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.

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.

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.

scholarly journals A method for robust design in a coupled decision environment

2021 ◽  
Vol 7 ◽  
Author(s):  
Gehendra Sharma ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Robust Design ◽  
Type I ◽  
Type Ii ◽  
Design Decisions ◽  
Design Variables ◽  
Robust Decisions ◽  
Decision Environment ◽  
Function Relationship ◽  
Noise Factors ◽  
Selection Of

Abstract The design of a connected engineered system requires numerous design decisions that influence one another. In a connected system that comprises numerous interacting decisions involving concurrency and hierarchy, accounting for interactions while also managing uncertainties, it is imperative to make robust decisions. In this article, we present a method for robust design using coupled decisions to identify design decisions that are relatively insensitive to uncertainties. To account for the influence among decisions, design decisions are modelled as coupled decisions. They are defined using three criteria: the types of decisions, the strength of interactions and the decision levels. In order to make robust decisions, robust design methods are classified based on sources of uncertainty, namely, Type I (noise factors), Type II (design variables) and Type III (function relationship between design variables and responses). The design of a one-stage reduction gearbox is used as a demonstration example. To illustrate the proposed method for robust design using coupled decisions, we present the simultaneous selection of gear material and gearbox geometry in a coupled decision environment while managing the uncertainties involved in designing gearboxes.

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.

Robust Concept Exploration of Materials, Products and Associated Manufacturing Processes

2018 ◽  
Author(s):  
Anand Balu Nellippallil ◽  
Pranav Mohan ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Robust Design ◽  
Design Method ◽  
Integrated Design ◽  
Manufacturing Processes ◽  
Type I ◽  
Process Chain ◽  
Process Chains ◽  
Concept Exploration ◽  
Decision Based Design ◽  
Noise Factors

In this paper, we present robust concept exploration using a goal-oriented, inverse decision-based design method to carry out the integrated design of material, product and associated manufacturing processes by managing the uncertainty involved. The uncertainty in complex material and product systems is derived from many sources and we classify robust design based on these sources — uncertainty in noise factors (Type I robust design); uncertainty in design variables or control factors (Type II robust design); uncertainty in function relationship between control/noise and response (Type III robust design); and propagation and potential amplification of uncertainty in a process chain (Type I to III robust designs across process chains). In this paper, we introduce a variation to the existing goal-oriented inverse decision-based design method to bring in robustness for multiple conflicting goals from the stand-point of Type I to III robust design across process chains. The variation embodies the introduction of specific robust design goals and constraints anchored in the mathematical constructs of error margin indices and design capability indices to determine “satisficing robust design” specifications for given performance requirement ranges using the goal-oriented, inverse design method. The design of a hot rolling process chain for the production of a rod is used as an example.

Robust Suspension System Design

1993 ◽  
Author(s):  
John E. Beard ◽  
John W. Sutherland
Keyword(s):  
System Design ◽  
Robust Design ◽  
Loss Function ◽  
Kinematic Model ◽  
Small Variation ◽  
Suspension System ◽  
Product Performance ◽  
Design Variables ◽  
Manufacturing Assembly ◽  
The Impact

Abstract Traditionally, levels for design variables are sought that produce optimal performance of a product. When manufacturing and assembly processes are used to realize the design intent, however, the product performance may differ from that envisioned during design. This is because the performance of a product is often very sensitive to manufacturing and assembly variations. This paper presents a methodology for robust design that incorporates the impact of manufacturing/assembly variations. The methodology characterizes the performance of a manufactured product via a loss function. The loss function measure is attractive from a robust design standpoint since it stresses both desirable performance on the average and small variation in performance from product to product. The design methodology is demonstrated through a suspension system design application. A model for the kinematic behavior of a suspension system is developed. The scrub rate is selected as the response of interest to demonstrate the methodology. The behavior of the kinematic model, in terms of the loss function, is approximated near a set point and levels of the design variables are sought that minimize the loss. An iterative procedure is described for optimizing the loss function. The application demonstrates that substantial improvements can be made in terms of actual manufactured product performance through the use of the methodology.

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.

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.

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