Disassembly sequencing in the regeneration of complex capital goods

Lucht, Torben; Heuer, Tammo; Nyhuis, Peter

doi:10.15488/9642

Cited by 2 publications

(4 citation statements)

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“…The prioritisation offers the possibility to accelerate material supply for downstream processes actively. One example is the prioritisation of severely damaged components during disassembly to enable the earliest possible start of subsequent repair processes [36]. As shown in Fig.…”

Section: B Logistics Modelling As An Enabler For Configuring the Company's Internal Supply Chainmentioning

confidence: 99%

Logistics-Oriented Production Configuration Using the Example of MRO Service Providers

2022

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Section: B Logistics Modelling As An Enabler For Configuring the Company's Internal Supply Chainmentioning

confidence: 99%

Logistics-Oriented Production Configuration Using the Example of MRO Service Providers

2022

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“…In order to expand the lifetime of complex capital goods, they must be maintained and overhauled regularly. That allows the monetary value to be continued into further service phases by maintenance, repair and overhaul (MRO) [1]. However, high stress on components and the resulting effects of wear on them lead to the partial or complete loss of their initially designed product properties.…”

Section: Introductionmentioning

confidence: 99%

“…Damage to the blades must be avoided to prevent the need for spare parts and successful regeneration. Also, the disassembly of the individual blades, as the last component when disassembling the engine, must be performed fastest possible to prevent unnecessarily long slack times for components disassembled prior [1,4]. Due to the uncertain condition of the blade-disk joint, the disassembly is carried out by manually or hydraulic hammering out.…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we present an approach to predict the effort for disassembly tasks based on the engine's usage. Using the example of HPT blades, we show the implementation and usage of a learning model that predicts tool dimension and disassembly time, increasing the workload information in order to plan and prioritize disassembly tasks [1]. However, since we cannot use components with a real product history for the experiment due to availability, the approach is validated on a replacement model.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Disassembly Parameters for Process Planning Based on Machine Learning

Blümel

Zander

Blankemeyer

et al. 2023

Lecture Notes in Production Engineering

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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 componentfriendly disassembly process adaptable to varying product conditions. In this article, we show the successful validation on a replacement model of an aircraft engine.

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Disassembly sequencing in the regeneration of complex capital goods

Cited by 2 publications

References 0 publications

Logistics-Oriented Production Configuration Using the Example of MRO Service Providers

Logistics-Oriented Production Configuration Using the Example of MRO Service Providers

Prediction of Disassembly Parameters for Process Planning Based on Machine Learning

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