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Concept for Process Parameter-Based Inline Quality Control as a Basis for Pairing in a Production Line

Research output: Contributions to collected editions/works › Chapter › peer-review

Standard

Concept for Process Parameter-Based Inline Quality Control as a Basis for Pairing in a Production Line. / Geiser, A.; Stamer, F.; Lanza, G.
Production at the Leading Edge of Technology : Proceedings of the 13th Congress of the German Academic Association for Production Technology (WGP), Freudenstadt, November 2023. ed. / Thomas Baunernhansl; Alexander Verl; Mathias Liewald; Hans-Christian Möhring. Springer Nature, 2024. p. 481-487 (Lecture Notes in Production Engineering; Vol. Part F1764).

Research output: Contributions to collected editions/works › Chapter › peer-review

Harvard

Geiser, A, Stamer, F & Lanza, G 2024, Concept for Process Parameter-Based Inline Quality Control as a Basis for Pairing in a Production Line. in T Baunernhansl, A Verl, M Liewald & H-C Möhring (eds), Production at the Leading Edge of Technology : Proceedings of the 13th Congress of the German Academic Association for Production Technology (WGP), Freudenstadt, November 2023. Lecture Notes in Production Engineering, vol. Part F1764, Springer Nature, pp. 481-487. https://doi.org/10.1007/978-3-031-47394-4_47

APA

Geiser, A., Stamer, F., & Lanza, G. (2024). Concept for Process Parameter-Based Inline Quality Control as a Basis for Pairing in a Production Line. In T. Baunernhansl, A. Verl, M. Liewald, & H.-C. Möhring (Eds.), Production at the Leading Edge of Technology : Proceedings of the 13th Congress of the German Academic Association for Production Technology (WGP), Freudenstadt, November 2023 (pp. 481-487). (Lecture Notes in Production Engineering; Vol. Part F1764). Springer Nature. https://doi.org/10.1007/978-3-031-47394-4_47

Vancouver

Geiser A, Stamer F, Lanza G. Concept for Process Parameter-Based Inline Quality Control as a Basis for Pairing in a Production Line. In Baunernhansl T, Verl A, Liewald M, Möhring HC, editors, Production at the Leading Edge of Technology : Proceedings of the 13th Congress of the German Academic Association for Production Technology (WGP), Freudenstadt, November 2023. Springer Nature. 2024. p. 481-487. (Lecture Notes in Production Engineering). doi: 10.1007/978-3-031-47394-4_47

Bibtex

@inbook{df42d1da04b24639a685793fef8235bb,
title = "Concept for Process Parameter-Based Inline Quality Control as a Basis for Pairing in a Production Line",
abstract = "This paper presents a general concept for an in-line quality control system and the basis for component pairing using the example of a pressure valve. The aim is to improve the functional quality of the product while increasing dimensional tolerances to reduce waste. The proposed system requires an end-of-line (EOL) functional test and collects pre-existing sensor data from the production line. This data is used to train machine learning models to identify correlations between measurements and EOL test results. The system uses this information to predict future EOL test results. Anomaly detection and root cause analysis is performed by comparing predicted results with actual measurements. To improve the data set, additional sensors are integrated into the identified production steps. Once parameters with a high influence on the product function have been identified, these should be used to find ideal pairs of components with favorable parameter combinations in order to improve functionality. The EOL test is then used for validation.",
keywords = "Functional quality control, In-Process, pairing, Engineering",
author = "A. Geiser and F. Stamer and G. Lanza",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.",
year = "2024",
doi = "10.1007/978-3-031-47394-4_47",
language = "English",
isbn = "978-3-031-47393-7",
series = "Lecture Notes in Production Engineering",
publisher = "Springer Nature",
pages = "481--487",
editor = "Thomas Baunernhansl and Alexander Verl and Mathias Liewald and Hans-Christian M{\"o}hring",
booktitle = "Production at the Leading Edge of Technology",
address = "Germany",

}

RIS

TY - CHAP

T1 - Concept for Process Parameter-Based Inline Quality Control as a Basis for Pairing in a Production Line

AU - Geiser, A.

AU - Stamer, F.

AU - Lanza, G.

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - This paper presents a general concept for an in-line quality control system and the basis for component pairing using the example of a pressure valve. The aim is to improve the functional quality of the product while increasing dimensional tolerances to reduce waste. The proposed system requires an end-of-line (EOL) functional test and collects pre-existing sensor data from the production line. This data is used to train machine learning models to identify correlations between measurements and EOL test results. The system uses this information to predict future EOL test results. Anomaly detection and root cause analysis is performed by comparing predicted results with actual measurements. To improve the data set, additional sensors are integrated into the identified production steps. Once parameters with a high influence on the product function have been identified, these should be used to find ideal pairs of components with favorable parameter combinations in order to improve functionality. The EOL test is then used for validation.

AB - This paper presents a general concept for an in-line quality control system and the basis for component pairing using the example of a pressure valve. The aim is to improve the functional quality of the product while increasing dimensional tolerances to reduce waste. The proposed system requires an end-of-line (EOL) functional test and collects pre-existing sensor data from the production line. This data is used to train machine learning models to identify correlations between measurements and EOL test results. The system uses this information to predict future EOL test results. Anomaly detection and root cause analysis is performed by comparing predicted results with actual measurements. To improve the data set, additional sensors are integrated into the identified production steps. Once parameters with a high influence on the product function have been identified, these should be used to find ideal pairs of components with favorable parameter combinations in order to improve functionality. The EOL test is then used for validation.

KW - Functional quality control

KW - In-Process

KW - pairing

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85178338153&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-47394-4_47

DO - 10.1007/978-3-031-47394-4_47

M3 - Chapter

AN - SCOPUS:85178338153

SN - 978-3-031-47393-7

T3 - Lecture Notes in Production Engineering

SP - 481

EP - 487

BT - Production at the Leading Edge of Technology

A2 - Baunernhansl, Thomas

A2 - Verl, Alexander

A2 - Liewald, Mathias

A2 - Möhring, Hans-Christian

PB - Springer Nature

ER -

DOI

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