دانشگاه سمنان

 آمار

تعداد نشریات21
تعداد شماره‌ها663
تعداد مقالات9,692
تعداد مشاهده مقاله69,022,200
تعداد دریافت فایل اصل مقاله48,496,290
Torabizadeh, Mohammad Amin, Fereidoon, Abdolhosein. (1405). Applying an Artificial Neural Network to Predict the Mechanical Properties of Epoxy Resin with Graphite Additive After Water Absorption. نشریه هنرهای کاربردی, 13(1), 245-257. 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, 13, 1, 1405, 245-257. doi: 10.22075/macs.2025.37083.1814
Torabizadeh, Mohammad Amin, Fereidoon, Abdolhosein. (1405). 'Applying an Artificial Neural Network to Predict the Mechanical Properties of Epoxy Resin with Graphite Additive After Water Absorption', نشریه هنرهای کاربردی, 13(1), pp. 245-257. 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, 1405; 13(1): 245-257. 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
مقاله 18، دوره 13، شماره 1 - شماره پیاپی 27، تیر 2026، صفحه 245-257    اصل مقاله (1020.31 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22075/macs.2025.37083.1814
نویسندگان
Mohammad Amin Torabizadeh* 1؛ Abdolhosein Fereidoon2
1Department of Industrial, University of Applied Science and Technology, Tehran, 91379-33435, Iran
2Faculty of Mechanical Engineering, Semnan University, Semnan, 35131-19111, Iran
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 types of water. A feedforward backpropagation algorithm was 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 were 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 materials under mechanical and moisture environments.
کلیدواژه‌ها
Artificial neural network؛ Graphite additive؛ Polymer composites؛ Water absorption؛ Mechanical properties
مراجع
  1. Zenzen, R., Khatir, S., Belaidi, I., Thanh, C.L., Abdel, M., 2020. A modified transmissibility indicator and Artificial Neural Network for damage identification and quantification in laminated composite structures. Composite Structures, 248, pp. 112497-112517.
  2. Tan, Z.X., Thambiratnam, D.P., Chan, T.H., Gordan, M., Razak, H.A., 2020. Damage detection in steel-concrete composite bridge using vibration characteristics and artificial neural network. Structure and Infrastructure Engineering, 16, pp. 1247-1261.
  3. Khatir, S., Tiachacht, S., Thanh, C.L., Ghandourah, E., Mirjalili, S., Abdel Wahab, M., 2021. An improved Artificial Neural Network using Arithmetic Optimization Algorithm for damage assessment in FGM composite plates. Composite Structures, 273, pp. 114302.
  4. Mardanshahi, V.N., Kazemirad, S., Shokrieh, M.M., 2020. Detection and classification of matrix cracking in laminated composites using guided wave propagation and artificial neural networks. Composite Structures 246, pp. 112403-112413.
  5. Marani, A., Nehdi, M.L., 2020. Machine learning prediction of compressive strength for phase change materials integrated cementitious composites. Construction and Building Materials, 265, 120286-120296.
  6. Sharma, A., Kumar, S.A., Kushvaha, V., 2020. Effect of aspect ratio on dynamic fracture toughness of particulate polymer composite using artificial neural network. Engineering. Fracture Mechanics, 228, pp. 106907-106918.
  7. Sharma, A., Kushvana, V., 2020. Predictive modelling of fracture behaviour in silica-filled polymer composite subjected to impact with varying loading rates using artificial neural network. Engineering Fracture Mechanics, 239, pp. 107328-107338.
  8. Wang, J., Lin C., Feng, G., Li, B., Wu, L., Cheng, J., 2022. Fracture prediction of CFRP laminates subjected to CW laser heating and pre-tensile loads based on ANN. AIP Advances, 12, pp. 120150-120158.
  9. Shabley, A., Nikolskaia, K., Varkentin, V., Peshkov, R., Petrova, L., 2023. Predicting the Destruction of Composite Materials Using Machine Learning Methods. Transportation Research Procedia, 68, pp. 191-197.
  10. Yin, B.B., Liew, K.M., 2021. Machine learning and materials informatics approaches for evaluating the interfacial properties of fiber-reinforced composites. Composite Structures, 273, 114328-114340.
  11. Natrayan L., Kumar, M.S., 2020. An integrated artificial neural network and Taguchi approach to optimize the squeeze cast process parameters of AA6061/Al2O3/SiC/Gr hybrid composites prepared by novel encapsulation feeding technique. Materials Today Communications, 25, 101586-101597.
  12. Nikzad, M.H., Heidari Rarani, M., Mirkhalaf, 2025. A novel Taguchi-based approach for optimizing neural network architectures: Application to elastic short fiber composites. Composites Science and Technology, 259, pp. 110951-110961.
  13. Al-Waily, M., Tolephi, M.H., Jweeg, M.J., 2020. Fatigue Characterization for Composite Materials used in Artificial Socket Prostheses with the Adding of Nanoparticles. Materials Science and Engineering, 928, pp. 22107-22123.
  14. Devadigaa, U., Kumar, R., Poojary, R., Fernandes, P., 2019. Artificial neural network technique to predict the properties of multiwall carbon nanotube-fly ashreinforced aluminium composite. Journal of Materials Research and Technology, 8, pp. 3970-3977.
  15. Kibrete, F., Trzepieci, T., Gebremedhen, H.S., Woldemichael, D.E., 2023. Artificial Intelligence in Predicting Mechanical Properties of Composite Materials. Journal of Composites Science, 364, pp. 364-394.
  16. Liu, X., Tian, S., Tao, F., Yu, W., 2021. A review of artificial neural networks in the constitutive modeling of composite materials. Composites Part B, 224, pp. 109152-109167.
  17. ASTM International, 2022. Standard Test Method for Water Absorption of Plastics, ASTM D570-22.
  18. ASTM International, 2013. Standard Test Method for Indentation Hardness of Rigid Plastics by Means of a Barcol Impressor, ASTM D2583-07.
  19. ASTM International, 2015. Standard Test Methods for Determining the Izod Pendulum Impact Resistance of Plastics, ASTM D256-10.
  20. ASTM International, 2016. Standard Test Method for Deflection Temperature of Plastics under Flexural Load in the Edgewise Position, ASTM D648-07.
  21. Torabizadeh, M.A., Maleki, S., 2024. Mechanical behavior of epoxy resin with graphite additive subjected to water absorption. Mechanical Testing, 66, pp. 856-866.
  22. Nikzad, M.H., Heidari Rarani, M.,  Momenzadeh-Kholenjani, A., Rasti, R., 2024. Implementation of specifically designed deep neural networks for the prediction and optimization of tensile properties of aluminum-copper alloy. Materialstoday Communication, 39, pp. 108964-108979.
  23. Terlapu, P., Salay, R.P., Pondreti, R., Tippana, C., 2022. Intelligent Identification of Liver Diseases Based on Incremental Hidden Layer Neurons ANN Model. International Journal of Computing and Digital Systems, 11, pp. 1027-1050.
  24. Mentges, N., Dashtbozorg, B., Mirkhalaf S.M., 2021. A micromechanics-based artificial neural networks model for elastic properties of short fiber composites. Composites Part B, 213, pp. 108736-108747.
آمار
تعداد مشاهده مقاله: 90
تعداد دریافت فایل اصل مقاله: 81


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب