Optimization and control of metal forming processes

Inevitable variations in process and material properties limit the accuracy of metal forming processes. Robust optimization methods or control systems can be used to improve the production accuracy. Robust optimization methods are used to design production processes with low sensitivity to the disturbances in the process. Efficient robust optimization procedures have been developed to determine a robust design of the production process with limited computational resources. The procedure is based on the use of an approximation of a finite element (FE) model and sequential improvement of the approximate model through additional evaluations of the FE model. The second approach for improvement of production accuracy is the use of control systems. The resulting improvement of production accuracy depends on the rate of variation in the process disturbances. Slowly varying disturbances can be controlled with feedback control, whereas disturbances which vary from product to product require feedforward control to be eliminated. When using feedforward control, process measurements are used to estimate the effect of product-to-product variations on the final product. In this work, it is studied whether force measurements can be used in a process estimator for feedforward control. Such a process estimator may either be built based on historical data from the process or may be determined with an FE model of the process. The effectiveness of control of metal forming processes is studied based on data from a demonstrator process with multiple forming stages. Several large datasets are used to investigate the feasibility of feedforward control for the demonstrator process. A significant part of the variations in the process can be predicted based on force measurements using LASSO regression. Another approach for building a process estimator is using an FE model of the process. The FE model is used to identify the causes of small variations in the force measurements and to predict their effect on the final properties of the product. The proposed procedure for real-time parameter estimation involves proper orthogonal decomposition, model interpolation and Bayesian estimation

Estimating product-to-product variations in metal forming using force measurements

The limits of production accuracy of metal forming processes can be stretched by the development of control systems for compensation of product-to-product variations. Such systems require the use of measurements from each semi-finished product. These measurements must be used to estimate the final quality of each product. We propose to predict part of the product-to-product variations in multi-stage forming processes based on force measurements from previous process stages. The reasoning is that final product properties as well as process forces are expected to be correlated since they are both affected by material and process variation. In this study, an approach to construct a moving window process model based on historical data from the process is presented. These regression models can be built and updated in real-time during production. The approach is tested with data from a demonstrator process with cutting, deep drawing and bending stages. It is shown that part of the product-to-product variations in the process can be predicted with the developed process model

Sequential improvement for robust optimization using an uncertainty measure for radial basis functions

The performance of the sequential metamodel based optimization procedure depends strongly on the chosen building blocks for the algorithm, such as the used metamodeling method and sequential improvement criterion. In this study, the effect of these choices on the efficiency of the robust optimization procedure is investigated. A novel sequential improvement criterion for robust optimization is proposed, as well as an improved implementation of radial basis function interpolation suitable for sequential optimization. The leave-one-out cross-validation measure is used to estimate the uncertainty of the radial basis function metamodel. The metamodeling methods and sequential improvement criteria are compared, based on a test with Gaussian random fields as well as on the optimization of a strip bending process with five design variables and two noise variables. For this process, better results are obtained in the runs with the novel sequential improvement criterion as well as with the novel radial basis function implementation, compared to the runs with conventional sequential improvement criteria and kriging interpolation

The effect of tooling deformation on process control in multistage metal forming

Forming of high-strength steels leads to high loads within the production process. In multistage metal forming, the loads in different process stages are transferred to the other stages through elastic deformation of the stamping press. This leads to interactions between process steps, affecting the process forces in each stage and the final geometry of the product. When force measurements are used for control of the metal forming process, it is important to understand these interactions. In his work, interactions within an industrial multistage forming process are investigated. Cutting, deepdrawing, forging and bending steps are performed in the production process. Several test runs of a few thousand products each were performed to gather information about the process. Statistical methods are used to analyze the measurements. Based on the cross-correlation between the force measurements of different stages, it can be shown that the interactions between the process steps are caused by elastic deformation of the tooling and the stamping press

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