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Automated and Customized CAD Drawings by Utilizing Machine Learning Algorithms: A Case Study
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.
2022 (English)In: ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering ConferenceAugust 14–17, 2022St. Louis, Missouri, USA: Volume 3B: 48th Design Automation Conference (DAC), St. Louis, MO, USA, 2022, Vol. BConference paper, Published paper (Refereed)
Abstract [en]

This paper describes a methodology for automation of measurements in Computer-Aided Design (CAD) software by enabling the use of supervised learning algorithms. The paper presents a proof of concept of how dimensions are placed automatically in the drawing at predicted positions. The framework consists of two trained neural networks and a rule-based system. Four steps compound the methodology. 1. Create a data set of labeled images for training a pre-built convolutional neural network (YOLOv5) using CAD automatic procedures. 2. Train the model to make predictions on 2D drawing imagery, identifying their relevant features. 3. Reuse the information extracted from YOLOv5 in a new neural network to produce measurement data. The output of this model is a matrix containing measurement location and size data. 4. Convert the final data output into actual measurements of an unseen geometry using a rule-based system for automatic dimension generation. Although the rule-based system is highly dependent on the problem and the CAD software, both supervised learning models exhibit high performance and reusability. Future work aims to make the framework suitable for more complex products. The methodology presented is promising and shows potential for minimizing human resources in repetitive CAD work, particularly in the task of creating engineering drawings.

Place, publisher, year, edition, pages
St. Louis, MO, USA, 2022. Vol. B
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-196468DOI: 10.1115/DETC2022-88971ISBN: 978-0-7918-8623-6 (print)OAI: oai:DiVA.org:liu-196468DiVA, id: diva2:1786095
Conference
ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
Projects
iProdAvailable from: 2023-08-07 Created: 2023-08-07 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-CIE2022/86236/V03BT03A040/1150451

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Villena Toro, JavierTarkian, Mehdi

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