Development of a variance prioritized multiresponse robust design framework for quality improvement (original) (raw)
Related papers
1995
In this paper we introduce a comprehensive and rigorous robust design procedure to overcome some limitations of the current approaches. A comprehensive approach is general enough to model the two major types of robust design applications, namely, • robust design associated with the minimization of the deviation of performance caused by the deviation of noise factors (uncontrollable parameters), AND • robust design due to the minimization of the deviation of performance caused by the deviation of control factors (design variables). We achieve mathematical rigor by using, as a foundation, principles from the design of experiments and optimization. Specifically, we integrate the Response Surface Method (RSM) with the compromise Decision Support Problem (DSP). Our approach is especially useful for design problems where there axe no closed-form solutions and system performance is computationally expensive to evaluate. The design of a solar powered irrigation system is used as an example. Our focus in this paper is on illustrating our approach rather than on the results per se. I OUR FRAME OF REFERENCE The fundamental principle in robust design is to improve the quality of a product by minimizing the effects of variation without eliminating these causes. There axe two broad categories, of robust design based on the source of variation: Type I Robust Design-minimizing performance deviation caused by deviation of noise factors (uncontrollable parameters). Type II Robust Design-minimizing performance deviation caused by control factor deviation (design variables). Related to this view, Otto and Antonsson, 1991, argue the necessity of incorporating constraints in robust design.
Efficient Methods for Industrial Robust Design Optimization
In this paper suitable methods for robust design optimization are presented and discussed. Starting with an initial sensitivity analysis, the important design parameters can be identified and the optimization task can be significantly simplified. Taking into account uncertainties, the optimization task becomes more challenging. Instead of deterministic response values, uncertain model responses need to be analyzed. For a successful implementation this analysis requires the estimation of the probabilities of rare events. With help of a variance-based and reliability-based robustness evaluation, the required safety level can be implied in the optimization process and verified for the final design. Starting with the accompanying paper, we introduced the overview of robust design optimization and illustrated practical application. In this paper we continue with a more detailed view on the methodology.
Single- and multiobjective optimization problems in robust parameter design
Sadhana, 1997
This paper reviews the evolution of off-line quality engineering methods with respect to one or more quality criteria, and presents some recent results. The fundamental premises that justify the use of robust product/process design are established with an illustrative example. The use of designed experiments to model quality criteria and their optimization is briefly reviewed. The fact that most design-for-quality problems involve multiple quality criteria motivates the development of multiobjective optimization techniques for robust parameter design. Two situations are considered: one in which response surface models for the quality characteristics can be obtained using regression and considered over a continuous factor space, and one in which the problem scenario and the experiment permit only discrete parameter settings for the design factors. In the former scenario, a multiobjective optimization technique based on the reference-point method is presented; this technique also incorporates an inference mechanism to deal with uncertainty in the response surface models caused by finite, noisy data. In the discrete-factors scenario, an efficient method to reduce computational complexity for a class of models is presented.
Robust design and reliability-based design optimization
2005
A large number of problems in manufacturing processes, production planning, finance and engineering design require an understanding of potential sources of variations and quantification of the effect of variations on product behavior and performance. Traditionally, in engineering problems uncertainties have been formulated only through coarse safety factors. Such methods often lead to overdesigned products. Furthermore, the deterministic optimization algorithms tend to push an optimized design towards the boundaries of the design space. This paper reviews theories and methodologies that have been developed to solve optimization problems under uncertainties. In the first part the paper gives an overview over the state of the art in stochastic optimization methods such as robust design and reliability-based design optimization. In addition, global response surface techniques as well as genetic programming in combination with first order reliability methods in reliability-based optimization are discussed. Two numerical examples from structural analysis under static and dynamic loading conditions show the applicability of these concepts. The probabilistic and structural analysis tasks are performed with ANSYS DesignXplorer and OptiSLang software packages.
Reliability-based robust design optimization: A comparative study
2011
Reliability-based robust design optimization (RBRDO) is one of the most essential tools developed in recent years to improve the quality and reliability of the products at an early design stage. This paper presents a comparative study of different formulation approaches of RBRDO models and their performances. The paper also proposes an evolutionary multi-objective genetic algorithm (MOGA) to one of the promising hybrid quality loss functions (HQLF)-based RBRDO model. The enhanced effectiveness of the RBRDO model is demonstrated by optimizing suitable example.
A trade-off function to tackle robust design problems in engineering
Journal of Engineering Design, 2012
In this paper, an original trade-off function is proposed to deal with robust design in engineering. Robust design in engineering aims at sizing systems with a low variation in all performance measures under uncertainties. It is mainly related to the generation and evaluation of candidate solutions. However, it is also suitable to consider the robustness in the decision-making process to ensure the selection of the most preferred design in the early stages of the design process. The purpose of our research work consists in tackling the robust design problem as a trade-off between two design objectives: (i) improve the global level of performance of the system and (ii) reduce the sensitivity of the performance with regard to uncertainty sources. The robustness of the candidate solutions is evaluated by a trade-off function modelling the designer's intention with an a priori articulation of preferences. Design objective formulation is based on the concept of desirability function and indices. Candidates solutions are ranked according to their ability of reducing the design sensitivity while remaining highly desirable. The developed approach is illustrated on an example of a truss structure design problem which is numerically solved by a genetic algorithm.
A Procedure for Robust Design: Minimizing Variations Caused by Noise Factors and Control Factors
Journal of Mechanical Design, 1996
In this paper, we introduce a small variation to current approaches broadly called Taguchi Robust Design Methods. In these methods, there are two broad categories of problems associated with simultaneously minimizing performance variations and bringing the mean on target, namely, Type I—minimizing variations in performance caused by variations in noise factors (uncontrollable parameters). Type II—minimizing variations in performance caused by variations in control factors (design variables). In this paper, we introduce a variation to the existing approaches to solve both types of problems. This variation embodies the integration of the Response Surface Methodology (RSM) with the compromise Decision Support Problem (DSP). Our approach is especially useful for design problems where there are no closed-form solutions and system performance is computationally expensive to evaluate. The design of a solar powered irrigation system is used as an example.
Quality Utility—A Compromise Programming Approach to Robust Design
Journal of Mechanical Design, 1999
In robust design, associated with each quality characteristic, the design objective often involves multiple aspects such as “bringing the mean of performance on target” and “minimizing the variations.” Current ways of handling these multiple aspects using either the Taguchi’s signal-to-noise ratio or the weighted-sum method are not adequate. In this paper, we solve bi-objective robust design problems from a utility perspective by following upon the recent developments on relating utility function optimization to a Compromise Programming (CP) method. A robust design procedure is developed to allow a designer to express his/her preference structure of multiple aspects of robust design. The CP approach, i.e., the Tchebycheff method, is then used to determine the robust design solution which is guaranteed to belong to the set of efficient solutions (Pareto points). The quality utility at the candidate solution is represented by means of a quadratic function in a certain sense equivalent...
Computing trade-offs in robust design: Perspectives of the mean squared error
Computers & Industrial Engineering, 2011
Researchers often identify robust design as one of the most effective engineering design methods for continuous quality improvement. When more than one quality characteristic is considered, an important question is how to trade off robust design solutions. In this paper, we consider a bi-objective robust design problem for which Pareto solutions of two quality characteristics need to be obtained. In practical robust design applications, a second-order polynomial model is adequate to accommodate the curvature of process mean and variance functions, thus mean-squared robust design models, frequently used by many researchers, would contain fourth-order terms. Consequently, the associated Pareto frontier might be non-convex and supported and non-supported efficient solutions needs to be generated. So, the objective of this paper is to develop a lexicographic weighted-Tchebycheff based bi-objective robust design model to generate the associated Pareto frontier. Our numerical example clearly shows the advantages of this model over frequently used weighted-sums model.
A preference-based robust design metric
Robust optimal design can be studied as a problem in decision-making requiring tradeoffs between mean and variance attributes. In this context, this paper views Taguchi's philosophy based design metrics using signal-to-noise (SN) ratios as empirical applications of decision-making under uncertainty with a priori sets of attribute tradeoff values. Alternatively, this paper presents a more rigorous preferencebased design metric using concepts from utility theory to accurately capture designer's intent and preferences. The use of this design metric as the robust optimal design criterion in a modified TRED (Tradeoffs in Robust Engineering Design) method with an innovative response-surface based iterative design space reduction strategy is presented. The effectiveness of the overall design procedure and the performance of the preference-based design metric are tested with the aid of demonstrative case studies and the results are discussed.