Process Parameter Prediction for Fused Deposition Modeling Using Invertible Neural Networks

Polymers, vol. 15, pp. 1884

Abstract

Additive manufacturing has revolutionized prototyping and small-scale production in the past years. By creating parts layer by layer, a tool-less production technology is established, which allows for rapid adaption of the manufacturing process and customization of the product. However, the geometric freedom of the technologies comes with a large number of process parameters, especially in Fused Deposition Modeling (FDM), all of which influence the resulting part's properties. Since those parameters show interdependencies and non-linearities, choosing a suitable set to create the desired part properties is not trivial. This study demonstrates the use of Invertible Neural Networks (INN) for generating process parameters objectively. By specifying the desired part in the categories of mechanical properties, optical properties and manufacturing time, the demonstrated INN generates process parameters capable of closely replicating the desired part. Validation trials prove the precision of the solution with measured properties achieving the desired properties to up to 99.96% and a mean accuracy of 85.34%.

Authors 5

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

   RWTH Aachen University

   Affiliation as printed

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

2. Andrés Felipe Posada-Moreno Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Data Science in Mechanical Engineering, RWTH Aachen University, 52068 Aachen, Germany

3. Kai Müller Aachen Institut für Textiltechnik

   RWTH Aachen University

   Affiliation as printed

   Institut für Textiltechnik, RWTH Aachen University, 52074 Aachen, Germany

4. Christoph Greb Aachen Institut für Textiltechnik

   RWTH Aachen University

   Affiliation as printed

   Institut für Textiltechnik, RWTH Aachen University, 52074 Aachen, Germany

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

   RWTH Aachen University

   Affiliation as printed

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