Effect of bending load on electrical conductivity of carbon/epoxy composites filled with nanoparticles using design of experiment and artificial neural networks
In this study, we investigate the impact of bending load on the electrical conductivity of carbon-epoxy composites containing various nanoparticles. The samples must meet the bending strength requirements and comply with the electrical conductivity standards set by the U.S. Energy Institute for elec...
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025002592 |
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| author | Ali Sadollah Seyed Morteza Razavi Abobakr Khalil Al-Shamiri |
| author_facet | Ali Sadollah Seyed Morteza Razavi Abobakr Khalil Al-Shamiri |
| author_sort | Ali Sadollah |
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| description | In this study, we investigate the impact of bending load on the electrical conductivity of carbon-epoxy composites containing various nanoparticles. The samples must meet the bending strength requirements and comply with the electrical conductivity standards set by the U.S. Energy Institute for electrode manufacturing. We use carbon black (CB) nanoparticles, carbon nanotubes (CNTs), and epoxy resin to create these samples. Using the four-point resistance method, we determine the optimal weight percentages of CB and CNTs incorporated into the carbon/epoxy composite and establish the electrical conductivity threshold. Afterward, we subject the samples to bending loads with several transverse displacements, measuring the electrical conductivity during loading and unloading. We analyze the input factors and employ prediction methods such as the design of experiments (DOE), artificial neural networks (ANNs), and extreme learning machines (ELM) to forecast the response factors. The ANNs and ELM models prove effective in accurately predicting data, and the model generated by DOE is statistically valid with a confidence level exceeding 95 %. We then compare the forecasted responses with the experimental results. Our experimental findings indicate that the decrease in electrical conductivity due to bending is minimal in carbon/epoxy samples containing CNTs and most significant in samples containing CB. Additionally, we determine the bending strength of the specimens using a three-point bending method. We examine the distribution pattern of nanoparticles in the samples through scanning electron microscope images. The results of this study carry significant implications for the manufacturing of composite electrodes subjected to bending loads in industrial applications. |
| format | Article |
| id | doaj-art-ef010959ce2e46a98453be54f8673e33 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-ef010959ce2e46a98453be54f8673e332025-01-30T05:14:53ZengElsevierResults in Engineering2590-12302025-03-0125104174Effect of bending load on electrical conductivity of carbon/epoxy composites filled with nanoparticles using design of experiment and artificial neural networksAli Sadollah0Seyed Morteza Razavi1Abobakr Khalil Al-Shamiri2Department of Mechanical Engineering, Faculty of Engineering, University of Science and Culture, Tehran, IranDepartment of Mechanical Engineering, Faculty of Engineering, University of Science and Culture, Tehran, Iran; Corresponding author.School of Computer Science, University of Southampton Malaysia, 79100 Iskandar Puteri, Johor, MalaysiaIn this study, we investigate the impact of bending load on the electrical conductivity of carbon-epoxy composites containing various nanoparticles. The samples must meet the bending strength requirements and comply with the electrical conductivity standards set by the U.S. Energy Institute for electrode manufacturing. We use carbon black (CB) nanoparticles, carbon nanotubes (CNTs), and epoxy resin to create these samples. Using the four-point resistance method, we determine the optimal weight percentages of CB and CNTs incorporated into the carbon/epoxy composite and establish the electrical conductivity threshold. Afterward, we subject the samples to bending loads with several transverse displacements, measuring the electrical conductivity during loading and unloading. We analyze the input factors and employ prediction methods such as the design of experiments (DOE), artificial neural networks (ANNs), and extreme learning machines (ELM) to forecast the response factors. The ANNs and ELM models prove effective in accurately predicting data, and the model generated by DOE is statistically valid with a confidence level exceeding 95 %. We then compare the forecasted responses with the experimental results. Our experimental findings indicate that the decrease in electrical conductivity due to bending is minimal in carbon/epoxy samples containing CNTs and most significant in samples containing CB. Additionally, we determine the bending strength of the specimens using a three-point bending method. We examine the distribution pattern of nanoparticles in the samples through scanning electron microscope images. The results of this study carry significant implications for the manufacturing of composite electrodes subjected to bending loads in industrial applications.http://www.sciencedirect.com/science/article/pii/S2590123025002592Electrical conductivityCarbon blackCarbon nanotubesArtificial neural networksExtreme learning machine |
| spellingShingle | Ali Sadollah Seyed Morteza Razavi Abobakr Khalil Al-Shamiri Effect of bending load on electrical conductivity of carbon/epoxy composites filled with nanoparticles using design of experiment and artificial neural networks Results in Engineering Electrical conductivity Carbon black Carbon nanotubes Artificial neural networks Extreme learning machine |
| title | Effect of bending load on electrical conductivity of carbon/epoxy composites filled with nanoparticles using design of experiment and artificial neural networks |
| title_full | Effect of bending load on electrical conductivity of carbon/epoxy composites filled with nanoparticles using design of experiment and artificial neural networks |
| title_fullStr | Effect of bending load on electrical conductivity of carbon/epoxy composites filled with nanoparticles using design of experiment and artificial neural networks |
| title_full_unstemmed | Effect of bending load on electrical conductivity of carbon/epoxy composites filled with nanoparticles using design of experiment and artificial neural networks |
| title_short | Effect of bending load on electrical conductivity of carbon/epoxy composites filled with nanoparticles using design of experiment and artificial neural networks |
| title_sort | effect of bending load on electrical conductivity of carbon epoxy composites filled with nanoparticles using design of experiment and artificial neural networks |
| topic | Electrical conductivity Carbon black Carbon nanotubes Artificial neural networks Extreme learning machine |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025002592 |
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