Conceptual Framework for Synthetic Data Generation in Remanufacturing

Dominik Koch; Florian Stamer; Gisela Lanza

doi:10.1007/978-3-031-86893-1_35

Conceptual Framework for Synthetic Data Generation in Remanufacturing

Dominik Koch^*
, Florian Stamer
, Gisela Lanza

^*Korrespondierende/r Autor/-in für diese Arbeit

Karlsruher Institut für Technologie

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

Abstract

Remanufacturing requires effective defect detection, but data scarcity and defect variability challenge ML model training. This paper presents a synthetic data generation pipeline focusing on bevel gears, simulating geometric defects like broken teeth, and applying scene randomization to diversify the dataset. The pipeline successfully generates synthetic data for geometric defects, supporting automated segmentation and annotation. However, the current results are limited to geometric defects. Future work will expand to surface and material defects using MDL graphs to improve the pipeline’s applicability. In conclusion, while the pipeline shows potential, real-world validation is necessary to confirm its robustness in remanufacturing.

Originalsprache	Englisch
Titel	Production at the Leading Edge of Technology : Proceedings of the 14th Congress of the German Academic Association for Production Technology (WGP), Chemnitz University of Technology, December 2024
Redakteure/-innen	Welf-Guntram Drossel, Steffen Ihlenfeldt, Martin Dix
Seitenumfang	9
Herausgeber (Verlag)	Springer Nature
Erscheinungsdatum	2025
Seiten	317-325
ISBN (Print)	978-3-031-86892-4, 978-3-031-86895-5
ISBN (elektronisch)	978-3-031-86893-1
DOIs	https://doi.org/10.1007/978-3-031-86893-1_35
Publikationsstatus	Erschienen - 2025
Extern publiziert	Ja

Bibliographische Notiz

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Fachgebiete und Schlagwörter

Ingenieurwissenschaften

ASJC Scopus Sachgebiete

Wirtschaftsingenieurwesen und Fertigungstechnik
Volkswirtschaftslehre, Ökonometrie und Finanzen (sonstige)
Sicherheit, Risiko, Zuverlässigkeit und Qualität

Zugriff auf Dokument

10.1007/978-3-031-86893-1_35

Dieses zitieren

Koch, D., Stamer, F., & Lanza, G. (2025). Conceptual Framework for Synthetic Data Generation in Remanufacturing. in W.-G. Drossel, S. Ihlenfeldt, & M. Dix (Hrsg.), Production at the Leading Edge of Technology: Proceedings of the 14th Congress of the German Academic Association for Production Technology (WGP), Chemnitz University of Technology, December 2024 (S. 317-325). (Lecture Notes in Production Engineering; Band Part F679). Springer Nature. https://doi.org/10.1007/978-3-031-86893-1_35

Koch, Dominik ; Stamer, Florian ; Lanza, Gisela. / Conceptual Framework for Synthetic Data Generation in Remanufacturing. Production at the Leading Edge of Technology: Proceedings of the 14th Congress of the German Academic Association for Production Technology (WGP), Chemnitz University of Technology, December 2024. Hrsg. / Welf-Guntram Drossel ; Steffen Ihlenfeldt ; Martin Dix. Springer Nature, 2025. S. 317-325 (Lecture Notes in Production Engineering).

@inbook{58f790f8886f4104a5e96e53b1de4edc,

title = "Conceptual Framework for Synthetic Data Generation in Remanufacturing",

abstract = "Remanufacturing requires effective defect detection, but data scarcity and defect variability challenge ML model training. This paper presents a synthetic data generation pipeline focusing on bevel gears, simulating geometric defects like broken teeth, and applying scene randomization to diversify the dataset. The pipeline successfully generates synthetic data for geometric defects, supporting automated segmentation and annotation. However, the current results are limited to geometric defects. Future work will expand to surface and material defects using MDL graphs to improve the pipeline{\textquoteright}s applicability. In conclusion, while the pipeline shows potential, real-world validation is necessary to confirm its robustness in remanufacturing.",

keywords = "optical inspection, remanufacturing, synthetical data, Engineering",

author = "Dominik Koch and Florian Stamer and Gisela Lanza",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.",

year = "2025",

doi = "10.1007/978-3-031-86893-1\_35",

language = "English",

isbn = "978-3-031-86892-4",

series = "Lecture Notes in Production Engineering",

publisher = "Springer Nature",

pages = "317--325",

editor = "Welf-Guntram Drossel and Steffen Ihlenfeldt and Martin Dix",

booktitle = "Production at the Leading Edge of Technology",

address = "Singapore",

}

Koch, D, Stamer, F & Lanza, G 2025, Conceptual Framework for Synthetic Data Generation in Remanufacturing. in W-G Drossel, S Ihlenfeldt & M Dix (Hrsg.), Production at the Leading Edge of Technology: Proceedings of the 14th Congress of the German Academic Association for Production Technology (WGP), Chemnitz University of Technology, December 2024. Lecture Notes in Production Engineering, Bd. Part F679, Springer Nature, S. 317-325. https://doi.org/10.1007/978-3-031-86893-1_35

Conceptual Framework for Synthetic Data Generation in Remanufacturing. / Koch, Dominik; Stamer, Florian; Lanza, Gisela.
Production at the Leading Edge of Technology: Proceedings of the 14th Congress of the German Academic Association for Production Technology (WGP), Chemnitz University of Technology, December 2024. Hrsg. / Welf-Guntram Drossel; Steffen Ihlenfeldt; Martin Dix. Springer Nature, 2025. S. 317-325 (Lecture Notes in Production Engineering; Band Part F679).

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

TY - CHAP

T1 - Conceptual Framework for Synthetic Data Generation in Remanufacturing

AU - Koch, Dominik

AU - Stamer, Florian

AU - Lanza, Gisela

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - Remanufacturing requires effective defect detection, but data scarcity and defect variability challenge ML model training. This paper presents a synthetic data generation pipeline focusing on bevel gears, simulating geometric defects like broken teeth, and applying scene randomization to diversify the dataset. The pipeline successfully generates synthetic data for geometric defects, supporting automated segmentation and annotation. However, the current results are limited to geometric defects. Future work will expand to surface and material defects using MDL graphs to improve the pipeline’s applicability. In conclusion, while the pipeline shows potential, real-world validation is necessary to confirm its robustness in remanufacturing.

AB - Remanufacturing requires effective defect detection, but data scarcity and defect variability challenge ML model training. This paper presents a synthetic data generation pipeline focusing on bevel gears, simulating geometric defects like broken teeth, and applying scene randomization to diversify the dataset. The pipeline successfully generates synthetic data for geometric defects, supporting automated segmentation and annotation. However, the current results are limited to geometric defects. Future work will expand to surface and material defects using MDL graphs to improve the pipeline’s applicability. In conclusion, while the pipeline shows potential, real-world validation is necessary to confirm its robustness in remanufacturing.

KW - optical inspection

KW - remanufacturing

KW - synthetical data

KW - Engineering

UR - https://www.scopus.com/pages/publications/105011695302

U2 - 10.1007/978-3-031-86893-1_35

DO - 10.1007/978-3-031-86893-1_35

M3 - Article in conference proceedings

AN - SCOPUS:105011695302

SN - 978-3-031-86892-4

SN - 978-3-031-86895-5

T3 - Lecture Notes in Production Engineering

SP - 317

EP - 325

BT - Production at the Leading Edge of Technology

A2 - Drossel, Welf-Guntram

A2 - Ihlenfeldt, Steffen

A2 - Dix, Martin

PB - Springer Nature

ER -

Koch D, Stamer F, Lanza G. Conceptual Framework for Synthetic Data Generation in Remanufacturing. in Drossel WG, Ihlenfeldt S, Dix M, Hrsg., Production at the Leading Edge of Technology: Proceedings of the 14th Congress of the German Academic Association for Production Technology (WGP), Chemnitz University of Technology, December 2024. Springer Nature. 2025. S. 317-325. (Lecture Notes in Production Engineering). doi: 10.1007/978-3-031-86893-1_35