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Yayınlar
- MakaleAçık Erişim2025Scopus SJRQ2Modeling and analysis of WEDM with a new deep neural network
Mahmat, Aybars; Dogan, Gurkan; Ergen, Burhan; Tosun, Nihat
In some cases, the models targeted in machining processes are difficult to apply to industrial production. Artificial intelligence techniques applied to industrial production methods in recent times eliminate these problems in machining. The use of deep learning methods in solving these problems has recently increased. In this study, a new deep neural network (DNN) based on artificial neural networks is proposed to model the machining of AISI4140 steel by wire electrical discharge machining (WEDM). The proposed DNN model predicts surface roughness (SR) and wire wear ratio (WWR), which are the most important inferences in cutting with WEDM. Obtaining inferences such as SR and WWR in a real experimental environment means a lot of time and cost. With the proposed model, there is a significant reduction in time and cost factors. The dataset used to train and test the proposed model was obtained from the real experimental environment. For training and testing the model, the data set is divided into two parts: 20% test data and 80% training data. In the experimental tests carried out with the proposed model, error rates such as Mean Absolute Error (MAE), R2, and Mean Squared Error (MSE) accuracy rate between SR and WWR values estimated according to the SR and WWR values obtained from the real environment were examined. MAE, MSE, and R2 for SR reached 0.0495, 0.0005, and 98.48%, respectively, while MAE, MSE, and R2 for WWR reached 0.0156, 0.0036, and 98.39%, respectively. These results of the proposed model provided a lower error rate and higher accuracy than the state-of-the-art models in the literature.
AtıfScopus5WoS5OpenAlex7
İncele - MakaleAçık Erişim2023Scopus SJRQ1A novel radial basis Bayesian regularization deep neural network for the Maxwell nanofluid applied on the Buongiorno model
Sabir, Zulqurnain; Akkurt, Nevzat; Ben Said, Salem
The aim of this work is to provide the numerical solutions of the fluid model by using the stochastic computing paradigms. The linear/exponential stretching sheets on magneto-rotating flow based on the Maxwell nanofluid have been provided using the Buongiorno model with the impacts of uneven heat source/sink, varying thermal conductivity and reactive species. The solutions of this transformed ordinary differential exponential stretching sheet model have been presented using a novel ‘radial basis’ (RB) activation function together with the Bayesian regularization deep neural network (BRDNN), i.e., RB-BRDNN. The deep neural network is presented into two hidden layers, while thirteen and twenty-five numbers of neurons have been used in the first and second layer. A reference dataset is proposed using the Runge-Kutta scheme for the model. The correctness of the stochastic RB-BRDNN procedure is examined through the comparison of proposed and database results, whereas minimal absolute error values provide the accuracy of the scheme. The reliability and competence of the computing RB-BRDNN solver is authenticated using the state transitions, correlation, regression, and error histograms.
AtıfScopus33WoS30OpenAlex36İncele