TY - JOUR
T1 - Identification of key design characteristics for complex products adaptive design
AU - Han, Xin
AU - Li, Rong
AU - Wang, Jian
AU - Qin, Sheng-feng
AU - Ding, Guofu
PY - 2018/3/1
Y1 - 2018/3/1
N2 - 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 productwill 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 focusedon 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.
AB - 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 productwill 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 focusedon 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.
KW - Identification of key design characteristics
KW - Gray relational analysis
KW - Systematic approach
KW - Complex products
KW - Adaptive design
KW - network analysis
U2 - 10.1007/s00170-017-1267-0
DO - 10.1007/s00170-017-1267-0
M3 - Article
SN - 0268-3768
VL - 95
SP - 1215
EP - 1231
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
IS - 1-4
ER -