Quality Monitoring Procedure in Additive Material Extrusion Using Machine Learning

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Authors

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

External Research Organisations

  • Institut für integrierte Produktion Hannover (IPH)
  • RWTH Aachen University
  • Fraunhofer Institute for Production Technology (IPT)
View graph of relations

Details

Original languageEnglish
Title of host publicationSoftware Engineering and Formal Methods. SEFM 2022 Collocated Workshops
Subtitle of host publicationAI4EA, F-IDE, CoSim-CPS, CIFMA, Berlin, Germany, September 26–30, 2022, Revised Selected Papers
EditorsPaolo Masci, Cinzia Bernardeschi, Maurizio Palmieri, Pierluigi Graziani, Mario Koddenbrock
Place of PublicationCham
Pages93-102
Number of pages10
ISBN (electronic)978-3-031-26236-4
Publication statusPublished - 11 Feb 2023
EventWorkshops 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 202230 Sept 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13765 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

Cite this

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. 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 proceedingConference contributionResearchpeer 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 (eds), 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), vol. 13765 LNCS, Cham, pp. 93-102, 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, 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 (Eds.), Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops: AI4EA, F-IDE, CoSim-CPS, CIFMA, Berlin, Germany, September 26–30, 2022, Revised Selected Papers (pp. 93-102). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 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, editors, 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. p. 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. editor / Paolo Masci ; Cinzia Bernardeschi ; Maurizio Palmieri ; Pierluigi Graziani ; Mario Koddenbrock. Cham, 2023. pp. 93-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Download
@inproceedings{ca001c3ded7440c78f221bb2c4918a3c,
title = "Quality Monitoring Procedure in Additive Material Extrusion Using Machine Learning",
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",
author = "Anne Rathje and Ronja Witt and Knott, {Anna Lena} and Benjamin K{\"u}ster and Malte Stonis and Ludger Overmeyer and Schmitt, {Robert H.}",
note = "Funding Information: The IGF-promotion plan 21610 N (saviour) of the Research Community for Quality (FQS), August-Schanz-Stra{\ss}e 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{\textquoteright}s Excellence Strategy—EXC-2023 Internet of Production—390621612. ; Workshops on AI4EA, F-IDE, CoSim-CPS, CIFMA 2022, Collocated with the 20th International Conference on Software Engineering and Formal Methods, SEFM 2022 ; Conference date: 26-09-2022 Through 30-09-2022",
year = "2023",
month = feb,
day = "11",
doi = "10.1007/978-3-031-26236-4_8",
language = "English",
isbn = "9783031262357",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "93--102",
editor = "Paolo Masci and Cinzia Bernardeschi and Maurizio Palmieri and Pierluigi Graziani and Mario Koddenbrock",
booktitle = "Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops",

}

Download

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 -