A four-terminal-architecture cloud-edge-based digital twin system for thermal error control of key machining equipment in production lines. (1st March 2022)

• Record Type: Journal Article
• Authors: Liu, Jialan; Ma, Chi; Gui, Hongquan; Wang, Shilong
• Is Part Of: Mechanical systems and signal processing. Volume 166(2022)
• Publication Date: 2022-03-01
• Subjects: Production line -- Digital twin -- Cloud computing -- Edge computing -- LSTM neural network; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108488
• Languages: English
• ISSNs: 0888-3270

Abstract

Graphical abstract: Highlights: Four-terminal-architecture CEDTS is proposed for thermal error control. Hyper-parameters of ILSTM network are optimized by ISOA. Comprehensive machining error model is constructed according to HCT theory. Data services, including clustering analysis, status monitoring, and data funsion, are provided. GPU-based cloud computing layer is used to expedite executing process. Abstract: Production lines are important for the high-accuracy and efficient machining of parts. The thermal error of key machining equipment in production lines has a significant effect on the geometric accuracy of machined parts. To improve the geometric accuracy of machined parts, the thermal error of key machining equipment in a production line should be controlled. Then the collection, storage, analysis, and calculation of the large-volume manufacturing data are essential. But the processing involving the large-volume manufacturing data is time-consuming and challenging, which leads to low executing efficiency. To solve the problem that the system is inefficient in the processing of the large-volume manufacturing data, a four-terminal-architecture cloud-edge-based digital twin system (CEDTS) is proposed with a reasonable functional division of four terminals, and thus the executing efficiency of CEDTS is expedited. Then the error mechanism is studied to prove the long-term memorizing behavior, and an improved seagull optimization algorithm (ISOA) is proposed based on the chaos thought to optimize the weights, thresholds, and the number of iterations of an improved long short term memory (ILSTM) network with the attention mechanism. The ISOA-ILSTM error model is embedded into the intelligent decision-making terminal of CEDTS to predict the thermal error. Moreover, a comprehensive machining error model is proposed and embedded into the intelligent decision-making terminal of CEDTS to control the thermal error. Finally, the effectiveness of CEDTS is verified on a production line. The results show that the reduction of the large-volume manufacturing data for the collection, storage, analysis, and calculation is significant. With the implementation of CEDTS, the fluctuation range of geometric errors of machined parts is reduced significantly. The executing time is reduced by more than half by CEDTS with the GPU acceleration.