Identification of Model Uncertainty via Optimal Design of Experiments Applied to a Mechanical Press

TL;DR

This paper presents a method to identify and quantify model uncertainty in engineering systems, particularly forming machines, by optimizing experimental design and statistical hypothesis testing to improve control and monitoring.

Contribution

It introduces a novel approach combining optimal experiment design, parameter identification, and hypothesis testing to detect and analyze model uncertainty in complex mechanical systems.

Findings

Optimal sensor placement reduces parameter estimate variance.

Confidence regions help distinguish data uncertainty from model uncertainty.

Method successfully applied to a 3D Servo Press component.

Abstract

In engineering applications almost all processes are described with the help of models. Especially forming machines heavily rely on mathematical models for control and condition monitoring. Inaccuracies during the modeling, manufacturing and assembly of these machines induce model uncertainty which impairs the controller's performance. In this paper we propose an approach to identify model uncertainty using parameter identification, optimal design of experiments and hypothesis testing. The experimental setup is characterized by optimal sensor positions such that specific model parameters can be determined with minimal variance. This allows for the computation of confidence regions in which the real parameters or the parameter estimates from different test sets have to lie. We claim that inconsistencies in the estimated parameter values, considering their approximated confidence ellipsoids as well, cannot be explained by data uncertainty but are indicators of model uncertainty. The proposed method is demonstrated using a component of the 3D Servo Press, a multi-technology forming machine that combines spindles with eccentric servo drives.