Analogical stimuli retrieval approach based on R-SBF ontology model

Jia, Lizhen; Peng, Qingjin; Tan, Runhua; Zhu, Xuehong

doi:10.1080/09544828.2019.1643830

Cited by 15 publications

(3 citation statements)

References 47 publications

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“…Structure mapping theory is the research result of analogy design for relational structure problems. Its key idea is to extract the structure between various factors in the source design, match it with the structure of the target design, and use the solution method of source problems to solve the target problem [90,91]. Structural mapping occurs at multiple levels, such as surface features, attributes, and relationships.…”

Section: Effect Structure Mappingmentioning

confidence: 99%

Product Improvement Using Knowledge Mining and Effect Analogy

Wang,

Tan,

Peng

2024

Applied Sciences

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Different from new product development, design improvement aims to solve the problems of existing products. Although design knowledge and effect tools have been applied in product improvement, the existing methods for design improvement are limited in their specific application areas. A general method of product improvement is proposed in this paper using the knowledge mining and effect analogy. The length–time dimension is introduced to link the problem analysis and problem-solving for the first time. This method includes the effect knowledge base construction, length–time dimension extraction, effect retrieval, effect ranking, analogy object selection, and effect structure mapping. This method integrates a variety of algorithms and software tools in design knowledge mining to improve the efficiency of the effect analogy for product improvement. Through the comparative analysis of three effect retrieval methods and design improvement of a button battery ring device, the superiority and feasibility of the proposed method are verified.

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Section: Effect Structure Mappingmentioning

confidence: 99%

Product Improvement Using Knowledge Mining and Effect Analogy

Wang,

Tan,

Peng

2024

Applied Sciences

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“…Similar functions can be achieved with different structures and principles. The analogy process is the transfer of structures or principles with similar functions from one system to another new system to achieve the desired function of the new system [75]. Guided by analogy, we can build radical concepts of the target subsystem with cross-domain knowledge gained from the above process.…”

Section: Knowledge Transfer and Radical Concept Generationmentioning

confidence: 99%

Radical Concept Generation Inspired by Cross-Domain Knowledge

Zhang

Tan

2022

Applied Sciences

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Cross-domain knowledge can stimulate radical concept generation (RCG), but there is a lack of guidance to utilize the cross-domain knowledge for RCG. This paper proposes an effective process of RCG in four steps: (1) Identifying radical technology opportunities based on analysis of the dynamic relationship between demand and technical performances; (2) Applying laws and lines of the technological system evolution to determine the search direction of cross-domain knowledge in a qualitative perspective; (3) Taking the minimum complementary distance measure for appropriate cross-domain knowledge in a quantitative perspective; (4) Forming radical concepts using the cross-domain knowledge as inspirations. The method reduces risks and costs caused by uncertainty in RCG and has potential to fertilize research on radical innovation and knowledge-based innovation. A radical concept of gas generator demonstrates the effectiveness of the proposed method.

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“…For determining the necessary routes of product improvement, there are different methods available, such as brainstorming, mind mapping, SCAMPER, and TRIZ [18][19][20][21]. For determining the necessary routes of technological developments, there are various methods available, such as technology prediction, bionic designs, and analogical designs [22][23][24]. For determining shifts in user needs, there are various established methods, such as questionnaires, focus groups, and customer interviews; these approaches gather users' voices and understand users' needs, the problems in existing products, and the gaps that exist between user expectations and reality [25].…”

Section: Introductionmentioning

confidence: 99%

Identification of Innovative Opportunities Based on Product Scenario Evolution

Wang,

Tan,

Peng

et al. 2023

Systems

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Innovation is a key factor for product development. Identifying innovative opportunities is the first step in innovative product design. Traditional methods of identifying innovative opportunities, such as market surveys and brainstorming, are limited by product users’ and designers’ experiences and lack systematic approaches to generate breakthrough innovations. This paper proposes a method to identify innovative opportunities based on product scenario evolution. The method models a product scenario based on product scenario elements, states, and behaviors. A Type II hierarchical function model is constructed based on the transformation and abstraction hierarchy of the product function model to identify target elements for the scenario evolution. Based on the theory of basic element extension and needs evolution characteristics, the method of extending target scenario elements is proposed. Based on the new scenario element sets and their impact, diffusion, identification, and evaluation methods are proposed for innovation opportunities. Potential opportunities are explored for product innovation from a scenario evolutionary perspective, which updates knowledge and technology reserves and finds new market opportunities for industries. The feasibility and effectiveness of the method are verified using the innovative design of a polyethylene (PE) pipeline hot-melt welding machine.

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Analogical stimuli retrieval approach based on R-SBF ontology model

Cited by 15 publications

References 47 publications

Product Improvement Using Knowledge Mining and Effect Analogy

Product Improvement Using Knowledge Mining and Effect Analogy

Radical Concept Generation Inspired by Cross-Domain Knowledge

Identification of Innovative Opportunities Based on Product Scenario Evolution

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