Quality Monitoring Procedure in Additive Material Extrusion Using Machine Learning

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Autoren

  • Anne Rathje
  • Ronja Witt
  • Anna Lena Knott
  • Benjamin Küster
  • Malte Stonis
  • Ludger Overmeyer
  • Robert H. Schmitt

Organisationseinheiten

Externe Organisationen

  • Institut für integrierte Produktion Hannover (IPH) gGmbH
  • Rheinisch-Westfälische Technische Hochschule Aachen (RWTH)
  • Fraunhofer-Institut für Produktionstechnologie (IPT)
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des SammelwerksSoftware Engineering and Formal Methods. SEFM 2022 Collocated Workshops
UntertitelAI4EA, F-IDE, CoSim-CPS, CIFMA, Berlin, Germany, September 26–30, 2022, Revised Selected Papers
Herausgeber/-innenPaolo Masci, Cinzia Bernardeschi, Maurizio Palmieri, Pierluigi Graziani, Mario Koddenbrock
ErscheinungsortCham
Seiten93-102
Seitenumfang10
ISBN (elektronisch)978-3-031-26236-4
PublikationsstatusVeröffentlicht - 11 Feb. 2023
Veranstaltung20th International Conference on Software Engineering and Formal Methods, SEFM 2022 - Berlin, Deutschland
Dauer: 26 Sept. 2022 → 30 Sept. 2022

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band13765 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)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.

ASJC Scopus Sachgebiete

Zitieren

Quality Monitoring Procedure in Additive Material Extrusion Using Machine Learning. / Rathje, Anne; Witt, Ronja; Knott, Anna Lena et al.
Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops: AI4EA, F-IDE, CoSim-CPS, CIFMA, Berlin, Germany, September 26–30, 2022, Revised Selected Papers. Hrsg. / Paolo Masci; Cinzia Bernardeschi; Maurizio Palmieri; Pierluigi Graziani; Mario Koddenbrock. Cham, 2023. S. 93-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13765 LNCS).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Rathje, A, Witt, R, Knott, AL, Küster, B, Stonis, M, Overmeyer, L & Schmitt, RH 2023, Quality Monitoring Procedure in Additive Material Extrusion Using Machine Learning. in P Masci, C Bernardeschi, M Palmieri, P Graziani & M Koddenbrock (Hrsg.), Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops: AI4EA, F-IDE, CoSim-CPS, CIFMA, Berlin, Germany, September 26–30, 2022, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 13765 LNCS, Cham, S. 93-102, 20th International Conference on Software Engineering and Formal Methods, SEFM 2022, Berlin, Deutschland, 26 Sept. 2022. https://doi.org/10.1007/978-3-031-26236-4_8
Rathje, A., Witt, R., Knott, A. L., Küster, B., Stonis, M., Overmeyer, L., & Schmitt, R. H. (2023). Quality Monitoring Procedure in Additive Material Extrusion Using Machine Learning. In P. Masci, C. Bernardeschi, M. Palmieri, P. Graziani, & M. Koddenbrock (Hrsg.), Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops: AI4EA, F-IDE, CoSim-CPS, CIFMA, Berlin, Germany, September 26–30, 2022, Revised Selected Papers (S. 93-102). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13765 LNCS).. https://doi.org/10.1007/978-3-031-26236-4_8
Rathje A, Witt R, Knott AL, Küster B, Stonis M, Overmeyer L et al. Quality Monitoring Procedure in Additive Material Extrusion Using Machine Learning. in Masci P, Bernardeschi C, Palmieri M, Graziani P, Koddenbrock M, Hrsg., Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops: AI4EA, F-IDE, CoSim-CPS, CIFMA, Berlin, Germany, September 26–30, 2022, Revised Selected Papers. Cham. 2023. S. 93-102. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-26236-4_8
Rathje, Anne ; Witt, Ronja ; Knott, Anna Lena et al. / Quality Monitoring Procedure in Additive Material Extrusion Using Machine Learning. Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops: AI4EA, F-IDE, CoSim-CPS, CIFMA, Berlin, Germany, September 26–30, 2022, Revised Selected Papers. Hrsg. / Paolo Masci ; Cinzia Bernardeschi ; Maurizio Palmieri ; Pierluigi Graziani ; Mario Koddenbrock. Cham, 2023. S. 93-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Download
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