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Application of optimized convolutional neural network to fixture layout in automotive parts
Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Produktrealisering. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-5950-4962
Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Produktrealisering. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Produktrealisering. Linköpings universitet, Tekniska fakulteten.
2023 (Engelska)Ingår i: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015Artikel i tidskrift (Refereegranskat) Epub ahead of print
Abstract [en]

Fixture layout is a complex task that significantly impacts manufacturing costs and requires the expertise of well-trained engineers. While most research approaches to automating the fixture layout process use optimization or rule-based frameworks, this paper presents a novel approach using supervised learning. The proposed framework replicates the 3-2-1 locating principle to layout fixtures for sheet metal designs. This principle ensures the correct fixing of an object by restricting its degrees of freedom. One main novelty of the proposed framework is the use of topographic maps generated from sheet metal design data as input for a convolutional neural network (CNN). These maps are created by projecting the geometry onto a plane and converting the Z coordinate into gray-scale pixel values. The framework is also novel in its ability to reuse knowledge about fixturing to lay out new workpieces and in its integration with a CAD environment as an add-in. The results of the hyperparameter-tuned CNN for regression show high accuracy and fast convergence, demonstrating the usability of the model for industrial applications. The framework was first tested using automotive b-pillar designs and was found to have high accuracy (approximate to 100%) in classifying these designs. The proposed framework offers a promising approach for automating the complex task of fixture layout in sheet metal design.

Ort, förlag, år, upplaga, sidor
SPRINGER LONDON LTD , 2023.
Nyckelord [en]
Design automation; Machine learning; Fixtures; CNN; Hyperparameter tuning; EfficientNet
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:liu:diva-192681DOI: 10.1007/s00170-023-10995-0ISI: 000938262100003OAI: oai:DiVA.org:liu-192681DiVA, id: diva2:1746597
Anmärkning

Funding Agencies|Linkping University; Vinnova-FFI (Fordonsstrategisk forskning ochinnovation) [2020-02974]

Tillgänglig från: 2023-03-29 Skapad: 2023-03-29 Senast uppdaterad: 2023-12-13
Ingår i avhandling
1. Machine Learning In Design Engineering and Manufacturing
Öppna denna publikation i ny flik eller fönster >>Machine Learning In Design Engineering and Manufacturing
2023 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2023. s. 48
Serie
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1979
Nationell ämneskategori
Produktionsteknik, arbetsvetenskap och ergonomi
Identifikatorer
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 (Engelska)
Opponent
Handledare
Forskningsfinansiär
Vinnova, 2020-02974Vinnova, 2021-02481
Tillgänglig från: 2023-12-15 Skapad: 2023-12-13 Senast uppdaterad: 2023-12-15Bibliografiskt granskad

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