<p>Chemical Composition of AlP0507.</p>

In the present investigation, the influence of various casting parameters viz. stirrer time, stirrer speed, and processing temperature and reinforcement content on the mechanical properties of AlP0507/CNT/RHA composite is assessed. The optimum parameter combination that produces greater multi-objective performance was obtained using the GRA method. The comparison of all R2-score showed that the ANN model is best fitted to predict the tensile strength of HAMMC with highest R2- score of 99.65%. GRA established that the MWCNT content has most significant influence on the response parameters followed by stirring time, RHA content, stirring speed and processing temperature; and the best properties of stir cast HAMMCs was obtained by the combination A2B3C3D2E2. ANNOVA performed on GRA indicated that MWCNT content with contribution of 48.26% exerted maximum impact on the properties of the fabricated HAMMC, followed by stirring time with contribution 19.4%. Processing temperature contributed least with meagre contribution of 2.17%. The predicted value of GRG (0.830775) was found very close to the GRG value of the highest-ranked experiment (0.79643454) confirming the accuracy of the optimization and its validation. The improvement in GRG value by 0.09792454 shows that the optimized parameters provided the optimal results and can be recommended.本研究针对AlP0507/碳纳米管（Carbon Nanotube, CNT）/稻壳灰（Rice Husk Ash, RHA）复合材料的力学性能，评估了各类铸造工艺参数——即搅拌时间、搅拌转速、加工温度与增强相含量——的影响。采用灰色关联分析（Grey Relational Analysis, GRA）方法，得到了可实现最优多目标性能的工艺参数组合。对所有决定系数R²进行对比后发现，人工神经网络（Artificial Neural Network, ANN）模型对搅拌铸造混杂铝基复合材料（Hybrid Aluminium Metal Matrix Composite, HAMMC）的抗拉强度预测拟合效果最优，最高决定系数可达99.65%。灰色关联分析结果表明，多壁碳纳米管（Multi-Walled Carbon Nanotube, MWCNT）含量对响应参数的影响最为显著，其次依次为搅拌时间、稻壳灰含量、搅拌转速与加工温度；采用参数组合A2B3C3D2E2可获得性能最优的搅拌铸造混杂铝基复合材料。基于灰色关联分析的方差分析（Analysis of Variance, ANOVA）结果显示，多壁碳纳米管含量对所制备混杂铝基复合材料性能的影响最大，贡献率达48.26%；其次为搅拌时间，贡献率为19.4%，加工温度的影响最小，贡献率仅为2.17%。灰色关联度（Grey Relational Grade, GRG）的预测值为0.830775，与最优等级试验的灰色关联度值（0.79643454）极为接近，验证了参数优化结果的准确性与有效性。灰色关联度值提升了0.09792454，表明该优化参数可获得最优性能，具备推广应用价值。