Research on Trajectory Optimization of Multilateral Thin Metal Rubber Automatic Laying Based on Virtual Fabrication Technology. Issue 2 (4th October 2021) - Shi, Linwei; Ren, Zhiying; Huang, Zhaohua; Shen, Liangliang; Lin, Youxi
Abstract : Herein, an in‐depth exploration of the trajectory optimization process of multilateral thin metal rubber (MT‐MR) in laying to build a virtual fabrication model that reflects the morphology and macro‐properties of material is done. According to the MT‐MR automatic blank laying process, which is based on the iteration mechanism of a local coordinate system in global space, and dynamically coupling with the Rodrigues rotation matrix, a parametric curve model under 3D random trajectory is constructed. Based on the evaluation of the background grid projection method, the optimization of the blank forming path of MT‐MR is discussed in depth using the improved multi‐objective genetic algorithm, and a MT‐MR complex network structure model based on the path optimization laying process is constructed. The structure and performance optimization of MT‐MR is further verified by combining virtual fabrication technology and experimental means. The results show that compared with MT‐MR with traditional experience‐based trajectory laying, the component with automatic trajectory laying optimization based on an improved genetic algorithm has a more uniform and compact wire turn structure. Therefore, an in‐depth discussion of the optimization of the MT‐MR automatic laying trajectory with a special configuration is presented and some theoretical guidance for the application of this material laying process is provided. Abstract : The spatial micropore finite‐element model of multilateral thin metal rubber (MT‐MR) with spatial multipoint random contact characteristics under the background of laying technology is constructed. Based on virtual manufacturing technology, the mapping relationship between MT‐MR microlaying trajectory and macromaterial properties is deeply explored. A path‐planning method based on genetic algorithm is proposed to optimize the performance of MT‐MR.