Abstract
This study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in milling AA6061 alloy using carbide cutting tools coated with CVD‐TiCN in dry condition. An experimental model has been improved for estimating the surface roughness using artificial neural networks (ANN) and response surface methodology (RSM). For these models, cutting speed, depth of cut, and feed rate were evaluated as input parameters for experimental design. For the ANN modelling, the standard backpropagation algorithm was established to be the optimum selection for training the model. In the forming of the network construction, five different learning algorithms were used: the conjugate gradient backpropagation, Levenberg–Marquardt, scaled conjugate gradient, quasi‐Newton backpropagation, and resilient backpropagation. The best consequent with single hidden layers for the surface roughness was obtained by 3‐8‐1 network structures. The statistical analysis was performed with RSM‐based second‐order mathematics model. The influences of the cutting parameters on surface roughness were defined by using analysis of variance (ANOVA). The ANOVA results show that the depth of cut is the most effective parameter on surface roughness. Prediction models developed using ANN and RSM were compared in terms of prediction accuracy R2, MEP, and RMSE. The data estimated from ANN and RSM were realized to be very close to the data acquired from experimental studies. The value R2 of RSM model was higher than the values of the ANN model which demonstrated the stability and sturdiness of the RSM method.
Highlights
Academic Editor: Jinyang Xu is study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in milling AA6061 alloy using carbide cutting tools coated with CVD-TiCN in dry condition
In the forming of the network construction, five different learning algorithms were used: the conjugate gradient backpropagation, Levenberg–Marquardt, scaled conjugate gradient, quasi-Newton backpropagation, and resilient backpropagation. e best consequent with single hidden layers for the surface roughness was obtained by 3-8-1 network structures. e statistical analysis was performed with response surface methodology (RSM)-based second-order mathematics model. e influences of the cutting parameters on surface roughness were defined by using analysis of variance (ANOVA). e ANOVA results show that the depth of cut is the most effective parameter on surface roughness
J 1 where wbi is the weight of the biases between the layers, xj is the output of the jth processing element, wij is the weight of the connections between the ith and jth processing elements, n is the number of processing elements in the previous layer, i and j are the processing elements, and NETi is the weighted sum of the input to the ith processing element. e output of the neuron is defined by the activation function. e sigmoid function is usually used for the transfer function and generates a value between 0 and 1 for each value of the net input [22, 23]. e logistic transfer function of the artificial neural networks (ANN) model improved in this study is given as follows: Component Wt %
Summary
Artificial Intelligence-Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy. Karabulut [13], in the milling of AA7039 and Al2O3 reinforced composites, analyzed the effect of parameters on surface roughness and cutting force under finish machining conditions with the depth of cut limited to under 1.5 mm He developed analytical models for the estimation of surface roughness and cutting force using regression analysis and ANNs. e development of a multilayer perceptron and radial basis function neural network model for estimating surface roughness in the machining of 2024-T351 aluminium alloy was described by Fang et al e results showed that, compared with the radial basis function model, the multilayer perceptron model offered a significantly higher accuracy of estimation for machined surface roughness, especially for maximum roughness height [14]. 2. Materials and Methods is study aimed to define the optimal machining conditions by estimating the effect of the cutting parameters on the surface roughness in the milling of AA6061 aluminium alloy. E studied outputs are chosen in order to analyze and study the effect of the distinct cutting parameters on machinability and for estimation using the artificial neural network (ANN) and response surface methodology (RSM). Where p is the number of samples, t is the goal value, and o is the output value
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