Sampling-based Tolerance-Cost Optimization of Systems with Interrelated Key Characteristics
Martin Hallmann, Benjamin Schleich, Sandro Wartzack
Abstract
Robustness is a key factor contributing to a high functionality of technical systems under uncertainty. In this context, existing methodologies, such as Axiomatic Design and Quality Function Deployment, can help to identify coupled functional requirements which significantly affect the robustness of a system. However, it is often not possible to decouple all in the final product design. As a consequence, multiple interrelated tolerance chains have to be considered in the subsequent tolerance design leading to multi-constrained or multi-objective optimization problems. Despite their significant influence on the optimization process and its results, interrelated tolerance chains have not been studied in detail yet, especially in the context of sampling-based tolerance-cost optimization. Moreover, a holistic framework for the tolerance-cost optimization of systems with multiple key characteristics is missing so far. In order to close that gap, this paper presents a framework to consider multiple key characteristics in both least-cost and best-quality tolerance-cost optimization using sampling techniques for tolerance analysis. Therefore, interrelated tolerance chains and their effects on the optimization process in terms of the definition and handling of multiple constraints and objectives are discussed in detail. The proposed research aims to bring all important aspects together in one common framework. Thus, it is supposed to help researchers and practitioners to properly define and solve the tolerance optimization problem. In order to show its benefits and applicability, it is applied to an illustrative case study. The novelty of this paper is to present a comprehensive method to support the tolerance engineer in creating a robust and optimal tolerance design of products with multiple interrelated key characteristics.