A Set-Based System for Eliminating Infeasible Designs in Engineering Problems Dominated by Uncertainty

1997 ◽  
Author(s):  
William W. Finch ◽  
Allen C. Ward
Keyword(s):  
Electronic Circuit ◽  
Predicate Logic ◽  
Software Tool ◽  
Design Parameters ◽  
Engineering Systems ◽  
Multiple Sources ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Prototype Software

Abstract This paper gives an overview of a system which eliminates infeasible designs from engineering design problems dominated by multiple sources of uncertainty. It outlines methods for representing constraints on sets of values for design parameters using quantified relations, a special class of predicate logic expressions which express some of the causal information inherent in engineering systems. The paper extends constraint satisfaction techniques and describes elimination algorithms that operate on quantified relations and catalogs of toleranced or adjustable parts. It demonstrates the utility of these tools on a simple electronic circuit, and describes their implementation and test in a prototype software tool.

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.

An Overview of Inference Mechanisms for Quantified Relations

1998 ◽  
Author(s):  
William W. Finch
Keyword(s):  
Predicate Logic ◽  
Mathematical Treatment ◽  
Engineering Systems ◽  
Design Constraint ◽  
Design Problems ◽  
Interval Mathematics ◽  
New Class ◽  
Engineering Design Problems ◽  
Feasible Sets ◽  
And Performance

Abstract This paper temporarily sheds formal mathematical treatment and presents a more intuitive overview of inference mechanisms for quantified relations. These predicate logic expressions are a new class of design constraint among sets of variations affecting the design and performance of engineering systems. Simple examples illustrate the use of quantified relations to infer constraints on the membership of feasible sets. A small design problem from the electronics domain joins the mathematical tools with engineering concepts. A brief comparison demonstrates the advantages of this approach over conventional interval mathematics. This paper’s objective is to illustrate the application of quantified relations and their associated methods to engineering design problems.

Parameter Optimization in the Design of Fuel Transmission System

2004 ◽  
Author(s):  
Anhua Mei ◽  
Ken Aung
Keyword(s):  
Design Parameters ◽  
Design Problems ◽  
Total Cost ◽  
Engineering Design Problems ◽  
Practical Engineering ◽  
Present Worth ◽  
Optimum Model ◽  
Mathcad Software ◽  
Relationship Of

This paper presents a methodology to construct an optimum model based on the Present Worth of Uniform Series of Amount (PWUSA) method. This paper uses the design of a gas pipeline for a power plant that recovers work at the destination as a case study. The relationship of system parameters has been developed by thermodynamics principles. The objective function is construct by combining cost of each component in the system. The main goal of the paper is to minimize the total cost of the system by optimizing the design parameters. The paper gives the detail procedure involved in minimizing the total cost of the system using MathCAD software package. The results show that MathCAD software provides a fast and easy way to conduct optimization of practical engineering design problems.

scholarly journals Material Generation Algorithm: A Novel Metaheuristic Algorithm for Optimization of Engineering Problems

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 859
Author(s):  
Siamak Talatahari ◽  
Mahdi Azizi ◽  
Amir H. Gandomi
Keyword(s):  
Optimization Problems ◽  
Design Problems ◽  
Generation Algorithm ◽  
Material Chemistry ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Different Dimensions ◽  
Overall Performance ◽  
The Mean ◽  
Metaheuristic Optimization Algorithms

A new algorithm, Material Generation Algorithm (MGA), was developed and applied for the optimum design of engineering problems. Some advanced and basic aspects of material chemistry, specifically the configuration of chemical compounds and chemical reactions in producing new materials, are determined as inspirational concepts of the MGA. For numerical investigations purposes, 10 constrained optimization problems in different dimensions of 10, 30, 50, and 100, which have been benchmarked by the Competitions on Evolutionary Computation (CEC), are selected as test examples while 15 of the well-known engineering design problems are also determined to evaluate the overall performance of the proposed method. The best results of different classical and new metaheuristic optimization algorithms in dealing with the selected problems were taken from the recent literature for comparison with MGA. Additionally, the statistical values of the MGA algorithm, consisting of the mean, worst, and standard deviation, were calculated and compared to the results of other metaheuristic algorithms. Overall, this work demonstrates that the proposed MGA is able provide very competitive, and even outstanding, results and mostly outperforms other metaheuristics.

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.

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.

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.

Wisdom of Microcrowds in Evaluating Solutions to Esoteric Engineering Problems

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Nurcan Gecer Ulu ◽  
Michael Messersmith ◽  
Kosa Goucher-Lambert ◽  
Jonathan Cagan ◽  
Levent Burak Kara
Keyword(s):  
Social Sciences ◽  
Computational Models ◽  
Engineering Problem ◽  
Wisdom Of Crowds ◽  
Specialized Knowledge ◽  
Main Hypothesis ◽  
Design Problems ◽  
Domain Experts ◽  
Engineering Design Problems ◽  
Engineering Problems

A multitude of studies in economics, psychology, political and social sciences have demonstrated the wisdom of crowds (WoC) phenomenon, where the collective estimate of a group can be more accurate than estimates of individuals. While WoC is observable in such domains where the participating individuals have experience or familiarity with the question at hand, it remains unclear how effective WoC is for domains that traditionally require deep expertise or sophisticated computational models to estimate objective answers. This work explores how effective WoC is for engineering design problems that are esoteric in nature, that is, problems (1) whose solutions traditionally require expertise and specialized knowledge, (2) where access to experts can be costly or infeasible, and (3) in which previous WoC studies with the general population have been shown to be highly ineffective. The main hypothesis in this work is that in the absence of experts, WoC can be observed in groups that consist of practitioners who are defined to have a base familiarity with the problems in question but not necessarily domain experts. As a way to emulate commonly encountered engineering problem-solving scenarios, this work studies WoC with practitioners that form microcrowds consisting of 5–15 individuals, thereby giving rise to the term the wisdom of microcrowds (WoMC). Our studies on design evaluations show that WoMC produces results whose mean is in the 80th percentile or better across varying crowd sizes, even for problems that are highly nonintuitive in nature.

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.

scholarly journals DTSMA: Dominant Swarm with Adaptive T-distribution Mutation-based Slime Mould Algorithm

2022 ◽  
Vol 19 (3) ◽  
pp. 2240-2285
Author(s):  
Shihong Yin ◽  
◽  
Qifang Luo ◽  
Yanlian Du ◽  
Yongquan Zhou ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Robot Manipulator ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
Design Problems ◽  
Slime Mould ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Inverse Kinematics Problem ◽  
T Distribution

<abstract> <p>The slime mould algorithm (SMA) is a metaheuristic algorithm recently proposed, which is inspired by the oscillations of slime mould. Similar to other algorithms, SMA also has some disadvantages such as insufficient balance between exploration and exploitation, and easy to fall into local optimum. This paper, an improved SMA based on dominant swarm with adaptive t-distribution mutation (DTSMA) is proposed. In DTSMA, the dominant swarm is used improved the SMA's convergence speed, and the adaptive t-distribution mutation balances is used enhanced the exploration and exploitation ability. In addition, a new exploitation mechanism is hybridized to increase the diversity of populations. The performances of DTSMA are verified on CEC2019 functions and eight engineering design problems. The results show that for the CEC2019 functions, the DTSMA performances are best; for the engineering problems, DTSMA obtains better results than SMA and many algorithms in the literature when the constraints are satisfied. Furthermore, DTSMA is used to solve the inverse kinematics problem for a 7-DOF robot manipulator. The overall results show that DTSMA has a strong optimization ability. Therefore, the DTSMA is a promising metaheuristic optimization for global optimization problems.</p> </abstract>

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