scholarly journals Surface Roughness Optimization of Polyamide-6/Nanoclay Nanocomposites Using Artificial Neural Network: Genetic Algorithm Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Mehdi Moghri ◽  
Milos Madic ◽  
Mostafa Omidi ◽  
Masoud Farahnakian
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Nonlinear Function ◽  
Polyamide 6 ◽  
Machining Performance ◽  
Milling Parameters ◽  
Artificial Neural

During the past decade, polymer nanocomposites attracted considerable investment in research and development worldwide. One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. The objective of this paper is to develop a predictive model using a combined design of experiments and artificial intelligence approach for optimization of surface roughness in milling of polyamide-6 (PA-6) nanocomposites. A surface roughness predictive model was developed in terms of milling parameters (spindle speed and feed rate) and nanoclay (NC) content using artificial neural network (ANN). As the present study deals with relatively small number of data obtained from full factorial design, application of genetic algorithm (GA) for ANN training is thought to be an appropriate approach for the purpose of developing accurate and robust ANN model. In the optimization phase, a GA is considered in conjunction with the explicit nonlinear function derived from the ANN to determine the optimal milling parameters for minimization of surface roughness for each PA-6 nanocomposite.

Artificial Intelligence Model of Surface Roughness for End Milling Operation of Steel and its Verification by Genetic Algorithm

2011 ◽  
Vol 110-116 ◽  
pp. 3459-3464
Author(s):  
Mohammed Anayet Ullah Patwari ◽  
A.K.M. Nurul Amin
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
End Milling ◽  
Depth Of Cut ◽  
Efficient Production ◽  
Statistical Experimental Design ◽  
Artificial Neural ◽  
Coated Carbide

Surface roughness is important for evaluating the machined surface quality. In this work, an Artificial Neural Network (ANN) surface roughness prediction model was developed by coupling it with Response Surface Methodology (RSM). For this interpretation, advantages of statistical experimental design techniques, experimental measurements, and artificial neural network were exploited in an integrated manner. Cutting experiments were designed based on small centre composite design technique to develop a RSM model. The input cutting parameters were: cutting speed, feed, and axial depth of cut, and the output parameter was surface roughness. The predictive model was created using a feed-forward back-propagation neural network exploiting the experimental data. The network was trained with pairs of inputs/outputs datasets generated by end milling medium carbon steel with TiN coated carbide inserts. The model can be used for the analysis and prediction of the complex relationships between cutting conditions and surface roughness, in metal-cutting operations, with the ultimate goal of efficient production. The ANN model was verified with the optimized parameters predicted by a coupled genetic algorithm (GA) and RSM technique also developed by the authors.

Prediction of Surface Roughness of Mild Steel Alloy in CNC Milling Process Using ANN and GA Technique

2020 ◽  
Vol 38 (12A) ◽  
pp. 1842-1851
Author(s):  
Hind H. Abdulridha
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Milling Process ◽  
Spindle Speed ◽  
Steel Alloy ◽  
Milling Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Tool Diameter

In this paper, Analysis Of Variance (ANOVA), Artificial Neural Network (ANN), and Genetic Algorithm (GA) have been studied to predict the effect of milling parameters on the Surface Roughness (Ra) during machining of mild steel alloy. The milling experiments carried out based on the Taguchi design of experiments method using (L16) orthogonal array with 3 factors and 4 levels. The influence of three independent variables such as spindle speed (910, 930, 960, and 1000 rpm), feed rate (93, 95, 98, and 102 mm/min), and Tool Diameter (8, 10, 12, and 14 mm) on the Surface Roughness (Ra) were tested and analyzed with (ANOVA) to predict the response which indicates that spindle speed was the most significant factor effecting on Surface Roughness (Ra). Artificial Neural Network (ANN) and numerical methods are used widely for modeling and predict the performance of manufacturing technologies. Neural Network technique with 2 hidden layers, 10 neurons size, 1000 epochs, and Trainlm transfer function is used to predict the result. The Genetic Algorithm (GA) has been utilized to find optimal cutting conditions during a milling process. From the results, the optimal value of spindle speed is (930 rpm), feed-rate is (95 mm/min) and tool diameter is (8 mm). This network structure is capable of predicting the Surface Roughness (Ra) well to optimize the milling parameters. Artificial Neural Network (ANN) predicted results indicate good agreement between the experimental and the predicted values.

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