Robert O. Jung, F. Bleicher, S. Krall, Christian Juricek, Rainer Lottes, Karoline Steinschuetz, T. Reininger
{"title":"Cyber Physical Production Systems for Deep Drawing","authors":"Robert O. Jung, F. Bleicher, S. Krall, Christian Juricek, Rainer Lottes, Karoline Steinschuetz, T. Reininger","doi":"10.1115/1.4062903","DOIUrl":null,"url":null,"abstract":"\n Deep Drawing is an essential manufacturing technology for car body parts. High process stability is a key for reducing scrap and therefore the ecological footprint during the 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 an 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. These are firstly large outer parts with a complex geometry and high value, typically produced with tandem presses. Secondly smaller structural parts from high strength steel for the body in white (BIW), 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. A variation of 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 towards the implementation of the CPPS, material inflow and force sensors are installed in an industrially relevant deep drawing tool.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"1 1","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering-transactions of The Asme","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4062903","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Deep Drawing is an essential manufacturing technology for car body parts. High process stability is a key for reducing scrap and therefore the ecological footprint during the 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 an 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. These are firstly large outer parts with a complex geometry and high value, typically produced with tandem presses. Secondly smaller structural parts from high strength steel for the body in white (BIW), 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. A variation of 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 towards the implementation of the CPPS, material inflow and force sensors are installed in an industrially relevant deep drawing tool.
期刊介绍:
Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining