Inline control of a strip bending process in mass production

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

Adaptive process control strategy for a two-step bending process

A robust production is an important goal in sheet metal forming in order to make the process outcome insensitive to variations in input and process conditions. This would guarantee a minimum number of defects and reduced press downtime. However, for com-plex parts it is difficult to achieve robust settings. Parts without defects can only be real-ized if the process parameters are adapted to the changed conditions.\ud In this paper, an approach for adaptive process control is presented, taking the uncertain-ties and tolerances of the process and material into consideration. The proposed control approach combines feedback and feed-forward control strategies. The most significant improvement is to incorporate feed-forward control with knowledge about the system (also known as predictive models). To create these models high fidelity numerical models have been created. Furthermore, a procedure is presented to update the coefficients of the predictive model to adapt it to the actual process state.\ud To evaluate the control strategy prior to its implementation, a testing environment has been developed. Different test scenarios for common states of the process have been generated to evaluate the improvement of the proposed control strategy

Adaptive process control strategy for a two-step bending process

A robust production is an important goal in sheet metal forming in order to make the process outcome insensitive to variations in input and process conditions. This would guarantee a minimum number of defects and reduced press downtime. However, for com-plex parts it is difficult to achieve robust settings. Parts without defects can only be real-ized if the process parameters are adapted to the changed conditions. In this paper, an approach for adaptive process control is presented, taking the uncertain-ties and tolerances of the process and material into consideration. The proposed control approach combines feedback and feed-forward control strategies. The most significant improvement is to incorporate feed-forward control with knowledge about the system (also known as predictive models). To create these models high fidelity numerical models have been created. Furthermore, a procedure is presented to update the coefficients of the predictive model to adapt it to the actual process state. To evaluate the control strategy prior to its implementation, a testing environment has been developed. Different test scenarios for common states of the process have been generated to evaluate the improvement of the proposed control strategy