An integration design optimization framework of robust design, axiomatic design, and reliability‐based design

Abstract

Robust design, axiomatic design, and reliability-based design provide effective approaches to deal with quality problems, and their integration will achieve better quality improvement. An integration design optimization framework of robust design, axiomatic design, and reliability-based design is proposed in this paper. First, the fitted response model of each quality characteristic is obtained by response surface methodology and the mean square error (MSE) estimation is given by a second-order Taylor series approximation expansion. Then the multiple quality characteristics robust design model is developed by the MSE criteria. Finally, the independence axiom constraints for decoupling and reliability constraints are integrated into the multiple quality characteristics robust design model, and the integration design optimization framework is formulated, where the weighted Tchebycheff approach is adopted to solve the multiple objective programming. An illustrative example is presented at the end, and the results show that the proposed approach can obtain better trade-offs among conflicting quality characteristics, variability, coupling degree and reliability requirements.

Mechanisms and coherences of robust design methodology: a robust design process proposal

“…Lijuan et al (2011) propose a method to integrate Robust Design, Axiomatic Design and reliability-based design to improve the efficiency of the optimization. Various case studies on RDO were conducted.…”

Robust Parameter Design of an EDM Process

“…Robinson et al 18 and Arvidsson and Gremyr 19 reported a comprehensive review of robust parameter design. Recent pertinent research studies include Rodriguez et al, 20 Matsuura et al 21 and Lijuan et al 22 For measuring the response variability, Taguchi 23,24 proposed the use of signal-to-noise (S/N) ratios that he derived on the basis of loss functions that penalize even small deviations from the target performance level. The S/N formula depends on the goal of the experiment; however, three formulae are considered standard and are generally applicable when the response can be classified as 'Target-is-Best', 'Larger-is-Better' or 'Smaller-is-Better'.…”

Web-based virtual learning for digital manufacturing fundamentals for automotive workforce training

“…Limitations and constraints related to the production are enabling Six Sigma methodologies to be implemented in the design phase (Aksoy and Dinçmen, 2011). Designing the robust product involves using manufacturing and assembly-based information in early design stages (Lijuan et al, 2011). Digital manufacturing is a term that is related to wide spectrum of digital models and methods that are dealing with every aspect with PLM.…”