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Sarabadan, S., Bagherian, A., Mirzababaie Mostofi, T. (2024). Predicting the Plastic Response of Circular Metal Plates under Uniform Dynamic Load Using Deep Neural Network. Electronic and Cyber Defense, 20(2), 105-121.
Saeed Sarabadan; Amirhossein Bagherian; Tohid Mirzababaie Mostofi. "Predicting the Plastic Response of Circular Metal Plates under Uniform Dynamic Load Using Deep Neural Network". Electronic and Cyber Defense, 20, 2, 2024, 105-121.
Sarabadan, S., Bagherian, A., Mirzababaie Mostofi, T. (2024). 'Predicting the Plastic Response of Circular Metal Plates under Uniform Dynamic Load Using Deep Neural Network', Electronic and Cyber Defense, 20(2), pp. 105-121.
Sarabadan, S., Bagherian, A., Mirzababaie Mostofi, T. Predicting the Plastic Response of Circular Metal Plates under Uniform Dynamic Load Using Deep Neural Network. Electronic and Cyber Defense, 2024; 20(2): 105-121.

Predicting the Plastic Response of Circular Metal Plates under Uniform Dynamic Load Using Deep Neural Network

مکانیک هوافضا
Article 8, Volume 20, Issue 2 - Serial Number 76, July 2024, Pages 105-121    PDF (1.13 M)
Document Type: Impact Mechanics
Authors
Saeed Sarabadan* 1; Amirhossein Bagherian2; Tohid Mirzababaie Mostofi3
1Corresponding author: Assistant Professor, Faculty of Science, Imam Hossein University, Tehran, Iran
2M.Sc Student, Faculty of Science, Imam Hossein University, Tehran, Iran
3Assistant Professor, Faculty of Mechanical Engineering, University of Eyvanekey, Eyvanekey, Iran
Receive Date: 08 February 2024,  Revise Date: 17 March 2024,  Accept Date: 20 April 2024 
Abstract
In the upcoming research, using deep neural networks, it is used to predict the maximum yield of circular metal sheets under uniform dynamic load. The neural network presented in this research was designed in the Python programming language and using the libraries available in it, including Tensorflow. The network is based on the regression problem and is of sequential type and includes 10 hidden layers which are the activation function in neurons of Leaky RELU type. The network optimizer algorithm was set to Adam and the objective function of the MSE problem and the number of network iterations was set to 700 times. The data set used in this article consists of 581 samples obtained from 16 series of experiments during the last forty years, which were standardized by the Scikit_learn library. The metal sheets are of 4 types: steel, aluminum, copper and titanium, and there is no separation between different metals.  The number of training data in the model was determined to be 443 equals to 75% of the data set. Also, the number of experimental and evaluator data was selected as 88 numbers equivalent to 15% and 50 numbers equivalent to 10% of the entire data set. Each sample has 8 features as neural network inputs and one label as output. The presented intelligent model among the 88 test data that was completely randomly selected from the data set was able to classify 76% of the data, approximately equivalent to 67 numbers, within the error range of less than 10% and 88% of the data, or in other words, equivalent to approximately 78 numbers within the error range. Predict less than 20%. The amount of the root mean square error index decreased 102 times compared to the analytical and traditional predictive relationships available in the research records. Also, the coefficient of determination criterion, which is an important indicator for evaluating the performance of neural networks based on regression problems, includes the value of 0.96.

Highlights
  • Using deep learning to predict the permanent deflection of metal sheets.
  • Using Adam's optimizer algorithm in deep neural network model architecture.
  • Determining optimal metaparameters based on evaluator search engines.
Keywords
Uniform dynamic loading; Circular metal sheets; Deep neural network; Artificial intelligence; Deflection; Metallic plates
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