Sequential optimization of strip bending process using multiquadric radial basis function surrogate models

Surrogate models are used within the sequential optimization strategy for forming processes. A sequential improvement (SI) scheme is used to refine the surrogate model in the optimal region. One of the popular surrogate modeling methods for SI is Kriging. However, the global response of Kriging models deteriorates in some cases due to local model refinement within SI. This may be problematic for multimodal optimization problems and for other applications where correct prediction of the global response is needed. In this paper the deteriorating global behavior of the Kriging surrogate modeling technique is shown for a model of a strip bending process. It is shown that a Radial Basis Function (RBF) surrogate model with Multiquadric (MQ) basis functions performs equally well in terms of optimization efficiency and better in terms of global predictive accuracy. The local point density is taken into account in the model formulatio

Inverse identification of process variations for thin steel sheet bending

The stability of a metal forming production process is influenced by several sources of scatter such as variation of material and lubrication properties. Identification of the sources of variation is needed to optimize the process settings or to design a control strategy for the process. Many engineers point out sources of variation by experience, but in complex cases a computational identification algorithm may be used to investigate the process.\ud \ud When using parameter estimation in a control system, process forces can be used for the estimation. However, many parameters may influence the process forces. Therefore extensive models are needed to be able to identify the process parameters, including parameters such as tooling misalignment.\ud \ud In the current work, a thin steel flap bending process is studied. Measurements from an industrial press are used to identify the process parameters. A metamodel based inverse analysis procedure is used. The procedure is extended with proper orthogonal decomposition (POD) of the force curves to increase its convergence rate

Parameter reduction for the Yld2004-18p yield criterion

The Yld2004-18p yield criterion uses 18 parameters to define anisotropy for a full 3D stress state. It is demonstrated in this paper that dependencies between the parameters exist and for a given set of experimental data the parameters are not uniquely defined. Analysis of the yield function shows that two specific combinations of parameters do not contribute to the value of the yield function. Therefore, the number of parameters can be reduced to 16, without any loss of flexibility. Similarly, the number of parameters for the plane stress version of this yield criterion reduces from 14 to 1

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

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

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

Model-based control of strip bending in mass production

The bending angle in an industrial strip bending process for mass production is influenced by uncontrollable process and material variations like thickness, strength and friction. Most of these variations are not directly measurable in the production line. In a two stage bending operation, the force–time curve of the pre-bending step is measured and used to adapt the back-bending displacement. In this study several measurements from long test runs are evaluated and the feasibility of model-based control in metal forming is discussed. It is concluded that a model-based control scheme is required to reach an angular accuracy of 0.1°

A New Large Scale Distributed System: Object Distribution

We introduce in this work Object Distribution System, a distributed system based on distribution models used in everyday life (e.g. food distribution chains, newspapers, etc.). This system is designed to scale correctly in a wide area network, using weak consistency replication mechanisms. It is formed by two independent virtual networks on top of Internet, one for replicating objects and the other one to build distribution chains to be used by the first network. As in Internet some sites often become inaccessible due to latency, partitions and flashcrowd, objects in our system are accessed locally and updated off-line. It also provides methods for the classification of objects. This allows selective distribution, and provides order in the chaos that reigns nowadays in Internet. Distribution chains are build dynamically to provide end users with the objects they want to consume, while making good use of available resources