To resolve the deformation problem for thin-walled aero-parts in the actual production, this paper simulates the effect of the tool-path on the machining accuracy of the thin-walled frame. The frame is shaped in the p...To resolve the deformation problem for thin-walled aero-parts in the actual production, this paper simulates the effect of the tool-path on the machining accuracy of the thin-walled frame. The frame is shaped in the part milling under a given clamping condition by using the three-dimensional finite element method （FEM）. Result shows that the sidewall deformation has a big difference if only the tool-path changes. When the tool-path from the outside to the inside is used, the machining deformation is smaller than another three kinds of toolpaths. Simulation results are compared with experimental data, and the correctness of the simulation is verified.Reasonable processing paths can be found by FEM.展开更多
文摘To resolve the deformation problem for thin-walled aero-parts in the actual production, this paper simulates the effect of the tool-path on the machining accuracy of the thin-walled frame. The frame is shaped in the part milling under a given clamping condition by using the three-dimensional finite element method （FEM）. Result shows that the sidewall deformation has a big difference if only the tool-path changes. When the tool-path from the outside to the inside is used, the machining deformation is smaller than another three kinds of toolpaths. Simulation results are compared with experimental data, and the correctness of the simulation is verified.Reasonable processing paths can be found by FEM.
基金supported in part by the Special Fund of Jiangsu Province for the Transformation of Scientific and Technological Achievements(Nos.BA2018110,BA2021036)the Natural Science Foundation of Jiangsu Province(No.BK20211061)。
文摘对于制造承包商来说,在正式接收订单之前,为了指导报价和预测交货日期,有必要对工时(Man-hours,MH)进行评估。装配工时作为工时的重要组成部分,具有重要的实际研究意义。针对多规格、小批量生产的特点,提出了一种基于支持向量机(Support vector machine,SVM)的装配工时估算模型。除了单部件属性、装配过程和历史工时数据外,还考虑了可量化装配复杂性的最短路径长度平均值(Average of shortest path length,ASPL)作为装配MH的影响因素,并提出了基于Creo JLink三维模型的这些因素的自动计算方法。通过对几种算法的比较,选择SVM作为装配体MH建模的最优算法。将遗传算法(Genetic algorithm,GA)应用于SVM中,有利于在SVM中搜索最优参数c和g时避免了局部求解,加快了收敛速度。最后,对所提出的GA-SVM模型进行训练,并应用于雷达装置仿生腿的装配工时预测。实验结果表明,GA-SVM具有比本文其他方法更高的预测精度,整个预测过程仅需3 min左右。
