The accuracy of a metal forming process is highly influenced by the variation of the process input, such as variation of friction and material properties. Therefore it may be required to decrease the input variation to meet the desired accuracy. However, this may increase the production costs, since stricter requirements generally come with a higher price tag. Other solutions may be to design the process in such a way that it becomes less sensitive to the input variation, or to implement a control scheme in the production line. Adding sensors to measure the state of the production process and actuators to change the process settings during production allows for a drastic increase of the production accuracy.\ud
In this study a numerical comparison is made between different methods to control a thin strip bending process with an over-bending and a back-bending stage. The aim is to implement the method in a mass production line with a production speed of 100 products per minute, which demands for fast measurement, processing and actuation. A discrete control scheme is used, meaning that the process settings can only be adapted in between the process stages. The adaptable control parameter is the amount of back-bending. In the case of the strip bending process, the angle of the measured strip may be used to adapt the angle of the following strip. However, the accuracy of such a control scheme is limited by product-to-product variation. Therefore the force of the over-bending stage is measured and used to construct a predictive model of the process based on measured process data. Hence, the final angle of the flap can be predicted by measuring the force at the first stage of the process. Different factors influence the effectiveness of the control methods: the size and autocorrelation of the input variation, the noise of the measurement system and the predictive ability of the predictive model. A qualitative study on the influence of these factors on different control methods is given in this paper
