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Optical character recognition on engineering drawings to achieve automation in production quality control
Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering. (Design Automation Laboratory)ORCID iD: 0000-0002-5950-4962
Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7210-0209
Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
2023 (English)In: Frontiers in Manufacturing Technology, E-ISSN 2813-0359, Vol. 3Article in journal (Refereed) Published
Abstract [en]

Introduction: Digitization is a crucial step towards achieving automation in production quality control for mechanical products. Engineering drawings are essential carriers of information for production, but their complexity poses a challenge for computer vision. To enable automated quality control, seamless data transfer between analog drawings and CAD/CAM software is necessary.

Methods: This paper focuses on autonomous text detection and recognition in engineering drawings. The methodology is divided into five stages. First, image processing techniques are used to classify and identify key elements in the drawing. The output is divided into three elements: information blocks and tables, feature control frames, and the rest of the image. For each element, an OCR pipeline is proposed. The last stage is output generation of the information in table format.

Results: The proposed tool, called eDOCr, achieved a precision and recall of 90% in detection, an F1-score of 94% in recognition, and a character error rate of 8%. The tool enables seamless integration between engineering drawings and quality control.

Discussion: Most OCR algorithms have limitations when applied to mechanical drawings due to their inherent complexity, including measurements, orientation, tolerances, and special symbols such as geometric dimensioning and tolerancing (GD&T). The eDOCr tool overcomes these limitations and provides a solution for automated quality control.

Conclusion: The eDOCr tool provides an effective solution for automated text detection and recognition in engineering drawings. The tool's success demonstrates that automated quality control for mechanical products can be achieved through digitization. The tool is shared with the research community through Github.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2023. Vol. 3
Keywords [en]
optical character recognition, image segmentation, object detection, engineering drawings, quality control, keras-ocr
National Category
Engineering and Technology Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:liu:diva-195416DOI: 10.3389/fmtec.2023.1154132OAI: oai:DiVA.org:liu-195416DiVA, id: diva2:1770999
Funder
Vinnova, 2021-02481Available from: 2023-06-20 Created: 2023-06-20 Last updated: 2023-12-13Bibliographically approved
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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Villena Toro, JavierWiberg, AntonTarkian, Mehdi

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