Details
| Original language | English |
|---|---|
| Title of host publication | Production at the Leading Edge of Technology |
| Publisher | Springer Nature |
| Pages | 613-622 |
| Number of pages | 10 |
| ISBN (electronic) | 978-3-031-18318-8 |
| ISBN (print) | 978-3-031-18317-1 |
| Publication status | Published - 2023 |
Publication series
| Name | Lecture Notes in Production Engineering |
|---|---|
| Volume | Part F1163 |
| ISSN (Print) | 2194-0525 |
| ISSN (electronic) | 2194-0533 |
Abstract
The disassembly of complex capital goods is characterized by strong uncertainty regarding the product condition and possible damage patterns to be expected during a regeneration job. Due to the high value of complex capital goods, the disassembly process must be as gentle as possible and being adaptable to the varying und uncertain product's state. While methods based on data mining have already been successfully used to forecast capacity and material requirements, the determination of the product’s or component's condition has become apparent in the recent past. Despite the rapid increase in sensor technology on capital goods such as aircraft engines and their use for condition monitoring due to countless interfering effects, it is only possible to react spontaneously to the product’s condition. So far, we have concentrated on product condition-based prioritization of disassembly operations in a logistics-oriented sequencing strategy. In this article, we present an approach to predict disassembly process-planning parameters based on operational usage data using machine learning. With the prediction of disassembly forces and times, processes, tools and capacities can be efficiently planned. Thus, we can establish a component-friendly disassembly process adaptable to varying product conditions. In this article, we show the successful validation on a replacement model of an aircraft engine.
Keywords
- Disassembly planning, Machine learning, Regeneration
ASJC Scopus subject areas
- Engineering(all)
- Industrial and Manufacturing Engineering
- Economics, Econometrics and Finance(all)
- Economics, Econometrics and Finance (miscellaneous)
- Engineering(all)
- Safety, Risk, Reliability and Quality
Cite this
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Production at the Leading Edge of Technology. Springer Nature, 2023. p. 613-622 (Lecture Notes in Production Engineering; Vol. Part F1163).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Prediction of Disassembly Parameters for Process Planning Based on Machine Learning
AU - Blümel, Richard
AU - Zander, Niklas
AU - Blankemeyer, Sebastian
AU - Raatz, Annika
N1 - Funding Information: Acknowledgements. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—SFB 871/3-119193472.
PY - 2023
Y1 - 2023
N2 - The disassembly of complex capital goods is characterized by strong uncertainty regarding the product condition and possible damage patterns to be expected during a regeneration job. Due to the high value of complex capital goods, the disassembly process must be as gentle as possible and being adaptable to the varying und uncertain product's state. While methods based on data mining have already been successfully used to forecast capacity and material requirements, the determination of the product’s or component's condition has become apparent in the recent past. Despite the rapid increase in sensor technology on capital goods such as aircraft engines and their use for condition monitoring due to countless interfering effects, it is only possible to react spontaneously to the product’s condition. So far, we have concentrated on product condition-based prioritization of disassembly operations in a logistics-oriented sequencing strategy. In this article, we present an approach to predict disassembly process-planning parameters based on operational usage data using machine learning. With the prediction of disassembly forces and times, processes, tools and capacities can be efficiently planned. Thus, we can establish a component-friendly disassembly process adaptable to varying product conditions. In this article, we show the successful validation on a replacement model of an aircraft engine.
AB - The disassembly of complex capital goods is characterized by strong uncertainty regarding the product condition and possible damage patterns to be expected during a regeneration job. Due to the high value of complex capital goods, the disassembly process must be as gentle as possible and being adaptable to the varying und uncertain product's state. While methods based on data mining have already been successfully used to forecast capacity and material requirements, the determination of the product’s or component's condition has become apparent in the recent past. Despite the rapid increase in sensor technology on capital goods such as aircraft engines and their use for condition monitoring due to countless interfering effects, it is only possible to react spontaneously to the product’s condition. So far, we have concentrated on product condition-based prioritization of disassembly operations in a logistics-oriented sequencing strategy. In this article, we present an approach to predict disassembly process-planning parameters based on operational usage data using machine learning. With the prediction of disassembly forces and times, processes, tools and capacities can be efficiently planned. Thus, we can establish a component-friendly disassembly process adaptable to varying product conditions. In this article, we show the successful validation on a replacement model of an aircraft engine.
KW - Disassembly planning
KW - Machine learning
KW - Regeneration
UR - http://www.scopus.com/inward/record.url?scp=85166665847&partnerID=8YFLogxK
U2 - 10.15488/13412
DO - 10.15488/13412
M3 - Contribution to book/anthology
AN - SCOPUS:85166665847
SN - 978-3-031-18317-1
T3 - Lecture Notes in Production Engineering
SP - 613
EP - 622
BT - Production at the Leading Edge of Technology
PB - Springer Nature
ER -