Design of modified model of intelligent assembly digital twins based on optical fiber sensor network

Abstract

Intelligent assembly of large-scale, complex structures using an intelligent manufacturing platform represents the future development direction for industrial manufacturing. During large-scale structural assembly processes, several bottleneck problems occur in the existing auxiliary assembly technology. First, the traditional LiDAR-based assembly technology is often limited by the openness of the manufacturing environment, in which there are blind spots, and continuous online assembly adjustment thus cannot be realized. Second, for assembly of large structures, a single-station LiDAR system cannot achieve complete coverage, which means that a multi-station combination method must be used to acquire the complete three-dimensional data; many more data errors are caused by the transfer between stations than by the measurement accuracy of a single station, which means that the overall system's measurement and adjustment errors are increased greatly. Third, because of the large numbers of structural components contained in a large assembly, the accumulated errors may lead to assembly interference, but the LiDAR-assisted assembly process does not have a feedback perception capability, and thus assembly component loss can easily be caused when assembly interference occurs. Therefore, this paper proposes to combine an optical fiber sensor network with digital twin technology, which will allow the test data from the assembly entity state in the real world to be applied to the “twin” model in the virtual world and thus solve the problems with test openness and data transfer. The problem of station and perception feedback is also addressed and represents the main innovation of this work. The system uses an optical fiber sensor network as a flexible sensing medium to monitor the strain field distribution within a complex area in real time, and then completes real-time parameter adjustment of the virtual assembly based on the distributed data. Complex areas include areas that are laser-unreachable, areas with complex contact surfaces, and areas with large-scale bending deformations. An assembly condition monitoring system is designed based on the optical fiber sensor network, and an assembly condition monitoring algorithm based on multiple physical quantities is proposed. The feasibility of use of the optical fiber sensor network as the real-state parameter acquisition module for the digital twin intelligent assembly system is discussed. The offset of any position in the test area is calculated using the convolutional neural network of a residual module to provide the compensation parameters required for the virtual model of the assembly structure. In the model optimization parameter module, a correction data table is obtained through iterative learning of the algorithm to realize state prediction from the test data. The experiment simulates a large-scale structure assembly process, and performs virtual and real mapping for a variety of situations with different assembly errors to enable correction of the digital twin data stream for the assembly process through the optical fiber sensor network. In the plane strain field calibration experiment, the maximum error among the test values for this system is 0.032 ​mm, and the average error is 0.014 ​mm. The results show that use of visual calibration can correct the test error to within a very small range. This result is equally applicable to gradient curvature surfaces and freeform surfaces. Statistics show that the average measurement accuracy error for regular surfaces is better than 11.2%, and the average measurement accuracy error for irregular surfaces is better than 14.8%. During simulation of large-scale structure assembly experiments, the average position deviation accuracy is 0.043 ​mm, which is in line with the designed accuracy.

Keywords

Digital twins / Intelligent manufacturing / Intelligent assembly / Optical fiber sensor network / Assembly condition monitoring algorithm

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