Dynamic design method of digital twin process model driven by knowledge-evolution machining features. Issue 7 (3rd April 2022)

• Record Type: Journal Article
• Authors: Liu, Jinfeng; Zhao, Peng; Jing, Xuwen; Cao, Xuwu; Sheng, Sushan; Zhou, Honggen; Liu, Xiaojun; Feng, Feng
• Is Part Of: International journal of production research. Volume 60:Issue 7(2022)
• Page Start: 2312
• Page End: 2330
• Publication Date: 2022-04-03
• Subjects: Digital twin -- process knowledge -- machining features -- dynamic evolution; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2021.1887531
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : Machining plan is the core of guiding manufacturing production and is regarded as one of the keys to ensure the quality of product processing. Existing process design methods are inefficient to quickly handle the machining plan changed induced by the unpredictable events in real-time production. It inevitably causes time and economic losses for the enterprise. In order to express the evolutionary characteristics of product processing, the construction method of digital twin process model (DTPM) is proposed based on the knowledge-evolution machining features. Three key technologies include correlation structure of process knowledge, expression method of the evolution geometric features and the association mechanism between two are solved. On this basis, the construction framework of DTPM is illustrated. Then, the organisation and management mechanism of multi-source heterogeneous data is discussed in detail. At last, a case study of the complex machined part is researched, the results show that the processing time reduced by about 7% and the processing stability improved by 40%. Meanwhile, the implementation scheme, application process and effect of this case are described in detail to provide reference for enterprises.