Effects based stimulation of ideas in design and engineering
File(s)Zhihua-W-2013-PhD-Thesis.pdf (12.19 MB) EffectsGeniusCode.rar (10.39 MB)
The thesis for PhD of Zhihua Wang
the programme code of the system
Author(s)
Wang, Zhihua
Type
Thesis
Abstract
The landmark date that modern researchers started to focus on creativity relates to J. P. Guiford’s 1950 address to the American Psychological Association. From then on, investigations mainly focused on individual’s personality traits to identify their associations with creativity. In recent decades, social factors have gradually been defined as playing a crucial role in creativity. The main four factors on the performance of creative thinking process are: motivation, expertise, sponsorship, and communication skills.
The creativity engine is a model that uses the analogy of a combustion engine with inputs (four factors) and creative outputs. The short term memory is refreshed in order to retain information while also supplying cues to enable the effective search of long term memory where solutions reside.
In modern design processes, designers often face a design task that relates to fields they are relatively unfamiliar with and the knowledge accumulation they have (long term memory) is not specific to the design task. To compensate for this shortage, an effects database system named “Effect Genius” has been developed and implemented to provide problem related effects and principles to assist designers rapidly access to knowledge guidance from experts at any stage of the design process. A web information gathering and analysis function is also implemented to update the data in the Effects Genius and identify new proposed effects from published data sources.
Two case studies were explored to evaluate the effectiveness and efficiency of the Effects Genius system in the idea generation process, which has three steps (keyword conclusion, related effects analysis, and idea generation). Some users also provided feedback on their usages of the system.
The comprehensive performance of the example ideas in the two case studies and positive feedback from other users demonstrate that the data gathered by the information gathering system have potential benefits for design tasks and the Effects Genius system indeed stimulates the idea generation process by suggesting design-related effects.
The creativity engine is a model that uses the analogy of a combustion engine with inputs (four factors) and creative outputs. The short term memory is refreshed in order to retain information while also supplying cues to enable the effective search of long term memory where solutions reside.
In modern design processes, designers often face a design task that relates to fields they are relatively unfamiliar with and the knowledge accumulation they have (long term memory) is not specific to the design task. To compensate for this shortage, an effects database system named “Effect Genius” has been developed and implemented to provide problem related effects and principles to assist designers rapidly access to knowledge guidance from experts at any stage of the design process. A web information gathering and analysis function is also implemented to update the data in the Effects Genius and identify new proposed effects from published data sources.
Two case studies were explored to evaluate the effectiveness and efficiency of the Effects Genius system in the idea generation process, which has three steps (keyword conclusion, related effects analysis, and idea generation). Some users also provided feedback on their usages of the system.
The comprehensive performance of the example ideas in the two case studies and positive feedback from other users demonstrate that the data gathered by the information gathering system have potential benefits for design tasks and the Effects Genius system indeed stimulates the idea generation process by suggesting design-related effects.
Version
Open Access
Date Issued
2013-06
Date Awarded
2013-12
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Childs, Peter
Aurisicchio, Marco
Publisher Department
Mechanical Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)