Model-Based Dimensioning of Multistage Processes Regarding Multiple Criteria

Denkena, Berend; Henning, Helge; Henjes, Jan

doi:10.1007/978-3-642-10430-5_80

Cited by 3 publications

(1 citation statement)

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“…This is a realistic use case as there are typically multiple alternatives to manufacture one part [37,38]. Generally, the result r of manufacturing process i can be expressed as a function of a number j of process parameters p j and of the result of its preceding manufacturing processes r i À 1 [39]:…”

Section: Features For Adaptive Repair Process Chainsmentioning

confidence: 99%

Programming of adaptive repair process chains using repair features and function blocks

Spöcker

Schreiner

Huwer

et al. 2015

Journal of Computational Design and Engineering

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The current trends of product customization and repair of high value parts with individual defects demand automation and a high degree of flexibility of the involved manufacturing process chains. To determine the corresponding requirements this paper gives an overview of manufacturing process chains by distinguishing between horizontal and vertical process chains. The established way of modeling and programming processes with CAx systems and existing approaches is shown. Furthermore, the different types of possible adaptions of a manufacturing process chain are shown and considered as a cascaded control loop. Following this it is discussed which key requirements of repair process chains are unresolved by existing approaches. To overcome the deficits this paper introduces repair features which comprise the idea of geometric features and defines analytical auxiliary geometries based on the measurement input data. This meets challenges normally caused by working directly on reconstructed geometries in the form of triangulated surfaces which are prone to artifacts. Embedded into function blocks, this allows the use of traditional approaches for manufacturing process chains to be applied to adaptive repair process chains

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Section: Features For Adaptive Repair Process Chainsmentioning

confidence: 99%