<div class="csl-bib-body">
<div class="csl-entry">Jung, R. O., Bleicher, F., Krall, S., Juricek, C., Lottes, R., Steinschütz, K., & Reininger, T. (2023). Cyber Physical Production Systems for Deep Drawing. <i>Journal of Manufacturing Science and Engineering</i>, <i>145</i>(10), Article 101006. https://doi.org/10.1115/1.4062903</div>
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dc.identifier.issn
1087-1357
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/191888
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dc.description.abstract
Deep Drawing is an essential manufacturing technology for car body parts. High process stability is a key to reducing scrap and therefore the ecological footprint during production. To deal with an unknown fluctuation of the incoming material properties and uncertainties considering the friction, an adaptive process needs to be implemented. Various approaches have been pursued in the past, but not all of them are suited for industrial series production with high demands for equipment durability, cost efficiency, and flexibility. For this reason, a new concept for cyber-physical production systems (CPPS) in deep drawing is presented, linking the data from the simulation, tool, press, material, and finished part quality. Two common application scenarios are distinguished. First, these are large outer parts with a complex geometry and high value, typically produced with tandem presses. Second, smaller structural parts from high-strength steel for the body in white (BIW) are usually produced through a transfer or progressive die. Non-destructive material testing, supplier material certificates, and data measured directly in the forming tool are considered regarding the input. The servo curve and blank holder force (BHF) operate as control instances. Within the two application scenarios, a reactive and a preventive solution are characterized. As a first step toward the implementation of the CPPS, material inflow, and force sensors are installed and tested in an industrially relevant deep drawing tool.
en
dc.language.iso
en
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dc.publisher
ASME
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dc.relation.ispartof
Journal of Manufacturing Science and Engineering
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dc.subject
Sheet metal forming
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dc.subject
Deep drawing
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dc.subject
Control and automation
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dc.subject
Production systems optimization
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dc.subject
Sensors
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dc.subject
Sustainable manufacturing
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dc.title
Cyber Physical Production Systems for Deep Drawing