基于冷却曲线特征与粒子群算法的铸造热物性参数寻优
Casting Thermal Parameters Optimization Based on Cooling Curve Characteristics and Particle Swarm Algorithm
张琦 1沈旭 1殷亚军 1李文 1计效园 1周建新1
作者信息
- 1. 华中科技大学材料科学与工程学院,材料成形与模具技术全国重点实验室,武汉 430074
- 折叠
摘要
针对铸造过程材料热物性参数求解方法适用参数种类少、人工经验依赖度高的问题,根据铸造冷却曲线特征与粒子群优化算法,建立了基于冷却曲线间差异的适应度函数.提出了基于种群搜索状态与粒子适应度排序的惯性权重调整策略,并结合冷却曲线特征适应度的粒子群速度迭代策略,建立了一种铸造过程材料热物性参数寻优方法,并以25G材料为例进行了实际验证.结果表明,试验方法可达到与人工反求法相同的精度,且所需计算次数少、人工经验依赖度低.
Abstract
In view of problems of few methods suitable for solving thermal physical parameters and high dependence on artificial experience during the casting process,a fitness function based on the difference between cooling curves was built up according to the characteristics of casting cooling curves and particle swarm optimization algorithm,and an iner-tial weight adjustment strategy based on the population search state and particle fitness ranking was proposed.Com-bined with the particle swarm velocity iteration strategy of the characteristic fitness of the cooling curve,a method for optimizing thermal physical parameters of materials during the casting process was established,which was verified by 25G steel.The reusults indicate that the proposed method can achieve the same accuracy as the artificial inverse method,which requires less calculation times and less dependence on artificial experience.
关键词
冷却曲线特征/粒子群算法/热物性参数Key words
Cooling Curve Characteristics/Particle Swarm Algorithm/Thermal Property Parameter引用本文复制引用
基金项目
国家重点研发计划资助项目(2020YFB1710100)
出版年
2024
NETL
NSTL
万方数据