Casting Thermal Parameters Optimization Based on Cooling Curve Characteristics and Particle Swarm Algorithm
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.
NETL
NSTL
万方数据
