Feedback control of sheet metal forming operations has been an active research field the last two decades and highly advanced control algorithms have been proposed - controlling both the total blank-holder force and in some cases also the distribution of the blank-holder force. However, there is a number of obstacles which need to be addressed before an industrial implementation is possible, e.g. the proposed control algorithms are often limited by the ability to sample process data with both sufficient accuracy and robustness - this lack of robust sampling technologies is one of the main barriers preventing successful industrial implementation. Secondly limitation in the current press designs; many of the presses currently used in industry only offer limited opportunities to change the blank-holder force during the punch stroke. Even if the press has the opportunity to change the blank-holder force, the reaction speed may be insufficient compared to the production rate in an industrial application. We propose to design an iterative learning control (ILC) algorithm which can control and update the blank-holder force as well as the distribution of the blank-holder force based on limited geometric data from previously produced parts. The proposed algorithm was exemplarily tested on a square cup.
Iddrg 2013 Conference Proceedings Towards Zero Failure Production Methods by Advanced Modeling Techniques and a Process Integrated Virtual Control, 2013, p. 69-74
deep drawing, iterative learning control, run-2-run control, distributed blank-holder force, process control, process robustness, finite element.