Knowledge-based optimization of artificial neural network topology for additive manufacturing process modeling: a case study for fused deposition modeling: A new approach and case study for fused deposition modeling

Hari P. N. Nagarajan, Hossein Mokhtarian, Hesam Jafarian, Saoussen Dimassi, Shahriar Bakrani Balani, Azarakhsh Hamedi, Eric Coatanea, G. Gary Wang, Haapala Kari R.

    Research output: Contribution to journalArticleScientificpeer-review

    16 Citations (Scopus)

    Abstract

    Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling the different process variables in AM using modeling techniques, such as, machine learning, can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network models would aid designers and manufacturers to make informed decisions about their products and processes. However, accurately defining an artificial neural network topology is challenging due to the need to integrate AM system behavior during modeling. Towards that goal, an approach combining dimensional analysis conceptual modeling (DACM), experimental sampling, factors selection, and modeling based on Knowledge-Based Artificial Neural Network (KB-ANN) is proposed. This approach integrates existing literature and expert knowledge of the AM process to implement system behavior centered topology optimization of the knowledge-based artificial neural network model. The usefulness of the method is demonstrated using a case study to model wall thickness, height of part, and total mass of the part in a Fused Deposition Modeling (FDM) process. The KB-ANN based model for FDM has better performance and generalization model with low mean squared error in comparison to a conventional ANN.
    Original languageEnglish
    Article number021705
    Number of pages12
    JournalJournal of Mechanical Design
    Volume141
    Issue number2
    Early online date2018
    DOIs
    Publication statusPublished - Feb 2019
    Publication typeA1 Journal article-refereed
    EventASME International Design Engineering Technical Conferences (IDETC) / Computers and Information in Engineering Conference (CIE) - Quebec City, Canada
    Duration: 26 Aug 201829 Aug 2018

    Keywords

    • additive manufacturing
    • fused deposition modeling
    • dimensional analysis
    • empirical learning
    • knowledge-based artificial neural networks
    • ENGINEERING DESIGN
    • OPTIMIZATION
    • FRAMEWORK
    • QUALITY

    Publication forum classification

    • Publication forum level 2

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