Understanding the Effects of Model Uncertainty in Robust Design With Computer Experiments

2006 ◽  
Author(s):  
Jun Liu ◽  
Daniel W. Apley ◽  
Wei Chen
Keyword(s):  
Robust Design ◽  
Prediction Interval ◽  
Computer Experiments ◽  
Design Problems ◽  
Deterministic Optimization ◽  
Automotive Engine ◽  
Design Objective ◽  
Design Variables ◽  
Complex Design ◽  
The Impact

The use of metamodels in simulation-based robust design introduces a new source of uncertainty that we term model interpolation uncertainty. Most existing approaches for treating interpolation uncertainty in computer experiments have been developed for deterministic optimization and are not applicable to design under uncertainty. With the randomness present in noise and/or design variables that propagates through the metamodel, the effects of model interpolation uncertainty are not nearly as transparent as in deterministic optimization. In this work, a methodology is developed within a Bayesian framework for quantifying the impact of interpolation uncertainty on robust design objective. By viewing the true response surface as a realization of a random process, as is common in kriging and other Bayesian analyses of computer experiments, we derive a closed-form analytical expression for a Bayesian prediction interval on the robust design objective function. This provides a simple, intuitively appealing tool for distinguishing the best design alternative and conducting more efficient computer experiments. Even though our proposed methodology is illustrated with a simple container design and an automotive engine piston design example here, the developed analytical approach is the most useful when applied to high-dimensional complex design problems in a similar manner.

Understanding the Effects of Model Uncertainty in Robust Design With Computer Experiments

2005 ◽  
Vol 128 (4) ◽  
pp. 945-958 ◽  
Author(s):  
Daniel W. Apley ◽  
Jun Liu ◽  
Wei Chen
Keyword(s):  
Robust Design ◽  
Prediction Interval ◽  
Random Noise ◽  
Computer Experiments ◽  
Deterministic Optimization ◽  
Automotive Engine ◽  
Design Objective ◽  
Design Variables ◽  
Discrete Design Variables ◽  
The Impact

The use of computer experiments and surrogate approximations (metamodels) introduces a source of uncertainty in simulation-based design that we term model interpolation uncertainty. Most existing approaches for treating interpolation uncertainty in computer experiments have been developed for deterministic optimization and are not applicable to design under uncertainty in which randomness is present in noise and/or design variables. Because the random noise and/or design variables are also inputs to the metamodel, the effects of metamodel interpolation uncertainty are not nearly as transparent as in deterministic optimization. In this work, a methodology is developed within a Bayesian framework for quantifying the impact of interpolation uncertainty on the robust design objective, under consideration of uncertain noise variables. By viewing the true response surface as a realization of a random process, as is common in kriging and other Bayesian analyses of computer experiments, we derive a closed-form analytical expression for a Bayesian prediction interval on the robust design objective function. This provides a simple, intuitively appealing tool for distinguishing the best design alternative and conducting more efficient computer experiments. We illustrate the proposed methodology with two robust design examples—a simple container design and an automotive engine piston design with more nonlinear response behavior and mixed continuous-discrete design variables.

Multi-Objective Optimization of an Autonomous Underwater Vehicle

2009 ◽  
Vol 43 (2) ◽  
pp. 48-60 ◽  
Author(s):  
M. Martz ◽  
W. L. Neu
Keyword(s):  
Design Space ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Design Problems ◽  
Single Measure ◽  
Design Variables ◽  
Complex Design ◽  
Final Design ◽  
Undersea Vehicle ◽  
Hierarchy Process

AbstractThe design of complex systems involves a number of choices, the implications of which are interrelated. If these choices are made sequentially, each choice may limit the options available in subsequent choices. Early choices may unknowingly limit the effectiveness of a final design in this way. Only a formal process that considers all possible choices (and combinations of choices) can insure that the best option has been selected. Complex design problems may easily present a number of choices to evaluate that is prohibitive. Modern optimization algorithms attempt to navigate a multidimensional design space in search of an optimal combination of design variables. A design optimization process for an autonomous underwater vehicle is developed using a multiple objective genetic optimization algorithm that searches the design space, evaluating designs based on three measures of performance: cost, effectiveness, and risk. A synthesis model evaluates the characteristics of a design having any chosen combination of design variable values. The effectiveness determined by the synthesis model is based on nine attributes identified in the U.S. Navy’s Unmanned Undersea Vehicle Master Plan and four performance-based attributes calculated by the synthesis model. The analytical hierarchy process is used to synthesize these attributes into a single measure of effectiveness. The genetic algorithm generates a set of Pareto optimal, feasible designs from which a decision maker(s) can choose designs for further analysis.

Employing Multidimensional Data Visualization Tools to Assess the Impact of Constraint Uncertainties on Complex Design Problems

2017 ◽  
Author(s):  
Gary M. Stump ◽  
Simon W. Miller ◽  
Michael A. Yukish ◽  
Christopher M. Farrell
Keyword(s):  
Data Visualization ◽  
Decision Makers ◽  
Multidimensional Data ◽  
Design Problems ◽  
Design Constraints ◽  
Performance Space ◽  
Multidimensional Data Visualization ◽  
Complex Design ◽  
Visualization Tools ◽  
The Impact

A potential source of uncertainty within multi-objective design problems can be the exact value of the underlying design constraints. This uncertainty will affect the resulting performance of the selected system commensurate with the level of risk that decision-makers are willing to accept. This research focuses on developing visualization tools that allow decision-makers to specify uncertainty distributions on design constraints and to visualize their effects in the performance space using multidimensional data visualization methods to solve problems with high orders of computational complexity. These visual tools will be demonstrated using an example portfolio design scenario in which the goal of the design problem is to maximize the performance of a portfolio with an uncertain budget constraint.

scholarly journals Tolerance Synthesis of Mechanisms: A Robust Design Approach

2003 ◽  
Author(s):  
Ste´phane Caro ◽  
Fouad Bennis ◽  
Philippe Wenger
Keyword(s):  
Robust Design ◽  
Optimization Problem ◽  
Synthesis Method ◽  
Upper And Lower Bounds ◽  
Design Parameters ◽  
Design Problems ◽  
Tolerance Synthesis ◽  
Design Variables ◽  
Synthesis Of Mechanisms ◽  
Serial Positioning

The paper aims at dimensioning a mechanism in order to make it robust, and synthesizing its dimensional tolerances. The design of a mechanism is supposed to be robust when its performance is as little as sensitive as possible to variations. First, a distinction is made between three sets to formulate a robust design problem; (i) the set of Design Variables (DV) whose nominal values can be selected between a range of upper and lower bounds, they are controllable; (ii) the set of Design Parameters (DP) that cannot be adjusted by the designer, they are uncontrollable; (iii) the set of performance functions. DV are however under uncontrollable variations although their nominal value can be adjusted. Moreover, two methods are described to solve robust design problems. The first method is explicit and solves problems that aim at minimizing variations in performance. The second method, an optimization problem, aims at optimizing the performance and minimizing its variations, but only when the ranges of variations in DV and DP are known. Besides, we define and compare some robustness indices. From the explicit method, we develop a new tolerance synthesis method. Finally, three examples are included to illustrate these methods: a damper, a two-dof and a three-dof serial positioning manipulator.

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.

scholarly journals Evaluating Clustering Algorithms for Identifying Design Subproblems

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Jeffrey W. Herrmann ◽  
Michael Morency ◽  
Azrah Anparasan ◽  
Erica L. Gralla
Keyword(s):  
Association Rule ◽  
Euclidean Distance ◽  
Clustering Algorithms ◽  
Design Teams ◽  
Clustering Methods ◽  
Design Problems ◽  
Design Variables ◽  
Complex Design ◽  
Synthetic Datasets ◽  
Better Than

Understanding how humans decompose design problems will yield insights that can be applied to develop better support for human designers. However, there are few established methods for identifying the decompositions that human designers use. This paper discusses a method for identifying subproblems by analyzing when design variables were discussed concurrently by human designers. Four clustering techniques for grouping design variables were tested on a range of synthetic datasets designed to resemble data collected from design teams, and the accuracy of the clusters created by each algorithm was evaluated. A spectral clustering method was accurate for most problems and generally performed better than hierarchical (with Euclidean distance metric), Markov, or association rule clustering methods. The method's success should enable researchers to gain new insights into how human designers decompose complex design problems.

A preliminary study on the integration of engineering and aesthetics measures via the design of vehicle silhouettes

2014 ◽  
Vol 229 (12) ◽  
pp. 2221-2230 ◽  
Author(s):  
Kuei-Yuan Chan ◽  
Shen-Cheng Chang
Keyword(s):  
Optimal Design ◽  
Level Structure ◽  
Point Of View ◽  
Design Problems ◽  
Physical Constraints ◽  
Engineering Discipline ◽  
Design Variables ◽  
Complex Design ◽  
Target Cascading ◽  
The Aesthetic

The success of a consumer product is the result of not only engineering specifications but also emotional effects. Therefore, product design must be multidisciplinary as well as transdisciplinary across both natural and social science. In this work, we investigate the optimal design of vehicle silhouettes considering various aesthetic and engineering measures. The entire design problem is modeled as a bi-level structure with the top level being the aesthetic subproblem and the lower level consists of subproblems in the engineering discipline. This multi-level system provides a feasible approach in solving complex design problems; it also resembles the interactions of different departments in the auto industry. The aesthetic subproblem uses 11 proportionality measures and curvature to quantify a vehicle silhouette. The engineering discipline includes safety, handling, and aerodynamics of a vehicle with physical constraints on vehicle geometry. The design variables are the locations of 15 nodal points in describing the silhouette of a vehicle. The linking variables between subsystems are body and chassis dimensions that must be consistent for a design to be feasible. The optimal design of this hierarchical problem is obtained using the analytical target cascading from the literature. Results show that the original prohibitively expensive all-in-one problem becomes solvable if systems of smaller subproblems are created. Adding emotional measures in engineering design is invaluable and will reveal the true merits of a product from consumers’ point of view. Although such metrics are generally opaque, this research demonstrates the impacts of these measures once they become available.

Multi-Objective Design Problem of Tire Wear and Visualization of Its Pareto Solutions2

2006 ◽  
Vol 34 (3) ◽  
pp. 170-194 ◽  
Author(s):  
M. Koishi ◽  
Z. Shida
Keyword(s):  
Inverse Problems ◽  
Conceptual Design ◽  
Dimensional Space ◽  
Design Stage ◽  
Objective Functions ◽  
Pareto Solutions ◽  
Design Problems ◽  
Multi Objective ◽  
Design Engineers ◽  
Design Variables

Abstract Since tires carry out many functions and many of them have tradeoffs, it is important to find the combination of design variables that satisfy well-balanced performance in conceptual design stage. To find a good design of tires is to solve the multi-objective design problems, i.e., inverse problems. However, due to the lack of suitable solution techniques, such problems are converted into a single-objective optimization problem before being solved. Therefore, it is difficult to find the Pareto solutions of multi-objective design problems of tires. Recently, multi-objective evolutionary algorithms have become popular in many fields to find the Pareto solutions. In this paper, we propose a design procedure to solve multi-objective design problems as the comprehensive solver of inverse problems. At first, a multi-objective genetic algorithm (MOGA) is employed to find the Pareto solutions of tire performance, which are in multi-dimensional space of objective functions. Response surface method is also used to evaluate objective functions in the optimization process and can reduce CPU time dramatically. In addition, a self-organizing map (SOM) proposed by Kohonen is used to map Pareto solutions from high-dimensional objective space onto two-dimensional space. Using SOM, design engineers see easily the Pareto solutions of tire performance and can find suitable design plans. The SOM can be considered as an inverse function that defines the relation between Pareto solutions and design variables. To demonstrate the procedure, tire tread design is conducted. The objective of design is to improve uneven wear and wear life for both the front tire and the rear tire of a passenger car. Wear performance is evaluated by finite element analysis (FEA). Response surface is obtained by the design of experiments and FEA. Using both MOGA and SOM, we obtain a map of Pareto solutions. We can find suitable design plans that satisfy well-balanced performance on the map called “multi-performance map.” It helps tire design engineers to make their decision in conceptual design stage.

Designing For Diversity: Implications for Research and Practice

2020 ◽  
Vol 64 (1) ◽  
pp. 563-567
Author(s):  
Abigail R. Wooldridge ◽  
Rod D. Roscoe ◽  
Rod D. Roscoe ◽  
Shannon C. Roberts ◽  
Rupa Valdez ◽  
...  
Keyword(s):  
Interdisciplinary Teams ◽  
Team Science ◽  
Participatory Methods ◽  
Sampling Strategies ◽  
Design Problems ◽  
Sociotechnical System ◽  
The Past ◽  
Diversity Committee ◽  
The Impact ◽  
Equity And Inclusion

The Diversity Committee of HFES has led sessions at the Annual Meeting for the past three years focused on improving diversity, equity and inclusion in the society as well as providing support to human factors and ergonomics (HF/E) researchers and practitioners who aim to apply HF/E knowledge and principles to improve diversity, equity and inclusion through their work. In this panel, we bring together researchers actively engaged in designing technology and systems by considering issues of diversity, equity and inclusion to share insights and methods. Topics include the thoughtful design of sampling strategies and research approaches, alternative and participatory methods to understand the impact of automation and technology on equity, scoping design problems to be inclusive and equitable through interdisciplinary partnerships, and the application of sociotechnical system design and team science to develop interdisciplinary teams. By sharing our experiences, we hope to prepare others to successfully approach these topics.

Sign in / Sign up

Export Citation Format

Share Document