scholarly journals BOM-based design knowledge representation and reasoning for collaborative product development

2016 ◽  
Vol 25 (2) ◽  
pp. 159-176 ◽  
Author(s):  
Gongzhuang Peng ◽  
Huachao Mao ◽  
Hongwei Wang ◽  
Heming Zhang
2013 ◽  
Vol 291-294 ◽  
pp. 2557-2561
Author(s):  
Tao Sun ◽  
Hai Bo Liu

The transformer fault diagnosis expert system design knowledge representation and reasoning mechanisms are the key issue. Characteristics of transformer fault diagnosis system based on human experts, learning on the basis of the human expert diagnosis of transformer faults, to build a transformer fault diagnosis expert system of systems architecture, knowledge representation and reasoning mechanisms for a more detailed analysis and discussion.


Author(s):  
Jung-Do Noh ◽  
Hyo-Won Suh

Traditionally, product development process has required knowledge management techniques to capture information and knowledge about design. In the meantime, the necessity for sharing and exchanging not only product data but also semantics of product data has been arisen because of the use of various software tools and product data models in distributed product development environment. The main focus of this research has been on exploiting implicit engineers’ design knowledge by explicitly expressing and sharing the knowledge through terms representing semantics of product data. In particular, it considers that distributed product design data can be semantically integrated by using ontology on which implicit design knowledge can be captured in the form of IF-THEN rule. Thus, in this paper, we use the Web Ontology Language (OWL), which is a Description Logic based ontology language, to represent product data and the Semantic Web Rule Language (SWRL), which is a rule based ontology language, to express design knowledge for car air purifiers in Prote´ge´. Then, this paper shows how OWL product data model and SWRL design knowledge can support design decision making of car air purifiers by their reasoning. In addition, it also demonstrates how SWRL can complement OWL to build product data model as well.


1999 ◽  
Vol 12 (2) ◽  
pp. 221-228 ◽  
Author(s):  
Sanja Vranes̆ ◽  
Mladen Stanojević

2021 ◽  
Vol 1 ◽  
pp. 1303-1312
Author(s):  
Ricardo Real ◽  
Chris Snider ◽  
Mark Goudswaard ◽  
Ben Hicks

AbstractWhilst prior works have characterised the affordances of prototyping methods in terms of generating knowledge about a product or process, the types, or ‘dimensions’ of knowledge towards which they contribute are not fully understood. In this paper we adapt the concept of ‘design domains’ as a method to interpret, and better understand the contributions of different prototyping methods to design knowledge in new product development. We first synthesise a set of ten dimensions for design knowledge from a review of literature in design-related fields. A study was then conducted in which participants from engineering backgrounds completed a Likert-type questionnaire to quantify the perceived contributions to design knowledge of 90 common prototyping methods against each dimension. We statistically analyse results to identify patterns in the knowledge contribution of different methods. Results reveal that methods exhibit significantly different contribution profiles, suggesting different methods to be suited to different knowledge. Thus, this paper indicates potential for new methods, methodology and processes to leverage such characterisations for better selection and sequencing of methods in the prototyping process.


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