Design of the flexibly-reconfigurable roll forming process by a progressively-improving goal seeking approach

Abstract In order to design the flexibly-reconfigurable roll forming (FRRF) process, i.e., to find the set of process parameters that lead to the target 3D surface, an efficient approach is suggested in the present study that can significantly improve the production rate by FRRF where each process design is usually applied for a few or a single forming. According to this approach, the results of the already performed simulations and experiments are used as the sample data for constructing a surrogate model that receives a set of process parameters and returns the longitudinal radius. Then, the genetic algorithm (GA) is hired to explore the surrogate model to predict a set of process parameters that would lead to the target longitudinal radius. This is accomplished by a goal seeking approach and maximization of a conditional likelihood function. The predicted set of the process parameters is given to an efficient finite element method (FEM) to evaluate the corresponding longitudinal radius, and the new input-output pair is added to the sample data to improve the subsequent modeling and predictions. Then, the sequential modeling-prediction-evaluation (MPE) steps are repeated until the output is close enough to the target value. Since the results of all the simulations and experiments are saved in a database to be used for the next designs, the efficiency of the procedure is progressively improved because the surrogate model becomes more informative that in turn enables more exact predictions. However, constructing the surrogate model becomes slower as the number of sample points increases. For preventing this phenomenon, a modified version of the Morris-Mitchel criterion is utilized for subset sampling that selects a desired number of sample points with maximum space-filling property. In order to examine the suggested procedure, an initial database containing 54 sample points collected from experimental study is constructed. Then, the suggested approach is hired to reproduce 3 of the samples which are randomly selected from the database. The results show that this method can be used for design of FRRF process with a few or even one MPE that can significantly improve the production rate in this process.

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