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Torabizadeh, Mohammad Amin, Fereidoon, Abdolhosein. (1404). Applying an Artificial Neural Network to Predict the Mechanical Properties of Epoxy Resin with Graphite Additive After Water Absorption. نشریه هنرهای کاربردی, (), -. doi: 10.22075/macs.2025.37083.1814
Mohammad Amin Torabizadeh; Abdolhosein Fereidoon. "Applying an Artificial Neural Network to Predict the Mechanical Properties of Epoxy Resin with Graphite Additive After Water Absorption". نشریه هنرهای کاربردی, , , 1404, -. doi: 10.22075/macs.2025.37083.1814
Torabizadeh, Mohammad Amin, Fereidoon, Abdolhosein. (1404). 'Applying an Artificial Neural Network to Predict the Mechanical Properties of Epoxy Resin with Graphite Additive After Water Absorption', نشریه هنرهای کاربردی, (), pp. -. doi: 10.22075/macs.2025.37083.1814
Torabizadeh, Mohammad Amin, Fereidoon, Abdolhosein. Applying an Artificial Neural Network to Predict the Mechanical Properties of Epoxy Resin with Graphite Additive After Water Absorption. نشریه هنرهای کاربردی, 1404; (): -. doi: 10.22075/macs.2025.37083.1814

Applying an Artificial Neural Network to Predict the Mechanical Properties of Epoxy Resin with Graphite Additive After Water Absorption

Mechanics of Advanced Composite Structures
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 26 تیر 1404    اصل مقاله (930.29 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22075/macs.2025.37083.1814
نویسندگان
Mohammad Amin Torabizadeh* 1؛ Abdolhosein Fereidoon2
1University of Applied Science and Technology
2Faculty of Mechanical Engineering, Semnan University, Semnan
تاریخ دریافت: 14 فروردین 1404،  تاریخ بازنگری: 10 تیر 1404،  تاریخ پذیرش: 26 تیر 1404 
چکیده
The present study utilized an artificial neural network (ANN) model to anticipate Barcol hardness, impact strength and heat deflection temperature data for epoxy resin specimens with varying weight percentages of graphite additive exposed in different sorts of water. A feedforward backpropagation algorithm used for predictive modeling with two input parameters: the weight percentage of the graphite additive (0, 5, 10, 15, and 25 wt.%) and the type of water used (dry specimen, potable water, distilled water, alkaline solution, and acidic solution). Experimental test data for mechanical properties used to train the ANN model. The network was validated by comparing the predicted outputs with experimental data and by evaluating performance metrics. The results conclude that the ANN model is a practical and accurate approach for rapidly predicting mechanical performance and can be considered a substitute for traditional procedures used to characterize composite materials through experimental methods. Among the two input parameters, the weight percentage of the graphite additive was the most essential input parameter used to predict the mechanical properties of composites. Besides, the key findings of this work can also be a reference for the engineering practice of composite material under mechanical and moisture environments.
کلیدواژه‌ها
Artificial neural network؛ Graphite additive؛ Polymer composites؛ Water absorption؛ Mechanical properties
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