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• W4383218104 abstract "Abstract This study presents an innovative approach utilizing Artificial Neural Network ( ANN ) strategies to evaluate the energy absorption capabilities of eco‐friendly rubber crumb/kenaf composites subjected to low‐velocity impact loads. The primary objectives of this research were to assess the energy absorption characteristics of these sustainable composites, understand their mechanical behavior under the impact, and provide valuable insights into their potential applications. To achieve these objectives, an experimental methodology was employed. Rubber crumb/kenaf composites with varying compositions were prepared, and low‐velocity impact tests were conducted using a drop‐weight impact testing apparatus to assess their energy absorption behavior and these parameters were used as inputs for training the ANN models. The weight percentage of waste tire rubber particle ( WTRP ), type of impactor and impact energies are considered as input data, whereas the absorbed energy is treated as the output. Through the advanced ANN strategies, accurate predictions of energy absorption performance were achieved for the rubber crumb/kenaf composites. The Levenberg–Marquardt optimisation algorithm with ten neurons and a tangent sigmoid activation function is used to train the ANN model. The trained ANN model is tested on an unseen dataset, different from the training data. It is shown to accurately predict the energy absorption characteristics of WTRP / KRE composites with a maximum error of 4.54%. The results revealed that the composite's energy absorption capabilities were influenced by the ratio of rubber crumb to kenaf, as well as the impact velocity. Additionally, the ANN models demonstrated excellent predictive capabilities, enabling efficient estimation of energy absorption behavior. The significance of these results lies in the potential applications of eco‐friendly rubber crumb/kenaf composites. By understanding their energy absorption characteristics, these composites can be effectively utilized in various industries. For instance, they could be employed in automotive parts manufacturing to enhance occupant safety during low‐velocity impact events. Furthermore, these composites can find applications in sports equipment, protective gear, and other impact‐prone products, contributing to sustainable and environmentally friendly materials. It is believed that by adopting the proposed ANN methodology, the experimentation costs and time can be significantly reduced without compromising the accuracy of the results. The obtained results provide valuable insights into the mechanical behavior of these sustainable composites and open avenues for their implementation in diverse industries where impact resistance is crucial Highlights Development of sustainable Rubber crumb/kenaf composites Application of advanced ANN based predictive assessment of the impact response Parametric study of impactor shape and height of impact is performed The damage mechanism contributing to energy absorption is micrographically studied" @default.
• W4383218104 created "2023-07-06" @default.
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• W4383218104 date "2023-07-05" @default.
• W4383218104 modified "2023-10-16" @default.
• W4383218104 title "Evaluating energy absorption of sustainable rubber crumb/kenaf composites through artificial neural network strategies for <scp>low‐velocity</scp> impact loads" @default.
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• W4383218104 doi "https://doi.org/10.1002/pc.27551" @default.
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