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

作　　者：张琦 沈旭[1] 殷亚军[1] 李文 计效园[1] 周建新[1] Zhang Qi;Shen Xu;Yin Yajun;Li Wen;Ji Xiaoyuan;Zhou Jianxin(State Key Laboratory of Materials Processing and Die&Mould Technology,School of Materials Science and Engineering,Huazhong University of Science and Technology,Wuhan 430074)

机构地区：[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

出　　处：《特种铸造及有色合金》2024年第2期165-169,共5页Special Casting & Nonferrous Alloys

基　　金：国家重点研发计划资助项目(2020YFB1710100)。

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。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 inertial weight adjustment strategy based on the population search state and particle fitness ranking was proposed.Combined 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 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论] O242[自动化与计算机技术—计算机科学与技术] TG249[理学—计算数学]