Learning In Design Engineering and Manufacturing
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Artificial intelligence (AI) has made significant strides in various fields, challenging conventional notions of computer capabilities. However, while data science research primarily concentrates on refining AI models, there are numerous challenges associated with integrating AI into industrial applications.

Knowledge-Based Engineering, with its potential to streamline the production cycle by reusing engineering knowledge and intent, emerges as a promising avenue for AI in the industry. When engineering knowledge is effectively processed and categorized, neural networks naturally emerge as potent tools for automation.

This thesis presents three case studies that demonstrate the practicality of supervised learning, particularly in the domain of neural networks, to address manufacturing automation challenges. These case studies span various stages of the manufacturing system, encompassing engineering design, production planning, and quality control phases. The first application employs supervised learning to automate the generation of engineering drawings, while the third employs optical character recognition to expedite the quality control process for complex engineering drawings. The second application centers on the estimation of fixturing clamps for welding operations in automobile parts.

In summary, this thesis strives to make a meaningful contribution to the field of design engineering and manufacturing by examining the potential of AI in enhancing processes and addressing automation hurdles. By presenting case studies that showcase the utility of machine learning models in production settings, this thesis aims to stimulate further research in this evolving field.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2023. p. 48
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1979
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-199612 (URN)10.3384/9789180754569 (DOI)9789180754552 (ISBN)9789180754569 (ISBN)
Presentation
2023-12-15, ACAS, Linköping University, IEI, A-building, Linköping, 10:15 (English)
Opponent
Supervisors
Funder
Vinnova, 2020-02974Vinnova, 2021-02481
Available from: 2023-12-15 Created: 2023-12-13 Last updated: 2023-12-15Bibliographically approved

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Publisher's full texthttps://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2023/87295/V002T02A091/1170444

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Villena Toro, JavierTarkian, Mehdi

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