Process Signatures–Knowledge-based approach towards function-oriented manufacturing Article Swipe
Bernhard Karpuschewski
,
Tobias Kinner-Becker
,
A. Klink
,
L. Langenhorst
,
Joachim Mayer
,
Daniel Meyer
,
Tim Radel
,
Stefanie Reese
,
Jens Sölter
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.procir.2022.01.001
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.procir.2022.01.001
Surface layer properties are of crucial importance for the functional performance of manufactured components. For the vision of a function-oriented production, the relevant surface layer properties for the considered functional properties (e.g. fatigue strength, corrosion resistance) are to be generated by an adequate selection and adjustment of manufacturing processes. In order to accomplish this in a resource-efficient manner, extensive knowledge about the impact of manufacturing processes on the workpiece material is required. During the process, the material responds to external loads (e.g. process forces, heat fluxes in the contact zone) which induce internal material loads (e.g. strain, temperatures, chemical potential), which finally are responsible for the resulting material modifications (e.g. changes in hardness, residual stress, chemical composition). The correlations between material modifications and internal material loads are aggregated in Process Signatures. This keynote shows exemplary Process Signature Components and their use in generating targeted surface layer properties. The key scientific challenges are developments towards a deeper understanding of the underlying mechanisms leading to modifications in the microstructure and the derivation of suitable descriptive quantities for the relevant internal material loads, which can usually only be obtained from process simulations. Based on this, a prediction and knowledge-based generation of depth profiles of surface layer properties is possible, wherefore first approaches are presented in this keynote.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procir.2022.01.001
- OA Status
- diamond
- Cited By
- 15
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285269272
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4285269272Canonical identifier for this work in OpenAlex
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https://doi.org/10.1016/j.procir.2022.01.001Digital Object Identifier
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Process Signatures–Knowledge-based approach towards function-oriented manufacturingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
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Bernhard Karpuschewski, Tobias Kinner-Becker, A. Klink, L. Langenhorst, Joachim Mayer, Daniel Meyer, Tim Radel, Stefanie Reese, Jens SölterList of authors in order
- Landing page
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https://doi.org/10.1016/j.procir.2022.01.001Publisher landing page
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.procir.2022.01.001Direct OA link when available
- Concepts
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Process (computing), Material properties, Residual stress, Function (biology), Mechanical engineering, Layer (electronics), Materials science, Process engineering, Computer science, Engineering, Nanotechnology, Composite material, Biology, Operating system, Evolutionary biologyTop concepts (fields/topics) attached by OpenAlex
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15Total citation count in OpenAlex
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2025: 4, 2024: 7, 2023: 4Per-year citation counts (last 5 years)
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19Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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