Details
| Original language | English |
|---|---|
| Pages (from-to) | 3-8 |
| Number of pages | 6 |
| Journal | Procedia CIRP |
| Volume | 120 |
| Early online date | 12 Jan 2023 |
| Publication status | Published - 2023 |
| Event | 56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023 - Cape Town, South Africa Duration: 24 Oct 2023 → 26 Oct 2023 |
Abstract
The integration of a digital twin into inspection planning enables a novel procedure that reduces avoidable inspection times and costs. This paper shows a method for component-specific adaption of inspection plans by feeding back data-based quality results into inspection planning. An initial evaluation of the method on a real aerospace aluminum component is carried out using a 3-axis milling process. Machine learning based quality models were implemented for the inspection features shape deviation and surface roughness. With the knowledge gained, the inspection time for the process can be reduced by up to 75 % per component.
Keywords
- adaptivity, digital twin, IP, machine learning, quality assurance
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 120, 2023, p. 3-8.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Adaptive inspection planning using a digital twin for quality assurance
AU - Reuter, Leon
AU - Denkena, Berend
AU - Wichmann, Marcel
N1 - Funding Information: The work is being carried out within the framework of the project (ZW1 - 80159725) by the Investitions-and development bank of Lower Saxony (NBank). We would also like to thank our project partner Premium Aerotec GmbH and the Sieglinde Vollmer Foundation for supporting this research.
PY - 2023
Y1 - 2023
N2 - The integration of a digital twin into inspection planning enables a novel procedure that reduces avoidable inspection times and costs. This paper shows a method for component-specific adaption of inspection plans by feeding back data-based quality results into inspection planning. An initial evaluation of the method on a real aerospace aluminum component is carried out using a 3-axis milling process. Machine learning based quality models were implemented for the inspection features shape deviation and surface roughness. With the knowledge gained, the inspection time for the process can be reduced by up to 75 % per component.
AB - The integration of a digital twin into inspection planning enables a novel procedure that reduces avoidable inspection times and costs. This paper shows a method for component-specific adaption of inspection plans by feeding back data-based quality results into inspection planning. An initial evaluation of the method on a real aerospace aluminum component is carried out using a 3-axis milling process. Machine learning based quality models were implemented for the inspection features shape deviation and surface roughness. With the knowledge gained, the inspection time for the process can be reduced by up to 75 % per component.
KW - adaptivity
KW - digital twin
KW - IP
KW - machine learning
KW - quality assurance
UR - http://www.scopus.com/inward/record.url?scp=85184592314&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2023.08.002
DO - 10.1016/j.procir.2023.08.002
M3 - Conference article
AN - SCOPUS:85184592314
VL - 120
SP - 3
EP - 8
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023
Y2 - 24 October 2023 through 26 October 2023
ER -