摘要
钛合金切削参数的合理选择,对于保证工件的加工质量,降低生产成本,提高生产效率具有重要作用。为此提出了一种以机床、刀具、工件等参量及所建立的切削力、刀具磨损和表面粗糙度非线性数学模型为约束条件,以最大生产率为目标,运用改进的自适应遗传算法(IAGA),实现对切削用量优化的方法。与传统的遗传算法相比,IAGA可根据种群的适应度和进化代数自动调整交叉概率和变异概率,加快种群的进化速度。并通过实验验证了该优化方法的可行性和有效性。
To guarantee the processing quality, reduce processing costs, improve productivity, titanium alloys＇the optimization of cutting parameters is of great importance. So a method for optimization of mill- ing parameters with an Improved Adaptive Genetic Algorithm（IAGA） is put forward, in which the opti- mization goals was based on maximum productivity, the constraints was based on machine tool, cutting tools, workpiece and the nonlinear mathematic model of cutting force, tool wear, surface roughness. Compared with conventional genetic algorithm, IAGA can adjust the crossover probability and mutation probability automatically based on the fitness of the population and evolution algebra. The experiment re- sults show that the proposed way is feasible and effective.
出处
《组合机床与自动化加工技术》
北大核心
2013年第11期44-46,57,共4页
Modular Machine Tool & Automatic Manufacturing Technique
基金
中航产学研创新基金项目(CXY2010SH29)
关键词
钛合金
数学模型
遗传算法
参数优化
titanium alloy
genetic algorithm
mathematical model
parameter optimization
