B. Aliemeke, Lucky Charles, Peace Omoregie, Abdulrazak Momodu, Christopher Jerry, Emmanuel Akpan
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引用次数: 0
摘要
实验采用响应面法(RSM)对金属合金的磨损率参数进行了优化。磨损率是影响金属部件耐久性和性能的关键因素,被视为响应参数,而轨道直径、滑动速度和质量差被视为自变量。中央复合设计(CCD)实验方法系统地探索了响应面,并优化了磨损率。建立的数学模型在方差分析表中显示出 0.043 的显著 p 值,表明在 0.05 的显著性水平下,自变量对磨损率有共同影响。此外,该模型还具有很强的解释力,R 方为 69.45%,调整 R 方为 51.95%。统计拟合度的 p 值为 0.60,表明模型令人满意。这些发现凸显了 RSM 在优化实验输入值方面的有效性,并为提高金属合金在各种工业应用中的耐用性和性能提供了宝贵的见解。所获得的结果解决了输入参数磨损实验最佳水平固有的不确定性问题。
Response Surface Methodology Optimization of wear rate Parameters in metallic alloys
The optimization of wear rate parameters in metallic alloys using Response Surface Methodology (RSM) has been experimentally performed. The wear rate, a critical factor affecting the durability and performance of metallic components, served as the response parameter, while track diameter, sliding speed, and mass difference were considered as independent variables. The Central Composite Design (CCD) experimental method systematically explored the response surface and optimizes the wear rate. A mathematical model was developed, revealing a significant p-value of 0.043 in the ANOVA table, indicating the collective influence of the independent variables on wear rate at a significance level of 0.05. Furthermore, the model demonstrates a substantial explanatory power, with R-squared of 69.45% and adjusted R-squared of 51.95%. The p-value calculated to be 0.60 for the statistical Lack of fit indicated a satisfactory model. These findings highlight the effectiveness of RSM in optimizing the experimental input values and offer valuable insights for enhancing the durability and performance of metallic alloys in various industrial applications. The obtained result addresses the problem of uncertainty inherent in optimal levels of input parameters wear experimentation.