Details
| Original language | English |
|---|---|
| Title of host publication | Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops |
| Subtitle of host publication | AI4EA, F-IDE, CoSim-CPS, CIFMA, Berlin, Germany, September 26–30, 2022, Revised Selected Papers |
| Editors | Paolo Masci, Cinzia Bernardeschi, Maurizio Palmieri, Pierluigi Graziani, Mario Koddenbrock |
| Place of Publication | Cham |
| Pages | 93-102 |
| Number of pages | 10 |
| ISBN (electronic) | 978-3-031-26236-4 |
| Publication status | Published - 11 Feb 2023 |
| Event | Workshops on AI4EA, F-IDE, CoSim-CPS, CIFMA 2022, Collocated with the 20th International Conference on Software Engineering and Formal Methods, SEFM 2022 - Berlin, Germany Duration: 26 Sept 2022 → 30 Sept 2022 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 13765 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Additive manufacturing enables the economical production of complex components with a high degree of customization. Therefore, the medical industry is using the advantages of additive manufacturing to produce individualized medical devices. Medical devices are subject to special quality control requirements that additive manufacturing processes do not meet yet. This article deals with the introduction of an in situ process monitoring concept using the example of fused deposition modeling. The process monitoring is carried out by a quality model, which accesses the data of a self-developed sensor concept integrated in the printer. This data is analyzed using a machine learning pipeline to predict process and product quality. Thereby, the machine learning pipeline consist of several sequential steps, ranging from data extraction and preprocessing to model training and deployment. The procedure presented for ensuring print quality forms a basis for the production of safety-relevant components in batch size one and extends conventional quality assurance methods in additive manufacturing.
Keywords
- Additive manufacturing, Machine learning, Quality control
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops: AI4EA, F-IDE, CoSim-CPS, CIFMA, Berlin, Germany, September 26–30, 2022, Revised Selected Papers. ed. / Paolo Masci; Cinzia Bernardeschi; Maurizio Palmieri; Pierluigi Graziani; Mario Koddenbrock. Cham, 2023. p. 93-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13765 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Quality Monitoring Procedure in Additive Material Extrusion Using Machine Learning
AU - Rathje, Anne
AU - Witt, Ronja
AU - Knott, Anna Lena
AU - Küster, Benjamin
AU - Stonis, Malte
AU - Overmeyer, Ludger
AU - Schmitt, Robert H.
N1 - Funding Information: The IGF-promotion plan 21610 N (saviour) of the Research Community for Quality (FQS), August-Schanz-Straße 21A, 60433 Frankfurt/Main has been funded by the AiF within the program for sponsorship by Industrial Joint Research (IGF) of the German Federal Ministry of Economic Affairs and Energy based on an enactment of the German Parliament. Funding Information: Acknowledgement. This research is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC-2023 Internet of Production—390621612.
PY - 2023/2/11
Y1 - 2023/2/11
N2 - Additive manufacturing enables the economical production of complex components with a high degree of customization. Therefore, the medical industry is using the advantages of additive manufacturing to produce individualized medical devices. Medical devices are subject to special quality control requirements that additive manufacturing processes do not meet yet. This article deals with the introduction of an in situ process monitoring concept using the example of fused deposition modeling. The process monitoring is carried out by a quality model, which accesses the data of a self-developed sensor concept integrated in the printer. This data is analyzed using a machine learning pipeline to predict process and product quality. Thereby, the machine learning pipeline consist of several sequential steps, ranging from data extraction and preprocessing to model training and deployment. The procedure presented for ensuring print quality forms a basis for the production of safety-relevant components in batch size one and extends conventional quality assurance methods in additive manufacturing.
AB - Additive manufacturing enables the economical production of complex components with a high degree of customization. Therefore, the medical industry is using the advantages of additive manufacturing to produce individualized medical devices. Medical devices are subject to special quality control requirements that additive manufacturing processes do not meet yet. This article deals with the introduction of an in situ process monitoring concept using the example of fused deposition modeling. The process monitoring is carried out by a quality model, which accesses the data of a self-developed sensor concept integrated in the printer. This data is analyzed using a machine learning pipeline to predict process and product quality. Thereby, the machine learning pipeline consist of several sequential steps, ranging from data extraction and preprocessing to model training and deployment. The procedure presented for ensuring print quality forms a basis for the production of safety-relevant components in batch size one and extends conventional quality assurance methods in additive manufacturing.
KW - Additive manufacturing
KW - Machine learning
KW - Quality control
UR - http://www.scopus.com/inward/record.url?scp=85151060766&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-26236-4_8
DO - 10.1007/978-3-031-26236-4_8
M3 - Conference contribution
AN - SCOPUS:85151060766
SN - 9783031262357
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 93
EP - 102
BT - Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops
A2 - Masci, Paolo
A2 - Bernardeschi, Cinzia
A2 - Palmieri, Maurizio
A2 - Graziani, Pierluigi
A2 - Koddenbrock, Mario
CY - Cham
T2 - Workshops on AI4EA, F-IDE, CoSim-CPS, CIFMA 2022, Collocated with the 20th International Conference on Software Engineering and Formal Methods, SEFM 2022
Y2 - 26 September 2022 through 30 September 2022
ER -