Replication Data for: Accounting for Non-normal Distribution of Input variables and Their Correlations in Robust Optimization

ExperimentalData.mat includes the data obtained via tensile tests in three directions from 49 different samples gathered from different coils. These are the raw data used to describe the input to the optimization problem. Principal component analysis is done after subtracting their mean values. OptimizationDOE.mat includes the DOE made by LHS sampling in combined noise and design variable space, SimulationResults.mat includes the finite element simulation results that are performed on DOE points. These data are used to build the Kriging metamodel for optimization using the above-mentioned material data. WindData.mat includes the collected wind data from a weather station described in the manuscript

Replication Data for: Accounting for Non-normal Distribution of Input variables and Their Correlations in Robust Optimization

ExperimentalData.mat includes the data obtained via tensile tests in three directions from 49 different samples gathered from different coils. These are the raw data used to describe the input to the optimization problem. Principal component analysis is done after subtracting their mean values. OptimizationDOE.mat includes the DOE made by LHS sampling in combined noise and design variable space, SimulationResults.mat includes the finite element simulation results that are performed on DOE points. These data are used to build the Kriging metamodel for optimization using the above-mentioned material data. WindData.mat includes the collected wind data from a weather station described in the manuscript

Robustimizer design of experiments database

This dataset includes the design of experiments that are used in the simulations performed using Robustimizer.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

From specified product tolerance to acceptable materialand process scatter: an inverse robust optimization approach

Production efficiency in metal forming processes can be improved by implementing robust optimization. In a robustoptimization method, the material and process scatter are taken into account to predict and to minimize the product variabilityaround the target mean. For this purpose, the scatter of input parameters are propagated to predict the product variability.Consequently, a design setting is selected at which product variation due to input scatter is minimized. If the minimumproduct variation is still higher than the specific tolerance, then the input noise must be adjusted accordingly. For examplethis means that materials with a tighter specification must be ordered, which often results in additional costs. In this article,an inverse robust optimization approach is presented to tailor the variation of material and process noise parameters basedon the specified product tolerance. Both robust optimization and tailoring of material and process scatter are performed onthe metamodel of an automotive part. Although the robust optimization method facilitates finding a design setting at whichthe product to product variation is minimized, the tighter product tolerance is only achievable by requiring less scatter ofnoise parameters. It is shown that the presented inverse approach is able to predict the required adjustment for each noiseparameter to obtain the specified product tolerance. Additionally, the developed method can equally be used to relax materialspecifications and thus obtain the same product tolerance, ultimately resulting in a cheaper process. A strategy for updatingthe metamodel on a wider (noise) base is presented and implemented to obtain a larger noise scatter while maintaining thesame product tolerance

Accounting for non-normal distribution of input variables and their correlations in robust optimization

In this work, metamodel-based robust optimization is performed using measured scatter of noise variables. Principal component analysis is used to describe the input noise using linearly uncorrelated principal components. Some of these principal components follow a normal probability distribution, others however deviate from a normal probability distribution. In that case, for more accurate description of material scatter, a multimodal distribution is used. An analytical method is implemented to propagate the noise distribution via metamodel and to calculate the statistics of the response accurately and efficiently. The robust optimization criterion as well as the constraints evaluation are adjusted to properly deal with multimodal response. Two problems are presented to show the effectiveness of the proposed approach and to validate the method. A basketball free throw in windy weather condition and forming of B-pillar component are presented. The significance of accounting for non-normal distribution of input variables using multimodal distributions is investigated. Moreover, analytical calculation of response statistics, and adjustment of the robust optimization problem are presented and discussed