{"title":"Sensor integration for process control in deep drawing","authors":"Robert Oliver Jung","doi":"10.21741/9781644903131-155","DOIUrl":null,"url":null,"abstract":"Abstract. In the context of increasing resource efficiency and profitability, deep drawing can be improved using a digital twin and closed-loop process control. Cyber-physical production systems (CPPS) enable data capture and analysis for an autonomous optimization of the manufacturing process. In this work reference sensor signals are used to control the force and material flow with hydraulic actuators between the blank holder and the die. A novel model-based optimization method is proposed to determine the best sensor location, allowing for standardized evaluation and reduced integration time. FE simulations and forming trials are conducted for validation. The findings indicate time and resource savings through an efficient sensor implementation in deep drawing tools for process control.","PeriodicalId":515987,"journal":{"name":"Materials Research Proceedings","volume":"129 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Research Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21741/9781644903131-155","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract. In the context of increasing resource efficiency and profitability, deep drawing can be improved using a digital twin and closed-loop process control. Cyber-physical production systems (CPPS) enable data capture and analysis for an autonomous optimization of the manufacturing process. In this work reference sensor signals are used to control the force and material flow with hydraulic actuators between the blank holder and the die. A novel model-based optimization method is proposed to determine the best sensor location, allowing for standardized evaluation and reduced integration time. FE simulations and forming trials are conducted for validation. The findings indicate time and resource savings through an efficient sensor implementation in deep drawing tools for process control.
摘要在提高资源利用效率和盈利能力的背景下,可以利用数字孪生和闭环过程控制来改进拉深工艺。网络物理生产系统(CPPS)可进行数据采集和分析,从而自主优化生产过程。在这项工作中,参考传感器信号被用于控制坯料支架和模具之间液压致动器的力和材料流。提出了一种基于模型的新型优化方法来确定最佳传感器位置,从而实现标准化评估并缩短集成时间。为进行验证,还进行了 FE 模拟和成型试验。研究结果表明,通过在深拉工具中有效安装传感器来实现过程控制,可以节省时间和资源。