Preferences and Correlated Uncertainties in Engineering Design

2003 ◽  
Author(s):  
Tomonori Honda ◽  
Erik K. Antonsson
Keyword(s):  
Engineering Design ◽  
Design Variable ◽  
Design Space ◽  
Design Method ◽  
Design Problems ◽  
Performance Variables ◽  
Design Variables ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
And Performance

The Method of Imprecision (MOI) is a multi-objective design method that maximizes the overall degree of both design and performance preferences. Sets of design variables are iteratively selected, and the corresponding performances are approximately computed. The designer’s judgment (expressed as preferences) are combined (aggregated) with the customer’s preferences, to determine the overall preference for sets of points in the design space. In addition to degrees of preference for values of the design and performance variables, engineering design problems also typically include uncertainties caused by uncontrolled variations, for example, measuring and fabrication limitations. This paper illustrates the computation of expected preference for cases where the uncertainties are uncorrelated, and also where the uncertainties are correlated. The result is a “best” set of design variable values for engineering problems, where the overall aggregated preference is maximized. As is illustrated by the examples shown here, where both preferences and uncontrolled variations are present, the presence of uncertainties can have an important effect on the choice of the overall best set of design variable values.

Standards, Codes of Practice and Expert Systems

1988 ◽  
Vol 21 (1) ◽  
pp. 5-9 ◽  
Author(s):  
E G McCluskey ◽  
S Thompson ◽  
D M G McSherry
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Engineering Design ◽  
Performance Criteria ◽  
Design Problems ◽  
Codes Of Practice ◽  
Engineering Design Problems ◽  
And Performance

Many engineering design problems require reference to standards or codes of practice to ensure that acceptable safety and performance criteria are met. Extracting relevant data from such documents can, however, be a problem for the unfamiliar user. The use of expert systems to guide the retrieval of information from standards and codes of practice is proposed as a means of alleviating this problem. Following a brief introduction to expert system techniques, a tool developed by the authors for building expert system guides to standards and codes of practice is described. The steps involved in encoding the knowledge contained in an arbitrarily chosen standard are illustrated. Finally, a typical consultation illustrates the use of the expert system guide to the standard.

scholarly journals SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 250 ◽  
Author(s):  
Umesh Balande ◽  
Deepti Shrimankar
Keyword(s):  
Constrained Optimization ◽  
Engineering Design ◽  
Real World ◽  
Optimization Design ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Design Problems ◽  
Improved Firefly Algorithm ◽  
Engineering Design Problems ◽  
And Performance

Firefly-Algorithm (FA) is an eminent nature-inspired swarm-based technique for solving numerous real world global optimization problems. This paper presents an overview of the constraint handling techniques. It also includes a hybrid algorithm, namely the Stochastic Ranking with Improved Firefly Algorithm (SRIFA) for solving constrained real-world engineering optimization problems. The stochastic ranking approach is broadly used to maintain balance between penalty and fitness functions. FA is extensively used due to its faster convergence than other metaheuristic algorithms. The basic FA is modified by incorporating opposite-based learning and random-scale factor to improve the diversity and performance. Furthermore, SRIFA uses feasibility based rules to maintain balance between penalty and objective functions. SRIFA is experimented to optimize 24 CEC 2006 standard functions and five well-known engineering constrained-optimization design problems from the literature to evaluate and analyze the effectiveness of SRIFA. It can be seen that the overall computational results of SRIFA are better than those of the basic FA. Statistical outcomes of the SRIFA are significantly superior compared to the other evolutionary algorithms and engineering design problems in its performance, quality and efficiency.

On combinatorial design spaces for the configuration design of product families

2001 ◽  
Vol 15 (2) ◽  
pp. 91-108 ◽  
Author(s):  
ZAHED SIDDIQUE ◽  
DAVID W. ROSEN
Keyword(s):  
Engineering Design ◽  
Design Space ◽  
Optimization Problems ◽  
Product Family ◽  
Product Variety ◽  
Combinatorial Design ◽  
Configuration Design ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Space Modeling

For typical optimization problems, the design space of interest is well defined: It is a subset of Rn, where n is the number of (continuous) variables. Constraints are often introduced to eliminate infeasible regions of this space from consideration. Many engineering design problems can be formulated as search in such a design space. For configuration design problems, however, the design space is much more difficult to define precisely, particularly when constraints are present. Configuration design spaces are discrete and combinatorial in nature, but not necessarily purely combinatorial, as certain combinations represent infeasible designs. One of our primary design objectives is to drastically reduce the effort to explore large combinatorial design spaces. We believe it is imperative to develop methods for mathematically defining design spaces for configuration design. The purpose of this paper is to outline our approach to defining configuration design spaces for engineering design, with an emphasis on the mathematics of the spaces and their combinations into larger spaces that more completely capture design requirements. Specifically, we introduce design spaces that model physical connectivity, functionality, and assemblability considerations for a representative product family, a class of coffeemakers. Then, we show how these spaces can be combined into a “common” product variety design space. We demonstrate how constraints can be defined and applied to these spaces so that feasible design regions can be directly modeled. Additionally, we explore the topological and combinatorial properties of these spaces. The application of this design space modeling methodology is illustrated using the coffeemaker product family.

Quantified Relations: A Class of Predicate Logic Design Constraints Among Sets of Manufacturing, Operating, and Other Variations

1996 ◽  
Author(s):  
William W. Finch ◽  
Allen C. Ward
Keyword(s):  
Engineering Design ◽  
Predicate Logic ◽  
Operating Conditions ◽  
Causal Relationships ◽  
Engineering Systems ◽  
Multiple Sources ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Computing Effort ◽  
And Performance

Abstract This paper addresses a class of engineering design problems in which multiple sources of variations affect a product’s design, manufacture, and performance. Examples of these sources include uncertainty in nominal dimensions, variations in manufacture, changing environmental or operating conditions, and operator adjustments. Quantified relations (QR’s) are defined as a class of predicate logic expressions representing constraints between sets of design variations. Within QR’s, each variable’s quantifier and the order of quantification express a physical system’s causal relationships. This paper also presents an algorithm which propagates intervals through QR’s involving continuous, monotonic equations. Causal relationships between variables in engineering systems are discussed, and a tabular representation for them is presented. This work aims to broaden the application of automated constraint satisfaction algorithms, shortening design cycles for this class of problem by reducing modeling, and possibly computing effort. It seems to subsume Ward’s prior work on the Label Interval Calculus, extending the approach to a wider range of engineering design problems.

Data-driven design paradigm in engineering problems

2016 ◽  
Vol 231 (8) ◽  
pp. 1522-1534 ◽  
Author(s):  
Changqing Liu ◽  
Xiaoqian Chen
Keyword(s):  
Engineering Design ◽  
Data Driven ◽  
Decision Strategy ◽  
Design Problems ◽  
Design Decisions ◽  
Uncertainty Factors ◽  
Satellite Design ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Design Paradigm

Engineering design problems can, in general, be discussed under the framework of decision making, namely engineering design decisions. Inherently, accounting for uncertainty factors is an indispensable part in these decision processes. In a sense, the goal of design decisions is to control or reduce the variational effect in decision consequences induced by many uncertainty factors, by optimizing an expected utility objective or other preference functions. In this paper, the value of data in facilitating making engineering design decisions is highlighted, and a data-driven design paradigm for practical engineering problems is proposed. The definition of data in this paradigm is elaborated first. Then the data involvement in a whole stage-based design process is investigated. An overall decision strategy for design problems under the data-driven paradigm is proposed. By a concrete satellite design example, the key ideas of the proposed data-driven design paradigm are demonstrated. Future work is also advised.

Generalized set-propagation operations over relations of more than three variables

1995 ◽  
Vol 9 (3) ◽  
pp. 231-242 ◽  
Author(s):  
William W. Finch ◽  
Allen C. Ward
Keyword(s):  
Optimal Design ◽  
Engineering Design ◽  
Arbitrary Number ◽  
Mechanical Systems ◽  
Real Numbers ◽  
Design Problems ◽  
Closed Intervals ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Class Of Functions

AbstractThis paper extends previously developed generalized set propagation operations to work over relationships among an arbitrary number of variables, thereby expanding the domain of engineering design problems the theory can address. It then narrows its scope to a class of functions and sets useful to designers solving engineering problems: monotonic algebraic functions and closed intervals of real numbers, proving formulas for computing the operations under these conditions. The work is aimed at the automated optimal design of electro-mechanical systems from catalogs of parts; an electronic example illustrates.

A symbiotic organisms search algorithm-based design optimization of constrained multi-objective engineering design problems

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Deniz Ustun ◽  
Serdar Carbas ◽  
Abdurrahim Toktas
Keyword(s):  
Engineering Design ◽  
Multiple Objectives ◽  
Optimal Solutions ◽  
Design Problems ◽  
Symbiotic Organisms Search ◽  
Content Type ◽  
Multi Objective ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Symbiotic Organisms

Purpose In line with computational technological advances, obtaining optimal solutions for engineering problems has become attractive research topics in various disciplines and real engineering systems having multiple objectives. Therefore, it is aimed to ensure that the multiple objectives are simultaneously optimized by considering them among the trade-offs. Furthermore, the practical means of solving those problems are principally concentrated on handling various complicated constraints. The purpose of this paper is to suggest an algorithm based on symbiotic organisms search (SOS), which mimics the symbiotic reciprocal influence scheme adopted by organisms to live on and breed within the ecosystem, for constrained multi-objective engineering design problems. Design/methodology/approach Though the general performance of SOS algorithm was previously well demonstrated for ordinary single objective optimization problems, its efficacy on multi-objective real engineering problems will be decisive about the performance. The SOS algorithm is, hence, implemented to obtain the optimal solutions of challengingly constrained multi-objective engineering design problems using the Pareto optimality concept. Findings Four well-known mixed constrained multi-objective engineering design problems and a real-world complex constrained multilayer dielectric filter design problem are tackled to demonstrate the precision and stability of the multi-objective SOS (MOSOS) algorithm. Also, the comparison of the obtained results with some other well-known metaheuristics illustrates the validity and robustness of the proposed algorithm. Originality/value The algorithmic performance of the MOSOS on the challengingly constrained multi-objective multidisciplinary engineering design problems with constraint-handling approach is successfully demonstrated with respect to the obtained outperforming final optimal designs.

Evaluating the Directed Intuitive Approach for Bioinspired Design

2014 ◽  
Vol 136 (7) ◽  
Author(s):  
Michael W. Glier ◽  
Joanna Tsenn ◽  
Julie S. Linsey ◽  
Daniel A. McAdams
Keyword(s):  
Engineering Design ◽  
Design Method ◽  
Idea Generation ◽  
Design Methods ◽  
Design Knowledge ◽  
Design Problems ◽  
Bioinspired Design ◽  
Engineering Problems ◽  
Novice Designers ◽  
The Impact

Bioinspired design, the practice of looking to nature to find inspiration for solutions to engineering problems, is increasingly a desired approach to design. It allows designers to tap a wealth of time-tested solutions to difficult problems in a domain less considered by designers. Only recently have researchers developed organized, systematic methods for bioinspired design. Traditionally, bioinspired design has been conducted without the benefit of any organized method. Designers relied on the informal “directed intuitive approach” of bioinspired design, which simply directs designers to consider how nature might solve a problem. This paper presents an experiment to explore the impact of the directed approach on idea generation. This experiment is foundationally important to bioinspired engineering design method research. The results of this experiment serve as a fundamental baseline and benchmark for the comparison of more systematic, and often more involved, bioinspired design methods. A group of 121 novice designers are given one of two design problems and instructed to either generate solutions using the directed approach or to generate solutions without being prompted in any additional fashion. Based on the findings presented here, the directed approach offers designers no advantage in the average number of nonredundant ideas, quality, novelty, or variety of the solutions produced. In conclusion, systematic and organized methods for bioinspired design should be sought to effectively leverage nature's design knowledge.

Approximation Assisted Multiobjective Optimization With Combined Global and Local Metamodeling

2012 ◽  
Author(s):  
W. Hu ◽  
K. H. Saleh ◽  
S. Azarm
Keyword(s):  
Multiobjective Optimization ◽  
Engineering Design ◽  
Design Space ◽  
Pareto Optimum ◽  
Optimization Approach ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Computationally Intensive ◽  
Global And Local ◽  
Global Metamodeling

Approximation Assisted Optimization (AAO) is widely used in engineering design problems to replace computationally intensive simulations with metamodeling. Traditional AAO approaches employ global metamodeling for exploring an entire design space. Recent research works in AAO report on using local metamodeling to focus on promising regions of the design space. However, very limited works have been reported that combine local and global metamodeling within AAO. In this paper, a new approximation assisted multiobjective optimization approach is developed. In the proposed approach, both global and local metamodels for objective and constraint functions are used. The approach starts with global metamodels for objective and constraint functions and using them it selects the most promising points from a large number of randomly generated points. These selected points are then “observed”, which means their actual objective/constraint function values are computed. Based on these values, the “best” points are grouped in multiple clustered regions in the design space and then local metamodels of objective/constraint functions are constructed in each region. All observed points are also used to iteratively update the metamodels. In this way, the predictive capabilities of the metamodels are progressively improved as the optimizer approaches the Pareto optimum frontier. An advantage of the proposed approach is that the most promising points are observed and that there is no need to verify the final solutions separately. Several numerical examples are used to compare the proposed approach with previous approaches in the literature. Additionally, the proposed approach is applied to a CFD-based engineering design example. It is found that the proposed approach is able to estimate Pareto optimum points reasonably well while significantly reducing the number of function evaluations.

scholarly journals Bionic Innovative Design of Pipeline Disinfection Robot Based on Biological Text Information

2021 ◽  
Author(s):  
Liang Chen ◽  
Huo Zhang ◽  
Xiao-Min Liu ◽  
De-Hua Fan
Keyword(s):  
Engineering Design ◽  
Design Method ◽  
Bionic Design ◽  
Design Problems ◽  
Innovative Design ◽  
Design Scheme ◽  
Engineering Design Problems ◽  
Text Information ◽  
Processing And Storage ◽  
Bio Engineering

Abstract The traditional bionic innovative design method relies too much on the designer's own experience and inspiration, and the design method could be limited by designer’s own thinking. This paper presents a new bionic design method, proposing one kind of special model for the engineering design problem, through obtaining biological text information, after processing and storage in the database, combined with engineering design problems, obtains the corresponding bionic design prototype, and generates the bionic design scheme through bio-engineering mapping. This design method can effectively avoid trapping designers into their own thinking, and this method is helpful for the generation of innovative design scheme. Finally, the meta design of innovative pipeline disinfection robot is shown as an example to verify the feasibility of the method proposed in this paper.

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