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Regression-BPNN modelling of surfactant concentration effects in electroless NiB coating and optimization using genetic algorithm
Surface & Coatings Technology ( IF 5.3 ) Pub Date : 2021-01-17 , DOI: 10.1016/j.surfcoat.2021.126878
M. Vijayanand , R. Varahamoorthi , P. Kumaradhas , S. Sivamani , Mithun V. Kulkarni

Critical micelle concentration (CMC) is an important factor to avoid the formation of micelles from monomeric surfactant molecules. Trisodium citrate stabilized electroless Nisingle bondB (ENi-B) coating on aluminium alloy (Al7075-T6) is attempted with the addition of amphoteric surfactant, 3-(N, N-Dimethylmyristylammonio) propanesulfonate (3-DMAPS), to enhance the surface finish (Ra) of the coatings. The main aim of the study is to investigate the influence of surfactant concentration on average surface roughness in ENi-B bath and determine the CMC of 3-DMAPS at minimum Ra. Mathematical models relating the concentration of amphoteric surfactant (0–0.162 g/L) as an independent variable and Ra as a dependent variable are developed using univariate regression analysis (Linear, quadratic, power, and exponential models) and back propagation neural network (BPNN) algorithm. The coefficient of determination (R2) is used to evaluate the goodness of fit between the models, and the BPNN model is found to be the best fit (R2 > 0.98). The minimum Ra of 0.171 ± 0.001 μm was achieved at the CMC of 0.049 g/L (0.135 mM) from the genetic algorithm (GA) using the validated models developed by quadratic regression analysis and BPNN as fitness functions. SEM, XRD and AFM techniques were carried out for the characterization of ENi-B coatings with and without surfactant at CMC.



中文翻译:

化学镍单键B涂层中表面活性剂浓度影响的回归BPNN建模和遗传算法优化

临界胶束浓度(CMC)是避免单体表面活性剂分子形成胶束的重要因素。单键尝试在铝合金(Al7075-T6)上添加柠檬酸三钠稳定的化学镀Ni B(ENi-B)涂层,并加入两性表面活性剂3-(NN - N-二甲基肉豆蔻酰铵)丙烷磺酸盐(3-DMAPS),以提高表面光洁度(R)的涂层。该研究的主要目的是研究表面活性剂浓度对ENi-B镀液中平均表面粗糙度的影响,并确定在最低R a下3-DMAPS的CMC 关于两性表面活性剂(0–0.162 g / L)作为自变量的浓度和R a的数学模型使用单变量回归分析(线性模型,二次模型,幂模型和指数模型)和反向传播神经网络(BPNN)算法开发作为因变量的变量。确定系数(R 2)用于评估模型之间的拟合优度,并且发现BPNN模型是最佳拟合(R 2  > 0.98)。遗传算法(GA)使用二次回归分析和BPNN作为适应度函数开发的经过验证的模型,在遗传算法(GA)中,CMC为0.049 g / L(0.135 mM)时,最小R a为0.171±0.001μm。进行了SEM,XRD和AFM技术对CMC上有或没有表面活性剂的ENi-B涂层进行表征。

更新日期:2021-02-05
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