Tampere University of Technology

TUTCRIS Research Portal

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

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
JournalJournal of Mechanical Design
Issue number2
Early online date2018
Publication statusPublished - Feb 2019
Publication typeA1 Journal article-refereed


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.

Publication forum classification

Field of science, Statistics Finland