Regeneration komplexer Investitionsgüter

Eickemeyer, Steffen C.; Herde, Felix

doi:10.3139/104.110836

Cited by 12 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ran et al, for example, summarized in their work the primary purpose of predictive maintenance as the reduction of costs, elimination of unexpected downtime and the improvement of availability and reliability of systems [8]. As an approach, Eickemeyer developed a damage library to predict the effort for the regeneration of capital goods, such as aircraft engines [9]. He defined temporal model categories to optimally perform workforce, resources or time planning.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Disassembly Parameters for Process Planning Based on Machine Learning

Blümel

Zander

Blankemeyer

et al. 2023

Lecture Notes in Production Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

“…As shown by Eickemeyer in [9], using machine learning (Bayesian network) can predict the regeneration effort. However, in this work, we use the replicated model of the solidified joints [10] to train the learning model to predict disassembly tools and times.…”

Section: Setupmentioning

confidence: 99%

Prediction of Disassembly Parameters for Process Planning Based on Machine Learning

Blümel

Zander

Blankemeyer

et al. 2023

Lecture Notes in Production Engineering

View full text Add to dashboard Cite

show abstract

“…The need for robustness is mainly dependent on the remaining, not anticipated (load) uncertainty left in the process. A reduction of this uncertainty is, among others, possible by analysing load data from previous orders or field data using modern methods of predictive data analytics [21,71]. The remaining load uncertainty that cannot be eliminated until the start of order processing can be modelled as a statistical distribution and thus enables the derivation of different load scenarios [72].…”

Section: Procedures For Deriving a Structurementioning

confidence: 99%