针对控制断路器在制造过程中温度变化的问题,对各工艺参数进行了优化.首先利用均匀设计与正交设计,在对试验次数相近时,均匀性更好的、更具代表性的特点进行了试验分析;然后通过响应面法(RSM)构建了断路器温度变化的回归模型,并且利用残差进行了插值拟合,构建了基于径向基函数的响应面模型;最终提出了一种改进的非线性遗传算法(INGA),在遗传算法的选择操作时,首先采用精英策略,选取了最大适应值种群,然后在剩下的种群中采取轮盘赌法选择,通过改进的变异算子不断调整搜索区域,再利用非线性规划算法具有很强的局部搜索能力提高了解的精度.研究结果表明:改进后的非线性遗传算法具有较好的全局优化能力,且比传统的遗传算法和非线性规划算法更优.%Aiming at the control circuit breaker temperature change in the manufacturing process,the process parameters were designed opti-mally. First,based on the uniformity of the better,more representative of the characteristics in the case that the number of uniform design and orthogonal design trials was similar,the test analysis was conducted. Then the regression model of temperature variations of circuit break-er was constructed by response surface methodology (RSM),and the residuals was utilized for interpolant fitting,which in turn to construct the response surface model based on radial basis function. Finally,an improved nonlinear genetic algorithm (INGA) was proposed. In the selection of genetic algorithm,the elite strategy was firstly adopted to select the maximum fitness population, and then the roulette method was selected in the remaining population. The improved region was adjusted by the improved mutation operator,and using strong local search ability of the nonlinear programming algorithm,the accuracy of solution was upgraded. The results indicate that the improved nonlinear genet-ic algorithm has good global optimization capability,and it better than others.
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