scholarly journals Integration of the Response Surface Methodology With the Compromise Decision Support Problem in Developing a General Robust Design Procedure

1995 ◽  
Author(s):  
Wei Chen ◽  
Kwok-Leung Tsui ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Decision Support ◽  
Response Surface ◽  
Robust Design ◽  
Irrigation System ◽  
Design Procedure ◽  
Closed Form Solutions ◽  
Control Factors ◽  
Design Variables ◽  
Solar Powered ◽  
Noise Factors

Abstract 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 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. Our focus in this paper is on illustrating our approach rather than on the results per se.

A Procedure for Robust Design: Minimizing Variations Caused by Noise Factors and Control Factors

1996 ◽  
Vol 118 (4) ◽  
pp. 478-485 ◽  
Author(s):  
Wei Chen ◽  
J. K. Allen ◽  
Kwok-Leung Tsui ◽  
F. Mistree
Keyword(s):  
Robust Design ◽  
Irrigation System ◽  
Small Variation ◽  
Type I ◽  
Closed Form Solutions ◽  
Control Factors ◽  
Design Variables ◽  
Solar Powered ◽  
And Control ◽  
Noise Factors

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.

Robust Design for Multiscale and Multidisciplinary Applications

2006 ◽  
Vol 128 (4) ◽  
pp. 832-843 ◽  
Author(s):  
Janet K. Allen ◽  
Carolyn Seepersad ◽  
HaeJin Choi ◽  
Farrokh Mistree
Keyword(s):  
Robust Design ◽  
Type I ◽  
Uncertain Information ◽  
Control Factors ◽  
Multiscale Systems ◽  
Design Variables ◽  
Challenges And Opportunities ◽  
Quality Of Products ◽  
Many Sources ◽  
Noise Factors

The intent in robust design is to improve the quality of products and processes by reducing their sensitivity to variations, thereby reducing the effects of variability without removing its sources. Robust design is especially useful for integrating information from designers working at multiple length and time scales. Inevitably this involves the integration of uncertain information. This uncertainty is derived from many sources and robust design may be classified based on these sources—uncertainty in noise or environmental and other noise factors (type I); uncertainty in design variables or control factors (type II); and uncertainty introduce by modeling methods (type III). Each of these types of uncertainty can be mitigated by robust design. Of particular interest are the challenges associated with the design of multidisciplinary and multiscale systems; these challenges and opportunities are examined in the context of materials design.

Universal Design Considering Physical Characteristics of Diverse Users

2019 ◽  
Vol 13 (4) ◽  
pp. 517-525
Author(s):  
Masato Inoue ◽  
Wataru Suzuki ◽  
◽  
Keyword(s):  
Universal Design ◽  
Design Method ◽  
Physical Characteristics ◽  
Design Solution ◽  
User Requirements ◽  
User Characteristics ◽  
Control Factors ◽  
Design Variables ◽  
Noise Factors

To achieve a universal design that satisfies diverse user requirements associated with aging and internationalization, designers must make a decision based on diverse user requirements. Designers have generally incorporated average human physical characteristics in their designs. Thus, user limitations are critically important. Traditional design methods often regard engineering and product design as iterative processes based on point values. However, when user information is represented as a point value, the resulting product satisfies only that specific user group and does not necessarily satisfy diverse user groups. This study proposes a universal design method that obtains diversely ranged design solutions for user requirements. The proposed method defines diverse user requirements, design variables, and user characteristics as sets, which range in value. To represent user information accurately, users are classified into numerous groups using classification techniques. Design variables are divided into two types: control and noise. Control factors are designer-controllable variables that are based on design specifications. Noise factors are designer-uncontrollable variables representing user characteristics. To derive a ranged design solution set, designers clarify the relationship between performance and design variables. Ranged solutions satisfying required performance are derived for each group using all relational expressions and ranged variable values. The combinations of divided design variables that cannot satisfy the required performance are eliminated from the design proposal, and the narrowed range of design variables become ranged solutions. The ranged solutions are derived for each group, and the common range of design variables is the ranged solution for all users. This paper chooses the design problem of the strap height of a train as a case study of the proposed universal design method. In this case study, we consider diverse user requirements based on the variability of physical characteristics. This paper discusses the suitability of our proposed approach for obtaining ranged solutions that reflect the physical characteristics of diverse users.

Taguchi Robust Design of Exhaust System Based on the Parametrically Process of Parts Location

2013 ◽  
Vol 579-580 ◽  
pp. 894-900
Author(s):  
Teng Fei Li ◽  
Hui Xia Liu ◽  
Yi Xue Mao
Keyword(s):  
Robust Design ◽  
Optimization Design ◽  
Exhaust System ◽  
The Finite Element Method ◽  
Control Factors ◽  
Body Design ◽  
Modal Property ◽  
Noise Factors ◽  
Taguchi Robust Design ◽  
Exhaust Systems

Due to the change of car-body design, the location of exhaust systems hanger is uncertain and always fluctuates around the initial design position. So the Taguchi method is introduced to conduct exhaust systems optimal design. Firstly, the parameterization of hanger location under the grid environment was realized by combining NastranHypermesh and Isight. Then, the Taguchi robust design of the exhaust system is performed taking the hanger location as noise factors and the stiffness of hanger shock absorber as control factors. As a result, modal property and robustness of the exhaust system are improved. At last, the results of Taguchi robust design and traditional sensitivity optimization design based on the finite element method are compared, which reveals the advantage of Taguchi robust design in improving product quality.

Response surface approach to robust design of assembly cells through simulation

2018 ◽  
Vol 38 (4) ◽  
pp. 450-464 ◽  
Author(s):  
Cem Savas Aydin ◽  
Senim Ozgurler ◽  
Mehmet Bulent Durmusoglu ◽  
Mesut Ozgurler
Keyword(s):  
Response Surface ◽  
Robust Design ◽  
Performance Measures ◽  
Performance Optimization ◽  
Design Approach ◽  
Shop Floor ◽  
Optimization Approach ◽  
Content Type ◽  
Performance Variability ◽  
Noise Factors

Purpose This paper aims to present a multi-response robust design (RD) optimization approach for U-shaped assembly cells (ACs) with multi-functional walking-workers by using operational design (OD) factors in a simulation setting. The proposed methodology incorporated the design factors related to the operation of ACs into an RD framework. Utilization of OD factors provided a practical design approach for ACs addressing system robustness without modifying the cell structure. Design/methodology/approach Taguchi’s design philosophy and response surface meta-models have been combined for robust simulation optimization (SO). Multiple performance measures have been considered for the study and concurrently optimized by using a multi-response optimization (MRO) approach. Simulation setting provided flexibility in experimental design selection and facilitated experiments by avoiding cost and time constraints in real-world experiments. Findings The present approach is illustrated through RD of an AC for performance measures: average throughput time, average WIP inventory and cycle time. Findings are in line with expectations that a significant reduction in performance variability is attainable by trading-off optimality for robustness. Reductions in expected performance (optimality) values are negligible in comparison to reductions in performance variability (robustness). Practical implications ACs designed for robustness are more likely to meet design objectives once they are implemented, preventing changes or roll-backs. Successful implementations serve as examples to shop-floor personnel alleviating issues such as operator/supervisor resistance and scepticism, encouraging participation and facilitating teamwork. Originality/value ACs include many activities related to cell operation which can be used for performance optimization. The proposed framework is a realistic design approach using OD factors and considering system stochasticity in terms of noise factors for RD optimization through simulation. To the best of the authors’ knowledge, it is the first time a multi-response RD optimization approach for U-shaped manual ACs with multi-functional walking-workers using factors related to AC operation is proposed.

Robustness Evaluation of a Miniaturized Machine Tool

1999 ◽  
Author(s):  
Nozomu Mishima ◽  
Kousuke Ishii
Keyword(s):  
Machine Tool ◽  
Robust Design ◽  
Design Guidelines ◽  
Design Parameters ◽  
Drive Unit ◽  
Control Factors ◽  
Feed Drive ◽  
Space Saving ◽  
Straightness Error ◽  
Noise Factors

Abstract This paper applies the method of robust design to machine tool design. The new design focuses on miniaturization that provides significant for energy and space saving. Our approach combines an analytical procedure representing the machining motions of a machine tool (form-shaping theory) with procedures for robust design. The effort identifies the design parameters of a machine tool that significantly influence the machining tolerance and leads to a general design guidelines for robust miniaturization. Further, this research applies the Taguchi method to the form-shaping function of a prototype miniature lathe. The analysis addresses five machine tool dimensions as control factors, while treating local errors in the machine structure as noise factors. The robustness study seeks to identify the importance of each factor in improving performance of the machine tool. The result shows that the thickness of the feed drive unit affects the performance most significantly. Among the local errors, straightness error of the same feed drive unit has a critical importance.

Robust Design: Goal Formulations and a Comparison of Metamodeling Methods

1999 ◽  
Author(s):  
Yao Lin ◽  
Kiran Krishnapur ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Response Surface ◽  
Robust Design ◽  
Design Space ◽  
Response Surface Models ◽  
Design Variables ◽  
Nonlinear Design ◽  
Highly Nonlinear ◽  
Variable Function ◽  
Surface Models ◽  
High Degree

Abstract In this paper, through theoretical analysis, we point out the limitations of goal formulations in previous methods for approximation-based robust design. Based on different philosophies and mathematical deduction, we propose three new methods to formulate robust design goals. Using a single variable function, we compare and contrast the use of response surface models and kriging models for approximating non-random, deterministic computer analyses in robust design with large variances of design variables in a highly nonlinear design space. Our preliminary conclusions are: 1) kriging models perform better than response surface models in a large design space with a high degree of nonlinearity, and 2) more robust solutions are achievable with kriging models than with response surface models.

Using Design Capability Indices to Satisfy Ranged Sets of Design Requirements

1996 ◽  
Author(s):  
Wei Chen ◽  
Timothy W. Simpson ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Robust Design ◽  
Irrigation System ◽  
Signal To Noise Ratio ◽  
Successful Implementation ◽  
Signal To Noise ◽  
Statistical Process ◽  
Capability Indices ◽  
Design Requirements ◽  
Noise Ratio ◽  
Solar Powered

Abstract For robust design it is desirable to allow the design requirements to vary within a certain range rather than setting point targets. This is particularly important during the early stages of design when little is known about the system and its requirements. Toward this end, design capability indices are developed in this paper to assess the capability of a family of designs, represented by a range of top-level design specifications, to satisfy a ranged set of design requirements. Design capability indices are based on process capability indices from statistical process control and provide a single objective, alternate approach to the use of Taguchi’s signal-to-noise ratio which is often used for robust design. Successful implementation of design capability indices ensures that a family of designs conforms to a given ranged set of design requirements. To demonstrate an application and the usefulness of design capability indices, the design of a solar powered irrigation system is presented. Our focus in this paper is on the development and implementation of design capability indices as an alternate approach to the use of the signal-to-noise ratio and not on the results of the example problem, per se.

scholarly journals Multiobjective Optimization of Solar-Powered Irrigation System with Fuzzy Type-2 Noise Modelling

2017 ◽  
pp. 189-214 ◽  
Author(s):  
T. Ganesan ◽  
Pandian Vasant ◽  
I. Elamvazuthi
Keyword(s):  
Design Optimization ◽  
Design Problem ◽  
Alternative Energy ◽  
Irrigation System ◽  
Pareto Frontier ◽  
Industrial Applications ◽  
Solution Methods ◽  
Fuzzy Type Ii ◽  
Solar Powered ◽  
Noise Factors

Design optimization has been commonly practiced for many years across various engineering disciplines. Optimization per se is becoming a crucial element in industrial applications involving sustainable alternative energy systems. During the design of such systems, the engineer/decision maker would often encounter noise factors (e.g. solar insolation and ambient temperature fluctuations) when their system interacts with the environment. Therefore, successful modelling and optimization procedures would require a framework that encompasses all these uncertainty features and solves the problem at hand with reasonable accuracy. In this chapter, the sizing and design optimization of the solar powered irrigation system was considered. This problem is multivariate, noisy, nonlinear and multiobjective. This design problem was tackled by first using the Fuzzy Type II approach to model the noise factors. Consequently, the Bacterial Foraging Algorithm (BFA) (in the context of a weighted sum framework) was employed to solve this multiobjective fuzzy design problem. This method was then used to construct the approximate Pareto frontier as well as to identify the best solution option in a fuzzy setting. Comprehensive analyses and discussions were performed on the generated numerical results with respect to the implemented solution methods.

scholarly journals Response surface methodology for estimating missing values in a pareto genetic algorithm used in parameter design

2017 ◽  
Vol 37 (2) ◽  
pp. 89-98
Author(s):  
Enrique Canessa ◽  
Sergio Chaigneau
Keyword(s):  
Genetic Algorithm ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Robust Design ◽  
Variance Reduction ◽  
Missing Values ◽  
Efficient Solutions ◽  
Target Values ◽  
Response Systems ◽  
Noise Factors

We present an improved Pareto Genetic Algorithm (PGA), which finds solutions to problems of robust design in multi-response systems with 4 responses and as many as 10 control and 5 noise factors. Because some response values might not have been obtained in the robust design experiment and are needed in the search process, the PGA uses Response Surface Methodology (RSM) to estimate them. Not only the PGA delivered solutions that adequately adjusted the response means to their target values, and with low variability, but also found more Pareto efficient solutions than a previous version of the PGA. This improvement makes it easier to find solutions that meet the trade-off among variance reduction, mean adjustment and economic considerations. Furthermore, RSM allows estimating outputs’ means and variances in highly non-linear systems, making the new PGA appropriate for such systems.

Sign in / Sign up

Export Citation Format

Share Document