Metamodel variability analysis combining bootstrapping and validation techniques

Research on metamodel-based optimization has received considerably increasing interest in recent years, and has found successful applications in solving computationally expensive problems. The joint use of computer simulation experiments and metamodels introduces a source of uncertainty that we refer to as metamodel variability. To analyze and quantify this variability, we apply bootstrapping to residuals derived as prediction errors computed from cross-validation. The proposed method can be used with different types of metamodels, especially when limited knowledge on parameters’ distribution is available or when a limited computational budget is allowed. Our preliminary experiments based on the robust version of the EOQ model show encouraging results

Observability of quality features of sheet metal parts based on metamodels

Deep drawn sheet metal parts are increasingly designed to the feasibility limit, thus achieving a robust process is often challenging. The fluctuation of process and material properties often leads to robustness problems. Especially skid impact lines can cause visible changes of the surface fine structure even after painting. Numerical simulations are used to detect critical regions and the influences on the skid impact lines. To enhance the agreement with the real process conditions, the measured material data and the force distribution are taken into account. The simulation metamodel contains the virtual knowledge of a particular forming process, which is determined based on a series of finite element simulations with variable input parameters. Based on these metamodels, innovative process windows can be displayed to determine the influences on the critical regions and on skid impact lines. By measuring the draw-in of the part, sensor positions can be identified. Each sensor observes the accordant quality criterion and is hence able to quantify potential splits, insufficient stretching, wrinkles or skid impact lines. Furthermore the virtual draw-in sensors and quality criteria are particularly useful for the assessment of the process observation of a subsequent process control

Coordination of Coupled Black Box Simulations in the Construction of Metamodels

This paper introduces methods to coordinate black box simulations in the construction of metamodels for situations in which we have to deal with coupled black boxes.We de.ne three coordination methods: parallel simulation, sequential simulation and sequential modeling.To compare these three methods we focus on .ve aspects: throughput time, .exibility, simulated product designs, coordination complexityand the use of prior information.Special attention is given to the throughput time aspect.For this aspect we derive mathematical formulas and we give relations between the throughput times of the three coordination methods.At the end of this paper we summarize the results and give recommendations on the choice of a suitable coordination method.simulation;simulation models;coordination;black box;metamodels

Regression Models and Experimental Designs: A Tutorial for Simulation Analaysts

This tutorial explains the basics of linear regression models. especially low-order polynomials. and the corresponding statistical designs. namely, designs of resolution III, IV, V, and Central Composite Designs (CCDs).This tutorial assumes 'white noise', which means that the residuals of the fitted linear regression model are normally, independently, and identically distributed with zero mean.The tutorial gathers statistical results that are scattered throughout the literature on mathematical statistics, and presents these results in a form that is understandable to simulation analysts.metamodels;fractional factorial designs;Plackett-Burman designs;factor interactions;validation;cross-validation

Design and Analysis of Monte Carlo Experiments

monte carlo experiments;simulation models;mathematical analysis;sensitivity analysis;experimental design