The different percentages of boron carbide (B₄C) reinforced Magnesium metal matrix composites were processed using Powder Metallurgy method. The characterizations are performed by Scanning Electronic Microscope (SEM), X-ray Diffraction pattern, Electron Backscattered Diffraction, and Energy Dispersive Spectrum. The texture analysis of the composites is conducted via an image processing method. Wear tests are executed in the dry sliding state under ambient room conditions. The three levels Box-Behnken design of Response Surface Methodology has been selected to create the mathematical models for the three variables (weight percentage of B₄C, sliding velocity, and load) to predict the responses namely specific wear rate (SWR) and coefficient of friction (COF). The developed model is validated and analyzed through the Analysis of variance (ANOVA). This model has a 95% efficient confidence to forecast the COF and SWR. The worn surface features are extracted and analyzed through an image processing technique. The roughness measurement of the worn surfaces is executed via Atomic Force Microscopy analysis.

Graphic abstract

中文翻译：

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Mg/B4C 复合材料的图像纹理描述、表面晶粒结构形态和磨损参数预测使用响应面方法

使用粉末冶金方法加工不同百分比的碳化硼（B ₄ C）增强的镁金属基复合材料。通过扫描电子显微镜 (SEM)、X 射线衍射图、电子背散射衍射和能量色散谱进行表征。复合材料的纹理分析是通过图像处理方法进行的。磨损测试是在室温下的干滑动状态下进行的。选择响应面方法的三个水平 Box-Behnken 设计来创建三个变量（B _{4 的}重量百分比）的数学模型C、滑动速度和负载）来预测响应，即比磨损率 (SWR) 和摩擦系数 (COF)。开发的模型通过方差分析 (ANOVA) 进行验证和分析。该模型预测 COF 和 SWR 的有效置信度为 95%。通过图像处理技术提取和分析磨损的表面特征。磨损表面的粗糙度测量通过原子力显微镜分析执行。

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