Fathi Reham, Chen Minghe, Abdallah Mohammed, Saleh Bassiouny
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China.
Materials (Basel). 2024 Sep 14;17(18):4523. doi: 10.3390/ma17184523.
This study focuses on the production of functionally graded composites by utilizing magnesium matrix waste chips and cost-effective eggshell reinforcements through centrifugal casting. The wear behavior of the produced samples was thoroughly examined, considering a range of loads (5 N to 35 N), sliding speeds (0.5 m/s to 3.5 m/s), and sliding distances (500 m to 3500 m). The worn surfaces were carefully analyzed to gain insights into the underlying wear mechanisms. The results indicated successful eggshell particle integration in graded levels within the composite, enhancing hardness and wear resistance. In the outer zone, there was a 25.26% increase in hardness over the inner zone due to the particle gradient, with wear resistance improving by 19.8% compared to the inner zone. To predict the wear behavior, four distinct machine learning algorithms were employed, and their performance was compared using a limited dataset obtained from various test operations. The tree-based machine learning model surpassed the deep neural-based models in predicting the wear rate among the developed models. These models provide a fast and effective way to evaluate functionally graded magnesium composites reinforced with eggshell particles for specific applications, potentially decreasing the need for extensive additional tests. Notably, the LightGBM model exhibited the highest accuracy in predicting the testing set across the three zones. Finally, the study findings highlighted the viability of employing magnesium waste chips and eggshell particles in crafting functionally graded composites. This approach not only minimizes environmental impact through material repurposing but also offers a cost-effective means of utilizing these resources in creating functionally graded composites for automotive components that demand varying hardness and wear resistance properties across their surfaces, from outer to inner regions.
本研究聚焦于通过离心铸造利用镁基废屑和具有成本效益的蛋壳增强体来生产功能梯度复合材料。考虑到一系列载荷（5 N至35 N）、滑动速度（0.5 m/s至3.5 m/s）和滑动距离（500 m至3500 m），对所制备样品的磨损行为进行了全面研究。对磨损表面进行了仔细分析，以深入了解潜在的磨损机制。结果表明，蛋壳颗粒成功地以梯度形式融入复合材料中，提高了硬度和耐磨性。在外层区域，由于颗粒梯度，硬度比内层区域提高了25.26%，耐磨性比内层区域提高了19.8%。为了预测磨损行为，采用了四种不同的机器学习算法，并使用从各种测试操作中获得的有限数据集对它们的性能进行了比较。在已开发的模型中，基于树的机器学习模型在预测磨损率方面超过了基于深度神经网络的模型。这些模型为评估用于特定应用的蛋壳颗粒增强功能梯度镁复合材料提供了一种快速有效的方法，有可能减少大量额外测试的需求。值得注意的是，LightGBM模型在预测三个区域的测试集时表现出最高的准确率。最后，研究结果突出了在制备功能梯度复合材料中使用镁废屑和蛋壳颗粒的可行性。这种方法不仅通过材料再利用将环境影响降至最低，而且还提供了一种具有成本效益的方式，利用这些资源来制造用于汽车部件的功能梯度复合材料，这些部件在其表面从外层到内层区域需要不同的硬度和耐磨性能。
