• Nenchev, Vladislav
• Hamedi, Azarakhsh
• Jafarian, Hesam
• Tilouche, Shaima
• Prodhon, Romaric
• Mokhtarian, Hossein
• Nagarajan, Hari
• Coatanéa, Eric

Abstract

Additive manufacturing (AM) continues to rise inpopularity due to its various advantages over traditionalmanufacturing processes. AM interests industry, but achievingrepeatable production quality remains problematic for manyAM technologies. Thus, modeling the influence of process variables on the production quality in AM can be highly beneficial in creating useful knowledge of the process and product. An approach combining dimensional analysisconceptual modeling, mutual information based analysis,experimental sampling, factors selection, and modeling basedon knowledge-Based Artificial Neural Network (KB-ANN) isproposed for Fused Deposition Modeling (FDM) process. KB-ANN reduces the excessive amount of training samples required in traditional neural networks. The developed KB-ANN’s topology for FDM, integrates existing literature and expert knowledge of the process. The KB-ANN is compared to conventional ANN using prescribed performance metrics. This research presents a methodology to concurrently perform experiments, classify influential factors, limit the effect of noise in the modeled system, and model using KB-ANN. This research can contribute to the qualification efforts of AM technologies.

Topics

• Deposition
• impedance spectroscopy
• experiment
• additive manufacturing