Graph-based knowledge reuse for supporting knowledge-driven decision-making in new product development

Zhang, Chao; Zhou, Guanghui; Lu, Qi; Chang, Fengtian

doi:10.1080/00207543.2017.1351643

Cited by 78 publications

(29 citation statements)

References 29 publications

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“…Chen et al proposed a method for the optimal conceptual design synthesis based on the distributed resource environment to promote the design efficiency and innovation (Chen and Xie 2017). Zhang et al presented a graph-based approach to knowledge reuse for knowledge-driven decision-making in new product development (Zhang et al 2017). Szejka et al proposed a review to analyse the main researches on semantic interoperability field (Szejka et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Digital twin-driven product design framework

Tao

Sui

Liu

et al. 2018

International Journal of Production Research

694

289

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With the advent of new generation information technologies in industry and product design, the big data-driven product design era has arrived. However, the big data-driven product design mainly places emphasis on the analysis of physical data rather than the virtual models, in other words, the convergence between product physical and virtual space is usually absent. Digital twin, a new emerging and fast growing technology which connects the physical and virtual world, has attracted much attention worldwide recently. This paper presents a new method for product design based on the digital twin approach. The development of product design is briefly introduced first. The framework of digital twin-driven product design (DTPD) is then proposed and analysed. A case is presented to illustrate the application of the proposed DTPD method.

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Section: Introductionmentioning

confidence: 99%

Digital twin-driven product design framework

Tao

Sui

Liu

et al. 2018

International Journal of Production Research

694

289

View full text Add to dashboard Cite

show abstract

“…Although research on ontology is rooted in computer science, ontology is domain neutral and widely applied for knowledge modeling and knowledge reuse. It is exploited in SE&D with many purposes, e.g., supporting design for additive manufacturing (DFAM) [34], supporting manufacturing decision making [35], representing prediction decision tree in manufacturing networks [36], representing design decision hierarchies [37], supporting decision-making in new product development [38], and supporting systematic design space exploration [39]. Notwithstanding, for supporting experimental design in SE&D there is little published work on knowledge reuse or ontology developing.…”

Section: Experiments and Ontology-based Knowledge Modelingmentioning

confidence: 99%

A Reusable and Executable Information Model of Experiments on Human Decision Making in Systems Engineering and Design

et al. 2020

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Understanding decision making in design is becoming increasingly important within systems engineering and design research. While controlled experiments are used for testing hypotheses and are playing a significant role in developing theories, there is a lack of support for capturing and reusing knowledge associated with experimental design. The goal of this paper is to model the information required for designing and executing experiments, thereby enabling the reuse of past experimental designs. Using declarative formulations of four aspects of an experiment, namely, problem, process, participants and incentives, we extract the generic elements and standardize the structure of information as a Decision Experiment Design Support (DEDS) template, based on which an ontology is developed. The information necessary to execute an experiment is archived as DEDS templates and represented by a frame-based ontology that can be reused from one experiment to another. The approach is illustrated using two example experiments for studying the decision strategies of information acquisition, and the impact of domain knowledge on decisions. This paper offers a fundamental step towards reducing the barriers in designing and executing behavioral experiments for design and systems engineering research.

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“…In this specific case, the ontology-based approach could result less effective than MCDA in terms of usability in practice. Adopting knowledge-graph (e.g., [26]) presents the clear advantage to introduce visualizations. However, the interpretation of such networks may be very subjective even if supported by network analysis techniques.…”

Section: A Comparative Studymentioning

confidence: 99%

Is the World Becoming a Better or a Worse Place? A Data-Driven Analysis

Pileggi

2019

Sustainability

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Is the World becoming a better or a worse place to live? In this paper, we propose a tool that can help to answer the question by combining a number of global indicators belonging to multiple categories. The proliferation of statistical data about various aspects of the World performance may suggest that it should be "easy" to evaluate the overall success of human enterprise on this planet. Moreover, it also points out the intrinsic importance in the selection of indicators. However, people have different values, biases, and preferences about the importance of various indicators, making it almost impossible to find an objective answer to this question. To address the variety and the heterogeneity of available indicators and world views, we present the analysis of global World performance as a multi-criteria decision problem, making sure that the assessment method remains as transparent as possible. By dynamically selecting a set of indicators of interest, defining the weights that we attach to various indicators and specifying the desired trends associated with each indicator, we make the assessment adaptive to individual values. We also try to deal with the inherent bias that may exist in the set of indicators that are chosen. As a study case, from various data sets that are openly available online, we have selected several that are most relevant and easy to interpret in the context of the question in the title of the paper. We demonstrate how the choice of personal preferences, or weights, can strongly change the result. Our method also provides analysis of the weights space, showing how results for particular value sets compare to the average and extreme (optimistic and pessimistic) combinations of weights that may be chosen by users.

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Graph-based knowledge reuse for supporting knowledge-driven decision-making in new product development

Cited by 78 publications

References 29 publications

Digital twin-driven product design framework

Digital twin-driven product design framework

A Reusable and Executable Information Model of Experiments on Human Decision Making in Systems Engineering and Design

Is the World Becoming a Better or a Worse Place? A Data-Driven Analysis

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