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Updated: Jul 16, 2025

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers

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

Lukas Pelzer¹, Tobias Schulze², Daniel Buschmann²

¹Institute for Plastics Processing at RWTH Aachen University, 52074 Aachen, Germany.

|September 9, 2023

Summary

This summary is machine-generated.

This study uses interpretable machine learning to understand additive manufacturing (AM) process parameters. It identifies optimal settings and parameter interactions, enabling better part quality without complex analytical models.

Keywords:

additive manufacturing feature importance fused layer modeling interpretable machine learning machine Learning process characterization process knowledge process optimization

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Area of Science:

Materials Science and Engineering
Manufacturing Technology
Artificial Intelligence

Background:

Additive manufacturing (AM) processes, particularly extrusion-based methods, involve numerous parameters influencing part properties.
Complex interdependencies between these parameters make analytical modeling challenging.
Machine learning (ML) offers a potential solution for optimizing AM parameters but often acts as a black box, hindering knowledge extraction.

Purpose of the Study:

To apply interpretable machine learning (IML) methods to derive process knowledge from AM data.
To overcome the limitations of black box ML models in understanding and verifying AM process outputs.
To objectively determine optimal process parameters and uncover parameter interactions in AM.

Main Methods:

Utilized interpretable machine learning techniques to analyze feature importance in AM datasets.
Applied fused layer modeling (FLM) as a case study for demonstrating the methodology.
Interpreted model outputs to identify relationships between process parameters and part properties.

Main Results:

Demonstrated that the FLM process can be fully characterized using IML.
Objectively identified 'sweet spots' for process parameters to achieve desired part properties.
Discovered significant interactions between various process parameters.

Conclusions:

Interpretable machine learning provides a viable approach for gaining process knowledge in AM.
Objective determination of optimal process parameters and understanding of parameter interactions are achievable.
The study establishes a foundation for further research into AM process optimization and control.