Novicos GmbH
Abstract:
Powder bed fusion with laser beam describes a popular additive manufacturing technique that allows for the creation of complex
three-dimensional shapes for
lightweight construction.
However, the current melting and solidification processes may introduce defects that lead to printed components that do not meet
the desired product quality
requirements and standards.
Automated process monitoring may aid in exhausting the full potential of powder bed fusion by reducing rejects, saving resources
at the same time, and
subsequently ensuring high product quality.
We therefore propose to utilize machine learning algorithms with training data obtained directly from in situ measurements using
acoustic emissions sensors as
well as numerically from supplementary acoustics simulations.
Here we outline the project and give the strategic roadmap for developing reliable methods that are capable of recognizing
deviations from common system
operations in the printing process due to defects and other artifacts.
This work includes a preview of intermediate results from first machine learning experiments.
Additionally, an early comparison of measurement and simulation data is given.