Prediction of Disassembly Parameters for Process Planning Based on Machine Learning

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Details

Original languageEnglish
Title of host publicationProduction at the Leading Edge of Technology
PublisherSpringer Nature
Pages613-622
Number of pages10
ISBN (electronic)978-3-031-18318-8
ISBN (print)978-3-031-18317-1
Publication statusPublished - 2023

Publication series

NameLecture Notes in Production Engineering
VolumePart 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

Cite this

Prediction of Disassembly Parameters for Process Planning Based on Machine Learning. / Blümel, Richard; Zander, Niklas; Blankemeyer, Sebastian et al.
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 proceedingContribution to book/anthologyResearchpeer review

Blümel, R, Zander, N, Blankemeyer, S & Raatz, A 2023, Prediction of Disassembly Parameters for Process Planning Based on Machine Learning. in Production at the Leading Edge of Technology. Lecture Notes in Production Engineering, vol. Part F1163, Springer Nature, pp. 613-622. https://doi.org/10.15488/13412, https://doi.org/10.1007/978-3-031-18318-8_61
Blümel, R., Zander, N., Blankemeyer, S., & Raatz, A. (2023). Prediction of Disassembly Parameters for Process Planning Based on Machine Learning. In Production at the Leading Edge of Technology (pp. 613-622). (Lecture Notes in Production Engineering; Vol. Part F1163). Springer Nature. https://doi.org/10.15488/13412, https://doi.org/10.1007/978-3-031-18318-8_61
Blümel R, Zander N, Blankemeyer S, Raatz A. Prediction of Disassembly Parameters for Process Planning Based on Machine Learning. In Production at the Leading Edge of Technology. Springer Nature. 2023. p. 613-622. (Lecture Notes in Production Engineering). doi: 10.15488/13412, 10.1007/978-3-031-18318-8_61
Blümel, Richard ; Zander, Niklas ; Blankemeyer, Sebastian et al. / Prediction of Disassembly Parameters for Process Planning Based on Machine Learning. Production at the Leading Edge of Technology. Springer Nature, 2023. pp. 613-622 (Lecture Notes in Production Engineering).
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