Evaluating the impact of Idea-Inspire 4.0 on analogical transfer of concepts

2018 ◽  
Vol 32 (4) ◽  
pp. 431-448 ◽  
Author(s):  
L. Siddharth ◽  
Amaresh Chakrabarti
Keyword(s):  
Engineering Design ◽  
Biological System ◽  
Biological Systems ◽  
Knowledge Bases ◽  
Design Problems ◽  
Function Decomposition ◽  
Engineering Design Problems ◽  
Biological Domain ◽  
Biological Concepts ◽  
The Impact

AbstractThe biological domain has the potential to offer a rich source of analogies to solve engineering design problems. However, due to the complexity embedded in biological systems, adding to the lack of structured, detailed, and searchable knowledge bases, engineering designers find it hard to access the knowledge in the biological domain, which therefore poses challenges in understanding the biological concepts in order to apply these concepts to engineering design problems. In order to assist the engineering designers in problem-solving, we report, in this paper, a web-based tool called Idea-Inspire 4.0 that supports analogical design using two broad features. First, the tool provides access to a number of biological systems using a searchable knowledge base. Second, it explains each one of these biological systems using a multi-modal representation: that is, using function decomposition model, text, function model, image, video, and audio. In this paper, we report two experiments that test how well the multi-modal representation in Idea-Inspire 4.0 supports understanding and application of biological concepts in engineering design problems. In one experiment, we use Bloom's method to test “analysis” and “synthesis” levels of understanding of a biological system. In the next experiment, we provide an engineering design problem along with a biological-analogous system and examine the novelty and requirement-satisfaction (two major indicators of creativity) of resulting design solutions. In both the experiments, the biological system (analogue) was provided using Idea-Inspire 4.0 as well as using a conventional text-image representation so that the efficacy of Idea-Inspire 4.0 is tested using a benchmark.

scholarly journals Representing Complex Analogues Using a Function Model to Support Conceptual Design

2018 ◽  
Author(s):  
L. Siddharth ◽  
Amaresh Chakrabarti ◽  
Srinivasan Venkataraman
Keyword(s):  
Complex Systems ◽  
Engineering Design ◽  
Levels Of Abstraction ◽  
Function Model ◽  
Design Problems ◽  
Web Based ◽  
Proposed Model ◽  
Engineering Design Problems ◽  
Biological Domain ◽  
Engineered Systems

Analogical design has been a long-standing approach to solve engineering design problems. However, it is still unclear as to how analogues should be presented to engineering design in order to maximize the utility of these. The utility is minimal when analogues are complex and belong to other domain (e.g., biology). Prior work includes the use of a function model called SAPPhIRE to represent over 800 biological and engineered systems. SAPPhIRE stands for the entities: States, Actions, Parts, Phenomena, Inputs, oRgans, and Effects that together represent the functionality of a system at various levels of abstraction. In this paper, we combine instances of SAPPhIRE model for representing complex systems (also from the biological domain). We use an electric buzzer to illustrate and compare the efficacy of this model in explaining complex systems with that of a well-known model from literature. The use of multiple-instance SAPPhIRE model instances seems to provide a more comprehensive explanation of a complex system, which includes elements of description that are not present in other models, providing an indication as to which elements might have been missing from a given description. The proposed model is implemented in a web-based tool called Idea-Inspire 4.0, a brief introduction of which is also provided.

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.

scholarly journals Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 341
Author(s):  
Bugra Alkan ◽  
Malarvizhi Kaniappan Chinnathai
Keyword(s):  
Engineering Design ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Computation Time ◽  
Population Based ◽  
Performance Comparison ◽  
Design Problems ◽  
Component Design ◽  
Engineering Design Problems ◽  
Optimisation Algorithms

The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for such problems. However, a plethora of novel metaheuristic algorithms are developed and constantly improved and hence it is important to evaluate the applicability of the novel optimisation strategies and compare their performance using real-world engineering design problems. Therefore, in this paper, eight recent population-based metaheuristic optimisation algorithms—African Vultures Optimisation Algorithm (AVOA), Crystal Structure Algorithm (CryStAl), Human-Behaviour Based Optimisation (HBBO), Gradient-Based Optimiser (GBO), Gorilla Troops Optimiser (GTO), Runge–Kutta optimiser (RUN), Social Network Search (SNS) and Sparrow Search Algorithm (SSA)—are applied to five different mechanical component design problems and their performance on such problems are compared. The results show that the SNS algorithm is consistent, robust and provides better quality solutions at a relatively fast computation time for the considered design problems. GTO and GBO also show comparable performance across the considered problems and AVOA is the most efficient in terms of computation time.

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