Cognitive Heuristics in Defining Engineering Design Problems

2016 ◽  
Author(s):  
Jaryn A. Studer ◽  
Seda Yilmaz ◽  
Shanna R. Daly ◽  
Colleen M. Seifert
Keyword(s):  
Engineering Design ◽  
Design Problem ◽  
Empirical Studies ◽  
Cognitive Strategies ◽  
Unique Problem ◽  
Design Problems ◽  
Creative Solutions ◽  
Engineering Design Problems ◽  
Systematic Methodology ◽  
Problem Description

This paper explores “problem exploration heuristics,” or cognitive strategies used to identify and reframe design problem descriptions. The way a design problem is structured influences the types of ideas a designer generates; in particular, some framings may lead to more creative solutions and using multiple framings can support diverse solutions. Most existing problem exploration strategies have not been derived from empirical studies of engineering design practice. Thus, in our work, we drew upon a sample of engineering design problems and analyzed how the problem descriptions evolved during design. Examining iterations on the problem description allowed us to identify heuristics evident in designers’ recrafting of problem descriptions. Heuristics were defined based on the elements in each problem description and their perceived role in transforming the problem. We present a systematic methodology for identifying problem exploration heuristics, and describe five unique Problem Exploration Heuristics commonly observed in structuring design briefs.

scholarly journals Optimal Solution of Structural Engineering Design Problems using Crow Search Algorithm

2019 ◽  
Vol 4 (4) ◽  
pp. 968-981
Author(s):  
Madhur Agarwal
Keyword(s):  
Engineering Design ◽  
Design Problem ◽  
Structural Engineering ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Worst Case ◽  
Design Problems ◽  
Beam Design ◽  
Engineering Design Problems ◽  
Better Than

In real world, the structural engineering design problems are large scale non-linear constrained problems. In the present study, crow search algorithm (CSA) is applied to find the optimal solution of structural engineering design problems such as pressure vessel design problem, welded beam design problem and tension/ compression string design problem. The numerical results are compared with the existing results reported in the literature including metaheuristic algorithms and it is found that the results obtained by the crow search algorithm are better than other existing algorithms. Further, the effectiveness of the algorithm is verified to be better than the existing algorithms by statistical analysis using mean, median, best case, and worst case scenarios. The present study confirms that the crow search algorithm may be easily and effectively applied to various structural design problems.

Multi-Objective Optimal Design of Automotive Engine Using Genetic Algorithm

1998 ◽  
Author(s):  
Kikuo Fujita ◽  
Noriyasu Hirokawa ◽  
Shinsuke Akagi ◽  
Shinji Kitamura ◽  
Hideaki Yokohata
Keyword(s):  
Genetic Algorithm ◽  
Engineering Design ◽  
Design Problem ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Total Power ◽  
Design Problems ◽  
Automotive Engine ◽  
Multi Objective ◽  
Engineering Design Problems

Abstract A genetic algorithm based optimization method is proposed for a multi-objective design problem of an automotive engine, that includes several difficulties in practical engineering optimization problems. While various optimization techniques have been applied to engineering design problems, a class of realistic engineering design problems face on a mixture of different optimization difficulties, such as the rugged nature of system response, the numbers of design variables and objectives, etc. In order to overcome such a situation, this paper proposes a genetic algorithm based multi-objective optimization method, that introduces Pareto-optimality based fitness function, similarity based selection and direct real number crossover. This optimization method is also applied to the design problem of an automotive engine with the design criteria on a total power train. The computational examples show the ability of the proposed method for finding a relevant set of Pareto optima.

scholarly journals ASSESSMENT OF EVIDENCE FOR THE EFFECTIVENESS OF DIFFERENT IDEATION PRACTICES

2012 ◽  
Author(s):  
Antony J Hodgson ◽  
HF Machiel Van Der Loos
Keyword(s):  
Engineering Design ◽  
Design Process ◽  
Design Problem ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Assessment Of Evidence ◽  
Engineering Schools

While most engineering schools substantially agree on the general form of the design process that should be used to address engineering design problems, surprisingly little is known about the actual practical effectiveness of many recommended techniques. In this paper and presentation, we review some of the recent evidence concerning the effectiveness of several well- known practices related to ideation - i.e., generating alternative potential solutions to a design problem.

Optimal Designs by Means of Genetic Algorithms

2021 ◽  
pp. 344-354
Author(s):  
Lata Nautiyal ◽  
Preeti Shivach ◽  
Mangey Ram
Keyword(s):  
Genetic Algorithms ◽  
Modeling And Simulation ◽  
Engineering Design ◽  
Design Problem ◽  
High Performance ◽  
Mechanical Engineering ◽  
Design Parameters ◽  
Design Problems ◽  
Simulation Tools ◽  
Engineering Design Problems

With the advancement in contemporary computational and modeling skills, engineering design completely depends upon on variety of computer modeling and simulation tools to hasten the design cycles and decrease the overall budget. The most difficult design problem will include various design parameters along with the tables. Finding out the design space and ultimate solutions to those problems are still biggest challenges for the area of complex systems. This chapter is all about suggesting the use of Genetic Algorithms to enhance maximum engineering design problems. The chapter recommended that Genetic Algorithms are highly useful to increase the High-Performance Areas for Engineering Design. This chapter is established to use Genetic Algorithms to large number of design areas and delivered a comprehensive conversation on the use, scope and its applications in mechanical engineering.

Optimal Designs by Means of Genetic Algorithms

2018 ◽  
pp. 151-161
Author(s):  
Lata Nautiyal ◽  
Preeti Shivach ◽  
Mangey Ram
Keyword(s):  
Genetic Algorithms ◽  
Modeling And Simulation ◽  
Engineering Design ◽  
Design Problem ◽  
High Performance ◽  
Mechanical Engineering ◽  
Design Parameters ◽  
Design Problems ◽  
Simulation Tools ◽  
Engineering Design Problems

With the advancement in contemporary computational and modeling skills, engineering design completely depends upon on variety of computer modeling and simulation tools to hasten the design cycles and decrease the overall budget. The most difficult design problem will include various design parameters along with the tables. Finding out the design space and ultimate solutions to those problems are still biggest challenges for the area of complex systems. This chapter is all about suggesting the use of Genetic Algorithms to enhance maximum engineering design problems. The chapter recommended that Genetic Algorithms are highly useful to increase the High-Performance Areas for Engineering Design. This chapter is established to use Genetic Algorithms to large number of design areas and delivered a comprehensive conversation on the use, scope and its applications in mechanical engineering.

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.

Biologically Inspired Design: A Macrocognitive Account

2010 ◽  
Author(s):  
Swaroop S. Vattam ◽  
Michael Helms ◽  
Ashok K. Goel
Keyword(s):  
Biological Sciences ◽  
Information Processing ◽  
Engineering Design ◽  
Design Patterns ◽  
Information Processing Model ◽  
Biologically Inspired ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Other Information ◽  
Biologically Inspired Design

Biologically inspired engineering design is an approach to design that espouses the adaptation of functions and mechanisms in biological sciences to solve engineering design problems. We have conducted an in situ study of designers engaged in biologically inspired design. Based on this study we develop here a macrocognitive information-processing model of biologically inspired design. We also compare and contrast the model with other information-processing models of analogical design such as TRIZ, case-based design, and design patterns.

scholarly journals Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-22 ◽  
Author(s):  
Zhiming Li ◽  
Yongquan Zhou ◽  
Sen Zhang ◽  
Junmin Song
Keyword(s):  
Engineering Design ◽  
Function Optimization ◽  
Superior Performance ◽  
Local Optimum ◽  
Lévy Flight ◽  
Design Problems ◽  
Levy Flight ◽  
Spiral Path ◽  
Straight Line ◽  
Engineering Design Problems

The moth-flame optimization (MFO) algorithm is a novel nature-inspired heuristic paradigm. The main inspiration of this algorithm is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a spiral path around artificial lights. Aiming at the phenomenon that MFO algorithm has slow convergence and low precision, an improved version of MFO algorithm based on Lévy-flight strategy, which is named as LMFO, is proposed. Lévy-flight can increase the diversity of the population against premature convergence and make the algorithm jump out of local optimum more effectively. This approach is helpful to obtain a better trade-off between exploration and exploitation ability of MFO, thus, which can make LMFO faster and more robust than MFO. And a comparison with ABC, BA, GGSA, DA, PSOGSA, and MFO on 19 unconstrained benchmark functions and 2 constrained engineering design problems is tested. These results demonstrate the superior performance of LMFO.

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