Paper Info
Title
Dual-process modeling and control method for new product collaborative design based on petri net
Abstract
With the increase of the complexity of new product design (NPD) and in order to adapt the competitive global market, enterprises are not only facing ensure product quality, they also facing how to shorten the design cycle as much as possible. In this context, how to improve NPD efficiency and quality is one of common aims in rapidly changing global market. This paper presents a dual-process modeling and control method for collaborative design of new products based on petri net for this reason. Firstly, the design process is divided into several design stages from the coarse-grained perspective, and the object petri net is established to make the whole design process in a collaborative environment. Secondly, the design relations among the design majors are analyzed from the perspective of fine granularity. This process transforms the complex relationships, establishes the specialty collaborative design meta model and simulates it. The work progress is quantitative analysis and feedback information is controlled based on object petri net. A design method based on factory mode is proposed to control process of design specialty collaboration, while a fuzzy comprehensive evaluation and evaluation compensation is proposed to confirm feedback information for adapting to the dynamic design process. Finally, a case is used to illustrate the method, which shows the effectiveness of the method.
Year
DOI
Venue
2019
10.1007/s12652-018-0904-2
Journal of Ambient Intelligence and Humanized Computing
Keywords
Field
DocType
Petri nets,Collaborative design,Dual process,Modeling and control
Petri net,Industrial engineering,Computer science,Process modeling,Fuzzy logic,Engineering design process,Artificial intelligence,Granularity,Design process,Machine learning,Metamodeling,New product development
Journal
Volume
Issue
ISSN
10.0
SP3.0
1868-5145
Citations 
PageRank 
References 
0
0.34
25
Authors
2
Name
Order
Citations
PageRank
Liang Guo115115.26
Chao Zhang2939103.66