基于遗传算法的涂层转接工艺优化

Coating transfer process optimization based on GA

为了优化涂层转接工艺与配方,提升转接涂层综合性能,在经验证的BP神经网络模型基础上进行优化设计,利用遗传算法以涂层的硬度、磨损量及剪切强度加权和最小为评价指标,以涂层的配方、真空熔结温度、保温时间为优化对象进行迭代优化。当硬度、磨损量及剪切强度的权重分别为-0. 3,0. 4,-0. 3,遗传算法最大迭代次数为100,种群规模为60,交叉与变异概率分别为0. 40和0. 01时,优化组合为:碳化钨(WC)添加量(质量分数) 26. 8%、真空熔结温度1 071℃、保温时间59. 7 min。采用优化配方、涂层转接工艺后制备的复合涂层硬度高达63. 6 HRC、磨损量仅为101 mg,剪切强度为157. 2 MPa。表明遗传算法对涂层配方、转接工艺进行优化的方式科学、有效,优化后的复合涂层综合性能得到较大的提升。

In order to optimize the transfer process and formulation of coating,and improve the comprehensive performance of the transferred coating,an optimized scheme was carried out based an a verified BP neural network model.The evaluation index was the minimum weighted sum of the coating hardness,wear extent and shear strength calculated by genetic algorithm(GA),and the iterative optimization was made on the formulation,vacuum fusion temperature and heat preservation time of the coating.When the weight of the hardness,the wear extent and the shear strength were-0.3,0.4 and-0.3,the maximum iterative time was 100,and the population was 60,the crossover and mutation probabilities would be 0.40 and 0.01,respectively.The optimization scheme includes keeping WC content at 26.8%,keeping the vacuum fusion temperature at 1071℃and keeping heat preservation time in 59.7 min.After the optimization,the hardness of the composite coating is 63.6 HRC,and the wear extent is 101 mg only,and the shear strength is 157.2 MPa.It shows that GA is scientific and effective in optimizing coating formulation and transfer process,and the comprehensive performance of the composite coating can be improved prominently.