Digital Twinning and Optimization of Manufacturing Process Flows

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
Hankang Lee, Hui Yang
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引用次数: 1

Abstract

The new wave of Industry 4.0 is transforming manufacturing factories into data-rich environments. This provides an unprecedented opportunity to feed large amounts of sensing data collected from the physical factory into the construction of digital twin (DT) in cyberspace. However, little has been done to fully utilize the DT technology to improve the smartness and autonomous levels of small and medium-sized manufacturing factories. Indeed, only a small fraction of small and medium-sized manufacturers (SMMs) has considered implementing DT technology. There is an urgent need to exploit the full potential of data analytics and simulation-enabled DTs for advanced manufacturing. Hence, this paper presents the design and development of DT models for simulation optimization of manufacturing process flows. First, we develop a multi-agent simulation model that describes nonlinear and stochastic dynamics among a network of interactive manufacturing things, including customers, machines, automated guided vehicles (AGVs), queues, and jobs. Second, we propose a statistical metamodeling approach to design sequential computer experiments to optimize the utilization of AGV under uncertainty. Third, we construct two new graph models - job flow graph and AGV traveling graph - to track and monitor the real-time performance of manufacturing jobshops. The proposed simulation-enabled DT approach is evaluated and validated with experimental studies for the representation of a real-world manufacturing factory. Experimental results show that the proposed methodology effectively transforms a manufacturing jobshop into a new generation of DT-enabled smart factories.
制造工艺流程的数字化结对与优化
工业4.0的新浪潮正在将制造工厂转变为数据丰富的环境。这提供了一个前所未有的机会,将从实体工厂收集的大量传感数据输入到网络空间的数字孪生(DT)建设中。然而,在充分利用DT技术来提高中小型制造工厂的智能和自主水平方面,人们做得很少。事实上,只有一小部分中小型制造商(smm)考虑实施DT技术。目前迫切需要利用数据分析和模拟技术在先进制造领域的全部潜力。因此,本文提出了用于制造工艺流程仿真优化的DT模型的设计和开发。首先,我们开发了一个多智能体仿真模型,该模型描述了交互制造事物网络之间的非线性和随机动力学,包括客户、机器、自动导引车(agv)、队列和作业。其次,我们提出了一种统计元建模方法来设计顺序计算机实验,以优化不确定条件下AGV的利用率。第三,构建了两个新的图形模型——作业流图和AGV行进图,用于跟踪和监控制造车间的实时性能。提出的仿真支持的DT方法进行了评估和验证,并通过实验研究来表示现实世界的制造工厂。实验结果表明,所提出的方法有效地将制造车间转变为支持dt的新一代智能工厂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.80
自引率
20.00%
发文量
126
审稿时长
12 months
期刊介绍: Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining
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