Acquiring Process Knowledge in Extrusion-Based Additive Manufacturing via Interpretable Machine Learning

Polymers, vol. 15, pp. 3509

Abstract

Additive manufacturing (AM), especially the extrusion-based process, has many process parameters which influence the resulting part properties. Those parameters have complex interdependencies and are therefore difficult if not impossible to model analytically. Machine learning (ML) is a promising approach to find suitable combinations of process parameters for manufacturing a part with desired properties without having to analytically model the process in its entirety. However, ML-based approaches are typically black box models. Therefore, it is difficult to verify their output and to derive process knowledge from such approaches. This study uses interpretable machine learning methods to derive process knowledge from interpreted data sets by analyzing the model's feature importance. Using fused layer modeling (FLM) as an exemplary manufacturing technology, it is shown that the process can be characterized entirely. Therefore, sweet spots for process parameters can be determined objectively. Additionally, interactions between parameters are discovered, and the basis for further investigations is established.

Authors 6

1. Lukas Pelzer corresponding Aachen Institute for Plastics Processing (IKV)

   RWTH Aachen University

   Affiliation as printed

   Institute for Plastics Processing at RWTH Aachen University, 52074 Aachen, Germany

2. Tobias Schulze Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, 52074 Aachen, Germany

3. Daniel Buschmann Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, 52074 Aachen, Germany

4. Chrismarie Enslin Aachen Cybernetics Lab IMA & IfU

   RWTH Aachen University

   Affiliation as printed

   Cybernetics Lab IMA & IfU, RWTH Aachen University, 52068 Aachen, Germany

5. Robert Heinrich Schmitt Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, 52074 Aachen, Germany

6. Christian Hopmann Aachen Institute for Plastics Processing (IKV)

   RWTH Aachen University

   Affiliation as printed

   Institute for Plastics Processing at RWTH Aachen University, 52074 Aachen, Germany