Abstract
Feedback and process control of metalforming processes has received increasing attention the lastdecade. Basically there exist four control philosophies; control ofprocess parameters during the punch stroke, iterative learning control(based on historical data), a combination iterative learning andfeedback control and finally feed-forward control. The present work willpresent three different control schemes which all are based onfeedback philosophy i.e. control during the punch stroke or iterativelearning control, where process parameters are updated according toprocess history. The three control schemes are tested using a non-linear finite element model of a square deep-drawing and finallypros and cons are discussed based on the numerical results.
Publisher
Trans Tech Publications, Ltd.
Reference10 articles.
1. B Endelt. Design strategy for optimal iterative learning control applied on a deep drawing process. International Journal of Advanced Manufacturing Technology, 88(1):3-18, (2017).
2. B. Endelt. Designing an iterative learning control algorithm based on process history using limited post- process geometrical information. In Proceedings of the IDDRG 2017 Conference, (2017).
3. B. Endelt and J. Danckert. Iterative learning and feedback control applied on a deep drawing process. International Journal of Material Forming, 3(SUPPL. 1):25-28, (2010).
4. B. Endelt and W. Volk. Designing an iterative learning control algorithm based on process history - using limited post- process geometrical information. In Proceedings of the IDDRG 2013 Conference, Best in Class Stamping, 2013. submitted for presentation at the IDDRG 2013 Conference.
5. Benny Endelt, Søren Tommerup, and Joachim Danckert. A novel feedback control system controlling the material flow in deep drawing using distributed blank-holder force. Journal of Materials Processing Technology, 213(1):36 - 50, (2013).
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献