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Acquiring Process Knowledge in Extrusion-Based Additive Manufacturing via Interpretable Machine Learning
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 ap...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490136/ https://www.ncbi.nlm.nih.gov/pubmed/37688135 http://dx.doi.org/10.3390/polym15173509 |
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author | Pelzer, Lukas Schulze, Tobias Buschmann, Daniel Enslin, Chrismarie Schmitt, Robert Hopmann, Christian |
author_facet | Pelzer, Lukas Schulze, Tobias Buschmann, Daniel Enslin, Chrismarie Schmitt, Robert Hopmann, Christian |
author_sort | Pelzer, Lukas |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10490136 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104901362023-09-09 Acquiring Process Knowledge in Extrusion-Based Additive Manufacturing via Interpretable Machine Learning Pelzer, Lukas Schulze, Tobias Buschmann, Daniel Enslin, Chrismarie Schmitt, Robert Hopmann, Christian Polymers (Basel) Article 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. MDPI 2023-08-23 /pmc/articles/PMC10490136/ /pubmed/37688135 http://dx.doi.org/10.3390/polym15173509 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pelzer, Lukas Schulze, Tobias Buschmann, Daniel Enslin, Chrismarie Schmitt, Robert Hopmann, Christian Acquiring Process Knowledge in Extrusion-Based Additive Manufacturing via Interpretable Machine Learning |
title | Acquiring Process Knowledge in Extrusion-Based Additive Manufacturing via Interpretable Machine Learning |
title_full | Acquiring Process Knowledge in Extrusion-Based Additive Manufacturing via Interpretable Machine Learning |
title_fullStr | Acquiring Process Knowledge in Extrusion-Based Additive Manufacturing via Interpretable Machine Learning |
title_full_unstemmed | Acquiring Process Knowledge in Extrusion-Based Additive Manufacturing via Interpretable Machine Learning |
title_short | Acquiring Process Knowledge in Extrusion-Based Additive Manufacturing via Interpretable Machine Learning |
title_sort | acquiring process knowledge in extrusion-based additive manufacturing via interpretable machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490136/ https://www.ncbi.nlm.nih.gov/pubmed/37688135 http://dx.doi.org/10.3390/polym15173509 |
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