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
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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
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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 -