Programmable Density of Laser Additive Manufactured Parts by Considering an Inverse Problem

Mika León Altmann; Stefan Bosse; Christian Werner; Rainer Fechte-Heinen; Anastasiya Toenjes

doi:10.3390/ma15207090

Programmable Density of Laser Additive Manufactured Parts by Considering an Inverse Problem

Materials (Basel). 2022 Oct 12;15(20):7090. doi: 10.3390/ma15207090.

Authors

Mika León Altmann¹, Stefan Bosse², Christian Werner¹, Rainer Fechte-Heinen^{1

3

4}, Anastasiya Toenjes^{1

3}

Affiliations

¹ Leibniz-Institute for Materials Engineering-IWT, Badgasteiner Str. 3, 28359 Bremen, Germany.
² Department of Mathematics and Computer Science, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany.
³ Faculty of Production Engineering, University of Bremen, Bibliothekstr. 1, 28359 Bremen, Germany.
⁴ MAPEX Centre for Materials and Processes, University of Bremen, 28359 Bremen, Germany.

Abstract

In this Article, the targeted adjustment of the relative density of laser additive manufactured components made of AlSi10Mg is considered. The interest in demand-oriented process parameters is steadily increasing. Thus, shorter process times and lower unit costs can be achieved with decreasing component densities. Especially when hot isostatic pressing is considered as a post-processing step. In order to be able to generate process parameters automatically, a model hypothesis is learned via artificial neural networks (ANN) for a density range from 70% to almost 100%, based on a synthetic dataset with equally distributed process parameters and a statistical test series with 256 full factorial combined instances. This allows the achievable relative density to be predicted from given process parameters. Based on the best model, a database approach and supervised training of concatenated ANNs are developed to solve the inverse parameter prediction problem for a target density. In this way, it is possible to generate a parameter prediction model for the high-dimensional result space through constraints that are shown with synthetic test data sets. The presented concatenated ANN model is able to reproduce the origin distribution. The relative density of synthetic data can be predicted with an R²-value of 0.98. The mean build rate can be increased by 12% with the formulation of a hint during the backward model training. The application of the experimental data shows increased fuzziness related to the big data gaps and a small number of instances. For practical use, this algorithm could be trained on increased data sets and can be expanded by properties such as surface quality, residual stress, or mechanical strength. With knowledge of the necessary (mechanical) properties of the components, the model can be used to generate appropriate process parameters. This way, the processing time and the amount of scrap parts can be reduced.

Keywords: AlSi10Mg; LPBF; PBF-LB/M; additive manufacturing; adjustable relative density; demand-oriented process parameters; inverse problem; machine learning.

Grants and funding

This research was funded by the European Union from the European Regional Development Fund (ERDF) within the Bremen aerospace research program in Germany, Grant number LURAFO2002A. The author is responsible for the contents of this publication.