ABSTRACT

In this work, a novel citrate stabilised electroless bath was developed and the process parameters (concentrations of nickel, reducing agent, and stabiliser) were optimised to achieve the maximum hardness in the ENi-B deposit on 7075-T6 aluminium alloy, using the back-propagation neural network (BPNN), Box–Behnken design (BBD), simulated annealing (SA) and genetic algorithm (GA). The effect of independent variables on dependent variable was modelled using the BPNN and BBD. The models were assessed for their significance using the coefficient of determination (R²) and mean squared error (MSE). The MSE and R² of 34.18 and 0.9852 were obtained for BPNN model against 20.48 and 0.9911 for BBD, which proved that the BBD fits well to the experimental data. The optimum nickel ion, reducing agent and stabiliser concentrations of 29.86, 0.77 and 30.92 g L⁻¹ were obtained from BBD for the maximum hardness of 592 HV. The local optimum values obtained from BBD were compared with global optimisation techniques, SA and GA, and the values were validated through experiments carried out in triplicate. The maximum hardness from local and global optimisation techniques was identical, with negligible change in the values of optimised process parameters. X-ray diffraction and scanning electron microscopy methods were used to examine the elemental composition and surface morphology, respectively, before and after heat treatment.

摘要

在这项工作中，开发了一种新型柠檬酸盐稳定的化学镀浴，并优化了工艺参数（镍、还原剂和稳定剂的浓度），以在 7075-T6 铝合金上的 ENi-B 镀层中获得最大硬度，使用背面-传播神经网络 (BPNN)、Box-Behnken 设计 (BBD)、模拟退火 (SA) 和遗传算法 (GA)。自变量对因变量的影响使用 BPNN 和 BBD 建模。使用决定系数 (R ² ) 和均方误差 (MSE)评估模型的显着性。MSE 和 R ²BPNN 模型获得了 34.18 和 0.9852，而 BBD 模型获得了 20.48 和 0.9911，这证明 BBD 与实验数据非常吻合。从 BBD 获得的最佳镍离子、还原剂和稳定剂浓度分别为 29.86、0.77 和 30.92 g L ^-1，最大硬度为 592 HV。将从 BBD 获得的局部最优值与全局优化技术 SA 和 GA 进行比较，并通过一式三份进行的实验验证这些值。局部和全局优化技术的最大硬度是相同的，优化过程参数值的变化可以忽略不计。X射线衍射和扫描电子显微镜方法分别用于检查热处理前后的元素组成和表面形貌。