An Experimental Study on the Characteristics of Concept Space in Design Concept Generation Process

2017 ◽  
Vol 2017.27 (0) ◽  
pp. 2204
Author(s):  
Takahiro Kawahara ◽  
Hiroshi Takano ◽  
Yutaka NOMAGUCHI ◽  
Kikuo FUJITA
Keyword(s):  
Experimental Study ◽  
Design Concept ◽  
Generation Process ◽  
Concept Generation ◽  
Concept Space

A Knowledge Extraction and Design Support System for Supporting Industrial and Product Design

2017 ◽  
Vol 4 (2) ◽  
pp. 1-18
Author(s):  
W. B. Lee ◽  
W. M. Wang ◽  
C. F. Cheung ◽  
Z. H. Wu
Keyword(s):  
Product Design ◽  
Support System ◽  
Knowledge Extraction ◽  
Design Stage ◽  
Generation Process ◽  
Concept Generation ◽  
Design Support ◽  
Design Concepts ◽  
Design Support System ◽  
Design Ideas

Industrial and product design involves a lot of unstructured information for the generation of innovative product design ideas. However, the generation of innovative design concepts is not only time consuming but also heavily relies on the experience of product designers. Most existing systems focus mainly on the technical aspects of realizing product designs, which are inadequate to support concept generation process at the pre-design stage. In this paper, a knowledge extraction and design support system (KEDSS) is presented. The system aims at extracting key design concepts and depicting the trends of these concepts from the massive amount of unstructured design information in the open domain. A summary report, a related concept list, and concept trend graphs are produced based on the inputs of the designers' design ideas. A series of experiments have been conducted to measure the performance of the system. Moreover, the system has been successfully trial implemented as part of a public service platform for modern industrial design of injection molding machinery and equipment.

An I-Ching-TRIZ-Inspired Approach to Design Concept Generation

2009 ◽  
Author(s):  
Duc Truong Pham ◽  
Huimin Liu
Keyword(s):  
Design Concept ◽  
Design Solution ◽  
Concept Generation ◽  
I Ching ◽  
Innovative Design ◽  
New Approach ◽  
Design Concepts ◽  
Aircraft Cabin ◽  
Design Specifications ◽  
Design Solutions

This paper presents a new approach to producing innovative design concepts. The proposed approach involves extending the inventive principles of TRIZ by integrating other TRIZ and TRIZ-inspired tools. The set of inventive principles is then structured according to a framework adapted from I-Ching and represented using TRIZ’s Behaviour-Entity (BE) formalism to which constraints have also been added. The adoption of the BE representation enables a reduction in the amount of repeated information in the inventive principles. A BE pair contains information on a design solution. A Behaviour-Entity-Constraint (BEC) triple additionally has information on constraints on the solution. The BEC representation thus facilitates the retrieval and generation of design solutions from design specifications. The paper uses the problem of laying out seats in an aircraft cabin to illustrate advantages of the proposed approach.

The effects of time and incubation on design concept generation

2014 ◽  
Vol 35 (5) ◽  
pp. 500-526 ◽  
Author(s):  
Joanna Tsenn ◽  
Olufunmilola Atilola ◽  
Daniel A. McAdams ◽  
Julie S. Linsey
Keyword(s):  
Design Concept ◽  
Concept Generation

Mining Functional Model Graphs to Find Product Design Heuristics With Inclusive Design Illustration

2013 ◽  
Vol 13 (4) ◽  
Author(s):  
Shraddha Sangelkar ◽  
Daniel A. McAdams
Keyword(s):  
Data Mining ◽  
Functional Model ◽  
Inclusive Design ◽  
Generation Process ◽  
Graph Visualization ◽  
Concept Generation ◽  
Product Representation ◽  
Graph Data ◽  
Design Heuristics ◽  
Functional Models

Engineering design heuristics offer the potential to improve the design process and resultant designs. Currently, heuristics are empirically derived by experts. The goal of this paper is to automate the heuristics generation process. Functional modeling, a well-established product representation framework, is applied in this research to abstract the intended functionality of a product. Statistically significant heuristics, extracted from a database of functional models, serve as design suggestions or guidelines for concept generation. The heuristics can further be applied to automate portions of the concept generation process. Prior research efforts in automated concept generation rely heavily on the design repository. The repository needs to be appended for broader categories of design problems, and, at the same time, a tool for quick analysis of the expanded repository is required. An automated heuristic extraction process has the capability to efficiently mine the updated repositories and find new heuristics for design practice. A key objective of this research is to develop design heuristics applicable in the diverse and challenging domain of inclusive design. The research applies graph theory for mathematical representation of the functional model, graph visualization for comprehending graphs, and graph data mining to extract heuristics. The results show that the graphical representation of functional models along with graph visualization allows quick updates to the design repository. In addition, we show that graph data mining has the capability to efficiently search for new design heuristics from the updated repository.

Constructive simulation of creative concept generation process in design: a research method for difficult-to-observe design-thinking processes

2012 ◽  
Vol 23 (4) ◽  
pp. 297-321 ◽  
Author(s):  
Toshiharu Taura ◽  
Eiko Yamamoto ◽  
Mohd Yusof Nor Fasiha ◽  
Masanori Goka ◽  
Futoshi Mukai ◽  
...  
Keyword(s):  
Research Method ◽  
Design Thinking ◽  
Generation Process ◽  
Concept Generation ◽  
Thinking Processes

scholarly journals Building Numerical Design Matrix and Managing Functional Couplings for Concept Improvement of The Existing Product

2019 ◽  
Vol 301 ◽  
pp. 00011
Author(s):  
Chu-Yi Wang ◽  
Ang Liu ◽  
Stephen Lu
Keyword(s):  
Design Theory ◽  
Design Concept ◽  
Design Parameters ◽  
Phase Method ◽  
Functional Coupling ◽  
Design Matrix ◽  
Concept Generation ◽  
Functional Requirements ◽  
Axiomatic Design Theory ◽  
Concept Improvement

Because parametric values are unknown during initial concept generation, the Axiomatic Design Theory uses the binary design matrix (DM) to represent the coupling relationship between functional requirements and design parameters. However, given an existing product, it would be possible to employ the numerical DM that has more detailed information than the binary DM to help improve the design concept. This paper proposed a two-phase method to create a numerical DM in phase I and manage the functional couplings in phase II for concept improvement of existing product. A decomposition-definition-levelling framework and the Puritan-Bennett’s 0-1-3-9 level rating are employed to evaluate the system impact of each functional coupling to create the numerical DM of an existing design concept. The Design Coupling Sequence (DCS) approach was extended to use the numerical DM to improve this design concept. Compared with other numerical matrices for product development and the structured approach by Su et al., our method is more generic and faster, providing useful details yet still able to maintain the dominance of the high-level couplings.

Design Concept Generation With Variational Deep Embedding Over Comprehensive Optimization

2021 ◽  
Author(s):  
Kikuo Fujita ◽  
Kazuki Minowa ◽  
Yutaka Nomaguchi ◽  
Shintaro Yamasaki ◽  
Kentaro Yaji
Keyword(s):  
Design Concept ◽  
Concept Generation ◽  
Design Concepts ◽  
Computational Optimization ◽  
Comprehensive Exploitation ◽  
New Concepts ◽  
Latent Space ◽  
Deep Embedding ◽  
Bridge Structures ◽  
Classification Capability

Abstract This paper proposes a framework for generating design concepts through the loop of comprehensive exploitation and consequent exploration. The former is by any sophisticated optimization such as topology optimization with diversely different. The latter realization is due to the variational deep embedding (VaDE), a deep learning technique with classification capability. In the process of design concept generation first, exploitation through computational optimization generates various possibilities of design entities. Second, VaDE learns them. This learning encodes the clusters of similar entities over the latent space with smaller dimensions. The clustering result reveals some design concepts and identifies voids where as-yet-unrecognized design concepts are prospective. Third, the decoder of the learned VaDE generates some possibilities for new design entities. Forth such new entities are examined, and relevant new conditions will trigger further exploitation by the optimization. In this paper, this framework is implemented for and applied to the conceptual design problem of bridge structures. This application demonstrates that the framework can identify voids over the latent space and explore the possibility of new concepts. This paper brings up some discussion on the promises and possibilities of the proposed framework.

scholarly journals “Collaborating To Success”: An Experimental Study of Group Idea Generation Techniques

2005 ◽  
Author(s):  
J. S. Linsey ◽  
M. G. Green ◽  
J. T. Murphy ◽  
K. L. Wood ◽  
A. B. Markman
Keyword(s):  
Experimental Study ◽  
Empirical Data ◽  
Idea Generation ◽  
Factorial Experiment ◽  
Concept Generation

Numerous concept generation methods have been developed that can assist an engineer in the initial phases of design. Unfortunately, limited empirical data is available to guide users in selecting preferred techniques. This study systematically investigates underlying factors of four well-used and documented techniques: Brainsketching, Gallery, 6-3-5, and C-Sketch. These techniques are resolved into their key parameters and a factorial experiment is performed to understand how the key parameters affect the outcomes of the techniques. The factors chosen for this study include: how ideas are displayed to participants (all are viewed at once or exchanged between participants, “rotational viewing”) and the mode used to communicate ideas (written words only, sketches only, or a combination of written words and sketches). This study also provides a method for measuring the quantity of ideas generated when the ideas are represented in the form of both sketches and words. A number of interesting findings are produced from the study. First, the study shows that individuals gain a significant number of ideas from their teammates. Ideas when shared, can foster new idea tracks, more complete layouts, and diverse synthesis. Second, the systematic exchange of a set of ideas between participants produces a greater quantity of ideas than having all ideas displayed in a gallery form. This result shows that techniques like 6-3-5 or C-Sketch, where each person views only a subset of all the team’s ideas at any given time, are more likely to produce a larger quantity of ideas than techniques where individuals can continuously view all the ideas the team has generated. Finally, as teams developed ideas, the quality improved. This result is a consequence of the teamsharing environment and, in conjunction with quantity of concepts, validates the effectiveness of group idea generation.

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