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

Peng, Gongzhuang; Mao, Huachao; Wang, Hongwei; Zhang, Heming

doi:10.1007/s11518-016-5306-4

Cited by 17 publications

(9 citation statements)

References 11 publications

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“…The match between knowledge units based semantic similarity takes both elements match and structure match into consideration. In this paper, two types of similarity are considered, namely semantic similarity and structure similarity [49], [50]. Mark = { 1 , ⋯ , 12 , ⋯ } as the requirement tree and = { ′ 1 , ⋯ ′ , ′ 12 , ⋯ ′ } as the description tree in a knowledge database.…”

Section: A Case Matching Algorithm Based On Semantic Similaritymentioning

confidence: 99%

A collaborative system for capturing and reusing in-context design knowledge with an integrated representation model

Peng

Wang

Zhang

et al. 2017

Advanced Engineering Informatics

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Section: A Case Matching Algorithm Based On Semantic Similaritymentioning

confidence: 99%

A collaborative system for capturing and reusing in-context design knowledge with an integrated representation model

Peng

Wang

Zhang

et al. 2017

Advanced Engineering Informatics

Self Cite

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“…Firstly, since KCs are key product features that reflect typical properties of a product, they are usually utilized to find the most useful cases to help solve a target problem by calculating the similarity of KCs in available cases. Peng et al [2], Kocsis et al [3], and Zhu et al [4] used KCs as an enabler to select a similar case during an engineering design phase. While Romli et al [5] found the optimal from all possible solutions based on KCs to support sustainable product design.…”

Section: The Application Of Kcsmentioning

confidence: 99%

“…Some enterprises and scholars used a method called key characteristic (KC) control to alleviate the above problems, such as using KCs to capture the most similar instances [2][3][4][5], or using KCs as an efficient medium for communication with suppliers [6,7]. However, at present, there is no mutual definition for a KC.…”

Section: Introductionmentioning

confidence: 99%

“…At present, there are some researches on the application of KCs [2][3][4][5][6][7][10][11][12][13][14][15], but few on the systematic identification. In addition, most of these studies focus on the KPCs, less on KDCs.…”

Section: Introductionmentioning

confidence: 99%

“…Our contributions have twofold. Firstly, in theory, we propose a framework with two related phases to (1) construct product design specification (PDS)-KDC Candidates Network (PKCN) and (2) identify KDCs for application. Secondly, in application, we develop a set of KDC identification techniques to support developments at each phase.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of key design characteristics for complex product adaptive design

Han

Wang

et al. 2017

Int J Adv Manuf Technol

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Key design characteristics (KDCs) are important information related to the product and part designs, which significantly influence on the product's functions, performances, and quality. Identifying KDCs for a complex product will help designers to focus on key design parameters during the design process and rapidly obtain design schemes based on their close relationships to the product's functions, performances, and quality. Although there are some researches on key characteristic (KC) identification, most of them are focused on key process characteristics (KPCs) and few on KDCs. There also lacks a KDC identification framework to support KDC identification with better completeness and diverse usages. Adaptive design is the most important pattern of complex product design. Therefore, this paper presents a systematic method to identify KDCs for complex product adaptive design, in which KDCs can be determined by two related phases. Firstly, a product design specification (PDS)-KDC Candidates Network (PKCN) is constructed by using existing product instance data, cluster analysis, KC flow-down, and network analysis approaches. Then, the result from the first phase is used as a basis to identify KDCs for adaptive design. Three KDC identification techniques: similarity reasoning technique, breadth-first search (BFS), and the gray relational analysis approach are applied to find out KDCs from the PKCN, which are the most sensitive to the variation of a PDS. These identified KDCs can help designers to understand the relationships between KDCs and PDS and rapidly develop a design scheme. The effectiveness and the feasibility of the proposed method are verified by a case study via the development of an electric multiple unit (EMU)'s bogie.

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Integrated Simulation Modeling Method for Complex Products Collaborative Design Using Engineering-APP

Ming

Wang

et al. 2018

Lecture Notes in Computer Science

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

Cited by 17 publications

References 11 publications

A collaborative system for capturing and reusing in-context design knowledge with an integrated representation model

A collaborative system for capturing and reusing in-context design knowledge with an integrated representation model

Identification of key design characteristics for complex product adaptive design

Integrated Simulation Modeling Method for Complex Products Collaborative Design Using Engineering-APP

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