Zn–Ni electrophosphate coating is one of the most commonly used materials in industrial applications. The corrosion resistance of this coating is very important in order to achieve the minimum corrosion current of the Zn–Ni electrophosphate coating. This study described a new reliability simulation framework to determine the corrosion behavior of coating using a gene artificial neural network (ANN) to estimate the corrosion current of the coating. The input parameters of the model are temperature, pH of electroplating bath, current density, and Ni2+ concentration, and corrosion current defined as output. The effectiveness and accuracy of the model were checked by utilizing the absolute fraction of variance (R2 = 0.9999), mean absolute percentage error (MAPE = 0.0171), and root mean square error (RMSE= 0.0002). This is determined using the genetic algorithm (GA) and the optimum practice condition.
In order to improve product format quality and material flexibility, variety of application, and cost-effectiveness, SiC, ZrO2, and Al hybrid composites were manufactured in the research utilizing the powder metallurgy (PM) technique. A model was created to predict the tribological behavior of SiC–ZrO2–Al hybrid composites using statistical data analysis and gene expression programming (GEP) based on artificial intelligence. For the purpose of examining the impact of zirconia concentration, sliding distance, and applied stress on the wear behavior of hybrid composites, a comprehensive factor design of experiments was used. The developed GEP model was sufficiently robust to achieve extremely high accuracy in the prediction of the determine coefficient (R2), the root mean square error (RMSE), and the root relative square error (RRSE). The maximum state of the RMSE was 0.4357 for the GEP-1 (w1) model and the lowest state was 0.7591 for the GEP-4 (w1) model, while the maximum state of the RRSE was 0.4357 for the GEP-1 (w1) model and the minimum state was 0.3115 for the GEP-3 model (w1).
An important problem in manufacturing or product and process design is optimization of several responses simultaneously. Common approaches for multiple response optimization problems often begin with estimating the relationship between responses as outputs and control factors as inputs. Among these methods, response surface methodology (RSM), has attracted more attention in recent years, but in certain cases, relationship between responses and control factors are far too complex to be efficiently estimated by regression models and RSM method especially when we want optimize several responses simultaneously. An alternative approach proposed in this paper is to use artificial neural network (ANN) to estimate the response functions, Because of high mean square error (MSE) in neural network training step we use heuristic algorithms instead of Descent Gradient based algorithms. In the optimization phase a particle swarm optimization (PSO) and desirability function are considered to determine the optimal settings for the control factors. Two case study from literature are prepared to illustrate the strength of proposed approach in optimization of multiple response problems.
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