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首页> 外文期刊>Polymer Composites >Application of box-behnken design and neural computation for tribo-mechanical performance analysis of iron-mud-filled glass-fiber/epoxy composite and parametric optimization using PSO
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Application of box-behnken design and neural computation for tribo-mechanical performance analysis of iron-mud-filled glass-fiber/epoxy composite and parametric optimization using PSO

机译：箱形式设计与神经计算的应用摩擦玻璃纤维/环氧复合材料和参数优化的摩擦力性能分析

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摘要

This study investigates the possible utilization of iron mine waste for developing a new class of hybrid polymer composites. The composites were fabricated using hand-layup process by reinforcing woven glass fibers in the epoxy polymer filled with different weight proportions of iron-mud. Abrasion wear experiments were conducted according to Box-Behnken design approach under controlled laboratory conditions using a dry abrasion tester. It was found that hardness, tensile modulus, impact energy and abrasion resistance of the fabricated composites improved with filler addition. Also, a prediction tool based on artificial neural network was implemented to investigate the tribological properties of the composites and the results were compared with the experimental ones. A metaheuristic approach like particle swarm optimization was also used to reveal the minimum wear (in volume) at optimal parametric combination. The results showed increase in both wear (in volume) and specific wear rate with respect to increase in the loads as well as sliding velocity. It also exhibited an increase in the wear with decrease in the specific wear rate with respect to the abrading distance. Finally, the morphology of the abraded surfaces was examined by using scanning electron microscopy and the possible abrasion mechanisms were critically examined and presented. POLYM. COMPOS., 40:1433-1449, 2019. (c) 2018 Society of Plastics Engineers
机译：本研究研究了铁矿废物的可能利用，以开发新类杂化聚合物复合材料。通过在填充具有不同重量比例的铁泥的环氧聚合物中加强编织玻璃纤维，使用手持玻璃纤维制造复合材料。根据使用干磨损测试仪的受控实验室条件下的Box-Behnken设计方法进行磨损磨损实验。结果发现，用填充剂加入改善制造复合材料的硬度，拉伸模量，冲击能量和耐磨性。此外，实施了基于人工神经网络的预测工具，以研究复合材料的摩擦学性质，并将结果与​​实验性进行比较。粒子群优化等成形培育方法也用于在最佳参数组合时露出最小磨损（体积）。结果表明，耐磨（体积）和特定磨损相对于负载的增加以及滑动速度增加。它还表现出磨损的增加，其磨损相对于研磨距离减小。最后，通过使用扫描电子显微镜检查磨损表面的形态，并且可能的磨损机制受到严重检查和呈现。聚合物。 Compos。，40：1433-1449,2019。（c）2018年塑料工程师协会

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