Defining a feature-level digital twin process model by extracting machining features from MBD models for intelligent process planning

Jingjing Li (), Guanghui Zhou (), Chao Zhang (), Junsheng Hu (), Fengtian Chang () and Andrea Matta ()
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Jingjing Li: Xi’an Jiaotong University
Guanghui Zhou: Xi’an Jiaotong University
Chao Zhang: Xi’an Jiaotong University
Junsheng Hu: Xi’an Jiaotong University
Fengtian Chang: Xi’an Jiaotong University
Andrea Matta: Politecnico di Milano

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 5, No 15, 3227-3248

Abstract: Abstract The booming development of emerging technologies and their integration in process planning provide new opportunities for solving the problems in traditional trial-and-error process planning. Combining digital twin with 3D computer vision, this paper defines a novel feature-level digital twin process model (FL-DTPM) by extracting machining features from model-based definition models. Firstly, a multi-dimensional FL-DTPM framework is defined by fusing on-site data, quality information, and process knowledge, where the synergistic mechanism of its virtual and physical processes is revealed. Then, 3D computer vision-enabled machining features extraction method is embedded into the FL-DTPM framework to support the reuse of process knowledge, which involves the procedures of data pre-processing, semantic segmentation, and instance segmentation. Finally, the effectiveness of the proposed features extraction method is verified and the application of FL-DTPM in machining process is presented. Oriented to the impeller process planning, a prototype of FL-DTPM is constructed to explore the potential application scenarios of the proposed method in intelligent process planning, which could provide insights into the industrial implementation of FL-DTPM for aerospace manufacturing enterprises.

Keywords: Digital twin; Digital twin process model; 3D computer vision; Machining features; Semantic segmentation; Instance segmentation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02406-2

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