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

IF 5.9 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jingjing Li, Guanghui Zhou, Chao Zhang, Junsheng Hu, Fengtian Chang, Andrea Matta
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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.

Abstract Image

从 MBD 模型中提取加工特征,定义特征级数字孪生工艺模型,用于智能工艺规划
新兴技术的蓬勃发展及其与工艺规划的整合为解决传统试错工艺规划中的问题提供了新的机遇。本文将数字孪生与三维计算机视觉相结合,通过从基于模型的定义模型中提取加工特征,定义了一种新颖的特征级数字孪生工艺模型(FL-DTPM)。首先,通过融合现场数据、质量信息和工艺知识,定义了多维 FL-DTPM 框架,揭示了其虚拟和物理工艺的协同机制。然后,将三维计算机视觉加工特征提取方法嵌入到 FL-DTPM 框架中,以支持工艺知识的重用,其中涉及数据预处理、语义分割和实例分割等程序。最后,验证了所提出的特征提取方法的有效性,并介绍了 FL-DTPM 在机械加工过程中的应用。针对叶轮工艺规划,构建了一个 FL-DTPM 原型,以探索所提方法在智能工艺规划中的潜在应用场景,从而为 FL-DTPM 在航空航天制造企业的工业实施提供启示。
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来源期刊
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing 工程技术-工程:制造
CiteScore
19.30
自引率
9.60%
发文量
171
审稿时长
5.2 months
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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