基于TOPSIS和改进NSGA-Ⅱ法的搅拌摩擦工艺制备B 4 C/A356复合材料显微组织和力学性能的混合多目标优化(英
发布时间:2023-03-05 14:55
采用搅拌摩擦工艺以A356合金为基体金属制备B4C/A356复合材料。利用人工神经网络(ANN)和非支配排序遗传算法-Ⅱ研究复合材料的显微组织和力学性能。首先,研究不同加工条件下制得的复合材料的显微组织。结果表明,搅拌摩擦工艺参数如搅拌头的旋转速度、横向移动速度和形状显著影响基体中初始Si颗粒的尺寸、复合材料层中B4C增强剂的分散效果及体积分数。采用高旋转/移动速度比和螺纹销形状搅拌头能获得较好的颗粒分布、较细的Si颗粒和较少的B4C团聚体。其次,通过硬度和拉伸试验研究复合材料的力学性能。结果显示,经搅拌摩擦工艺处理后样品的断裂机理由脆性断裂转变为延性断裂。最后,利用人工神经网络技术建立了搅拌摩擦工艺参数与复合材料显微组织和力学性能的关系。采用结合多样性保护机制的NSGA-Ⅱ法,即ε消除算法得到搅拌摩擦工艺参数的Pareto最优解集。
【文章页数】:17 页
【文章目录】:
1 Introduction
2 Experimental
2.1 Materials
2.2 Friction stir processing
2.3 Microstructure and hardness
2.4 Tensile measurements
2.5 Force acquisition
2.6 Composites preparation
3 Results and discussion
3.1 Microstructural properties
3.2 Axial force during FSP
3.3 Microhardness
3.4 Effects of process parameters on UTS
3.5 Establishment of models using ANN
3.6 Multi-objective optimization
3.6.1 Pareto optimization of rotational and traverse speeds
3.6.2 Best trade-off solutions using TOPSIS
4 Conclusions
本文编号:3756403
【文章页数】:17 页
【文章目录】:
1 Introduction
2 Experimental
2.1 Materials
2.2 Friction stir processing
2.3 Microstructure and hardness
2.4 Tensile measurements
2.5 Force acquisition
2.6 Composites preparation
3 Results and discussion
3.1 Microstructural properties
3.2 Axial force during FSP
3.3 Microhardness
3.4 Effects of process parameters on UTS
3.5 Establishment of models using ANN
3.6 Multi-objective optimization
3.6.1 Pareto optimization of rotational and traverse speeds
3.6.2 Best trade-off solutions using TOPSIS
4 Conclusions
本文编号:3756403
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