Digital twin-driven intelligent production line for automotive MEMS pressure sensors

Zhang, Quanyong; Shen, Shengnan; Li, Hui; Cao, Wan; Wen, Tian; Jiang, Jin; Deng, Mohong; Zhang, Yunfan; Gu, Beikang; Wu, Kangkang; Zhang, Kun; Liu, Sheng

doi:10.1016/j.aei.2022.101779

Cited by 14 publications

(2 citation statements)

References 40 publications

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“…Planning and design, virtual commissioning [19][20][21][22][23][24] Optimization of the layout and balance [22,25] Virtual commissioning [6,[26][27][28] Reconfiguration of production lines Production scheduling and process control [29][30][31][32] Scheduling decisions [8,[33][34][35][36][37][38][39][40][41] Optimization of processing parameters [2,[42][43][44][45][46][47][48][49] Route planning and visualization [7,50] Reducing energy consumption and the scrap rate Prediction, maintenance, and fault diagnosis [31,51,52] Fault diagnosis [46,53] Optimized maintenance planning [54] Predicting production plans [48,[55][56][57] Predicting energy consumption or operational performance…”

Section: Ref Commentmentioning

confidence: 99%

“…Finally, the optimal processing parameters are implemented in the physical production line to control the products' quality, after simulation and verification by the twin model, as shown in Figure 5. Zhang [33] designed a new intelligent production line for automotive MEMS pressure sensors driven by a DT, and the real-time online monitoring and regulation of the products' quality was realized by establishing a database of the key processes. Liu [38] proposed a cloud-edge-based DT system (CEDTS) with a four-terminal-architecture.…”

Section: Visualization and Quality Control Of Production Linesmentioning

confidence: 99%

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Digital Twins for Discrete Manufacturing Lines: A Review

Feng,

Wan

2024

BDCC

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Along with the development of new-generation information technology, digital twins (DTs) have become the most promising enabling technology for smart manufacturing. This article presents a statistical analysis of the literature related to the applications of DTs for discrete manufacturing lines, researches their development status in the areas of the design and improvement of manufacturing lines, the scheduling and control of manufacturing line, and predicting faults in critical equipment. The deployment frameworks of DTs in different applications are summarized. In addition, this article discusses the three key technologies of high-fidelity modeling, real-time information interaction methods, and iterative optimization algorithms. The current issues, such as fine-grained sculpting of twin models, the adaptivity of the models, delay issues, and the development of efficient modeling tools are raised. This study provides a reference for the design, modification, and optimization of discrete manufacturing lines.

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