Knowledge-Based Design of Artificial Neural Network Topology for Additive Manufacturing Process Modeling: A New Approach and Case Study for Fused Deposition Modeling
| Autor: | Hossein Mokhtarian, Hesam Jafarian, Saoussen Dimassi, Gongming Wang, Karl R. Haapala, Azarakhsh Hamedi, Eric Coatanéa, Shahriar Bakrani Balani, Hari P.N. Nagarajan |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
media_common.quotation_subject Computer Science::Neural and Evolutionary Computation 0211 other engineering and technologies Topology (electrical circuits) 02 engineering and technology law.invention Empirical learning law 0202 electrical engineering electronic engineering information engineering Quality (business) 021106 design practice & management media_common Artificial neural network Fused deposition modeling Manufacturing process business.industry Mechanical Engineering Computer Graphics and Computer-Aided Design Computer Science Applications Mechanics of Materials 020201 artificial intelligence & image processing Artificial intelligence Wall thickness business Engineering design process |
| Zdroj: | Journal of Mechanical Design. 141 |
| ISSN: | 1528-9001 1050-0472 |
| Popis: | 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 different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN. |
| Databáze: | OpenAIRE |
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