Performance Estimation and Robust Design Decisions

2000 ◽  
Author(s):  
Xiao Tang ◽  
Sundar Krishnamurty
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Robust Design ◽  
Estimation Method ◽  
Performance Estimation ◽  
Accurate Estimation ◽  
Design Decision ◽  
Computationally Efficient ◽  
Design Variables ◽  
Highly Nonlinear

Abstract This paper deals with two major issues critical to the development and implementation of a decision-based robust design, namely, representation of design performance under conditions of uncertainty and the development of a robust design decision model. Specifically, this paper presents a computationally efficient procedure for accurate estimation of performance variance using a novel Surround Point Method (SPM) and discusses its incorporation into a decision-based robust design framework. Results indicate that by mimicking effects from Monte-Carlo Simulation (MCS), SPM-based uncertainty estimation method appears to offer the best promise in achieving an optimal balance between computational complexity and design-scenario independence. It can be expected to be a viable and applicable probability estimation tool in generic engineering design, and particularly useful in highly nonlinear configuration design with many design variables. Furthermore, to explicitly incorporate robustness criteria, this paper introduces the concept of design evaluation level as a means for decision-making in an evolving design process. Using this concept, this paper introduces a robust decision-based design methodology that can methodically handle multiple performance attributes, system constraints, and robustness issues in engineering design. These issues are discussed in the context of engineering design decision-making with the aid of a simple case study and the results are discussed.

Computationally Efficient Imprecise Uncertainty Propagation in Engineering Design and Decision Making

2012 ◽  
Author(s):  
Dipanjan D. Ghosh ◽  
Andrew Olewnik
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Computational Efficiency ◽  
Design Problem ◽  
Uncertainty Propagation ◽  
Future Research ◽  
Parameter Estimates ◽  
Design Decision ◽  
Computationally Efficient ◽  
Imprecise Uncertainty

Modeling uncertainty through probabilistic representation in engineering design is common and important to decision making that considers risk. However, representations of uncertainty often ignore elements of “imprecision” that may limit the robustness of decisions. Further, current approaches that incorporate imprecision suffer from computational expense and relatively high solution error. This work presents the Computationally Efficient Imprecise Uncertainty Propagation (CEIUP) method which draws on existing approaches for propagation of imprecision and integrates sparse grid numerical integration to provide computational efficiency and low solution error for uncertainty propagation. The first part of the paper details the methodology and demonstrates improvements in both computational efficiency and solution accuracy as compared to the Optimized Parameter Sampling (OPS) approach for a set of numerical case studies. The second half of the paper is focused on estimation of non-dominated design parameter spaces using decision policies of Interval Dominance and Maximality Criterion in the context of set-based sequential design-decision making. A gear box design problem is presented and compared with OPS, demonstrating that CEIUP provides improved estimates of the non-dominated parameter range for satisfactory performance with faster solution times. Parameter estimates obtained for different risk attitudes are presented and analyzed from the perspective of Choice Theory leading to questions for future research. The paper concludes with an overview of design problem scenarios in which CEIUP is the preferred method and offers opportunities for extending the method.

Arrow's Theorem and Engineering Design Decision Making

1999 ◽  
Vol 11 (4) ◽  
pp. 218-228 ◽  
Author(s):  
Michael J. Scott ◽  
Erik K. Antonsson
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Design Decision ◽  
Arrow’S Theorem ◽  
Arrow's Theorem

A Preliminary Study of Novice and Expert Users’ Decision-Making Procedures During Visual Trade Space Exploration

2009 ◽  
Author(s):  
David Wolf ◽  
Timothy W. Simpson ◽  
Xiaolong Luke Zhang
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Data Visualization ◽  
Space Exploration ◽  
Computational Thinking ◽  
Test Group ◽  
Design Decision ◽  
Ongoing Research ◽  
Trade Space Exploration ◽  
Insight Into

Thanks to recent advances in computing power and speed, designers can now generate a wealth of data on demand to support engineering design decision-making. Unfortunately, while the ability to generate and store new data continues to grow, methods and tools to support multi-dimensional data exploration have evolved at a much slower pace. Moreover, current methods and tools are often ill-equipped at accommodating evolving knowledge sources and expert-driven exploration that is being enabled by computational thinking. In this paper, we discuss ongoing research that seeks to transform decades-old decision-making paradigms rooted in operations research by considering how to effectively convert data into knowledge that enhances decision-making and leads to better designs. Specifically, we address decision-making within the area of trade space exploration by conducting human-computer interaction studies using multi-dimensional data visualization software that we have been developing. We first discuss a Pilot Study that was conducted to gain insight into expected differences between novice and expert decision-makers using a small test group. We then present the results of two Preliminary Experiments designed to gain insight into procedural differences in how novices and experts use multi-dimensional data visualization and exploration tools and to measure their ability to use these tools effectively when solving an engineering design problem. This work supports our goal of developing training protocols that support efficient and effective trade space exploration.

Manufacturing Investment and Allocation in Product Line Design Decision-Making

2005 ◽  
Author(s):  
Jeremy J. Michalek ◽  
Oben Ceryan ◽  
Panos Y. Papalambros ◽  
Yoram Koren
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Product Line ◽  
Manufacturing Cost ◽  
Design Decision ◽  
Product Line Design ◽  
Product Decision ◽  
Market Consequences ◽  
Line Design ◽  
Is Design

An important aspect of product development is design for manufacturability (DFM) analysis that aims to incorporate manufacturing requirements into early product decision-making. Existing methods in DFM seldom quantify explicitly the tradeoffs between revenues and costs generated by making design choices that may be desirable in the market but costly to manufacture. This paper builds upon previous work coordinating models for engineering design and marketing product line decision-making by incorporating quantitative models of manufacturing investment and production allocation. The result is a methodology that considers engineering design decisions quantitatively in the context of manufacturing and market consequences in order to resolve tradeoffs, not only among performance objectives, but also between market preferences and manufacturing cost.

Imprecision in Engineering Design

1995 ◽  
Vol 117 (B) ◽  
pp. 25-32 ◽  
Author(s):  
E. K. Antonsson ◽  
K. N. Otto
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Engineering Design ◽  
Cost Estimation ◽  
Formal Method ◽  
Concurrent Design ◽  
Design Decision ◽  
Design Cost ◽  
Formal Information

Methods for incorporating imprecision in engineering design decision-making are briefly reviewed and compared. A tutorial is presented on the Method of Imprecision (MoI), a formal method, based on the mathematics of fuzzy sets, for representing and manipulating imprecision in engineering design. The results of a design cost estimation example, utilizing a new informal cost specification, are presented. The MoI can provide formal information upon which to base decisions during preliminary engineering design and can facilitate set-based concurrent design.

Understanding the Utilization of Information Stimuli in Design Decision Making Using Eye Gaze Data

2015 ◽  
Author(s):  
Youyi Bi ◽  
Murtuza Shergadwala ◽  
Tahira Reid ◽  
Jitesh H. Panchal
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Eye Gaze ◽  
Design Decision ◽  
Design Engineers ◽  
Cognitive Decision ◽  
Improved Design ◽  
Student Subjects ◽  
Types Of Information ◽  
Normative Models

Research on decision making in engineering design has focused primarily on how to make decisions using normative models given certain information. However, there exists a research gap on how diverse information stimuli are combined by designers in decision making. In this paper, we address the following question: how do designers weigh different information stimuli to make decisions in engineering design contexts? The answer to this question can provide insights on diverse cognitive models for decision making used by different individuals. We investigate the information gathering behavior of individuals using eye gaze data from a simulated engineering design task. The task involves optimizing an unknown function using an interface which provides two types of information stimuli, including a graph and a list area. These correspond to the graphical stimulus and numerical stimulus, respectively. The study was carried out using a set of student subjects. The results suggest that individuals weigh different forms of information stimuli differently. It is observed that graphical information stimulus assists the participants in optimizing the function with a higher accuracy. This study contributes to our understanding of how diverse information stimuli are utilized by design engineers to make decisions. The improved understanding of cognitive decision making models would also aid in improved design of decision support tools.

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.

Streamlining ‘environment in design' decision-making – from concept to operations in major development projects

2018 ◽  
Vol 58 (2) ◽  
pp. 679
Author(s):  
Janine M. Barrow
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Design Process ◽  
Practical Experience ◽  
Development Project ◽  
Hazard Identification ◽  
Design Decision ◽  
Engineering Design Process ◽  
Start Up ◽  
Major Development

As the engineering design process for a major development project advances from concept through to ready for start up, many key decisions are made and controls formulated that ultimately influence environmental, social (and safety) outcomes. These decisions are often made based on sound technical grounds with key decision logs, hazard identification or hazard and operability studies or similar used to record the process, but with limited recognition of environmental outcomes. Many of the onshore and offshore regulations in Australia (most notably, the Offshore Petroleum and Greenhouse Gas (Environment) Regulations 2009) require environmental risks and impacts to be reduced to a level that is as low as reasonably practicable (ALARP). Additionally, justifiable assessment of controls and decisions are presented in the environment plans (EP) that are typically prepared later on in the design process. Challenges can often arise when geographically disparate design contractors lack ALARP assessment processes to evaluate decisions and controls from an environmental perspective and record outcomes for future use in regulatory documentation. This can be particularly pronounced for operations EPs. Janine shares her practical experience in environmental integration in engineering design to showcase methods that tangibly demonstrate robust decision-making, inclusive of delivering environmental outcomes, to regulators.

A conceptual tool for environmentally benign design: development and evaluation of a “proof of concept”

2020 ◽  
Vol 34 (1) ◽  
pp. 30-44
Author(s):  
Shakuntala Acharya ◽  
Amaresh Chakrabarti
Keyword(s):  
Decision Making ◽  
Knowledge Base ◽  
Estimation Method ◽  
Environmentally Benign ◽  
Design Stage ◽  
Design Element ◽  
Design Decision ◽  
Proof Of Concept ◽  
Design Development ◽  
Conceptual Tool

AbstractDesign is a decision-making process for which knowledge is a prerequisite. Most decisions are taken at the conceptual stage and have pronounced influence on the final design. The literature, therefore, recommends the incorporation of sustainability criteria, such as environment, at this stage. Difficulty in performing life cycle assessment (LCA) due to low availability of information at the conceptual stage for evaluation and highly abstract nature of solutions, inadequate incorporation of DfE (Design for Environment) guidelines and LCA reports into the design process, and a lack of effective communication of the same to the designers for prompt decision-making are major motivations for the development of a support. This paper discusses a “conceptual Tool for environmentally benign design” – concepTe – that supports designers in decision-making during the conceptual design stage, by offering environmental impact (EI) estimates of abstract solutions with associated uncertainty, for evaluation and selection of the most environmentally benign solution as concept. The EI estimates are calculated by a module in the tool based on a proposed EI estimation method, which requires the support of a knowledge base to fetch appropriate LCA information corresponding to the design element being conceptualized. This knowledge base is grounded in the domain-agnostic SAPPhIRE model ontology, allows semantic operability of the knowledge, and offers the results to the designers in a familiar domain language to aid decision-making. A “proof of concept” of the tool is developed for application in design of building in the AEC (Architectural design, Engineering, and Construction) domain. Further, empirical studies are conducted to evaluate the effectiveness of the “proof of concept” to support decision-making and results are found favorable. The paper also discusses the future scope for further development of the tool into a holistic design decision-making platform.

Imprecision in Engineering Design

1995 ◽  
Vol 117 (B) ◽  
pp. 25-32 ◽  
Author(s):  
E. K. Antonsson ◽  
K. N. Otto
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Engineering Design ◽  
Cost Estimation ◽  
Formal Method ◽  
Concurrent Design ◽  
Design Decision ◽  
Design Cost ◽  
Formal Information

Methods for incorporating imprecision in engineering design decision-making are briefly reviewed and compared. A tutorial is presented on the Method of Imprecision (MoI), a formal method, based on the mathematics of fuzzy sets, for representing and manipulating imprecision in engineering design. The results of a design cost estimation example, utilizing a new informal cost specification, are presented. The MoI can provide formal information upon which to base decisions during preliminary engineering design and can facilitate set-based concurrent design.

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