论文部分内容阅读
基于修正的Archard磨损模型,对G3镍基合金热挤压成形工艺中挤压模具磨损行为进行了有限元分析。采用BP神经网络建立热挤压模具形状和磨损深度的映射关系。以模具表面磨损深度均匀分布为目标,结合遗传算法(genetic algorithm,GA),提出了一种集三者为一体的G3镍基合金热挤压模具型腔优化设计方法。计算结果表明,模具型腔经过优化后,最大磨损深度值降低约30%,磨损深度沿锥模表面分布更均匀,表明这种优化设计方法可以提高挤压模具的耐磨性能和使用寿命。
Based on the modified Archard wear model, the wear behavior of extrusion die in G3 nickel base alloy during hot extrusion process was analyzed by finite element method. Using BP neural network to establish the mapping relationship between hot extrusion die shape and wear depth. Based on the uniform distribution of the wear depth on the surface of the mold, a cavity-optimized design method of G3 nickel-base alloy hot extrusion mold was put forward based on the genetic algorithm (GA). The results show that the maximum depth of wear is reduced by about 30% after the cavity of the mold is optimized, and the wear depth is more evenly distributed along the surface of the cone mold. This shows that the optimized design method can improve the wear resistance and service life of the extrusion die.