In this research work, the performance determining criteria’s (PDC) measures like mechanical, tribological and thermo-mechanical etc. of AA2024-SiC alloy composite (i.e. ASC-0; ASC-2; ASC-4; ASC-6) are analysed using hybrid Analytic Hierarchy Process (AHP) and Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) technique (a Multi-Criteria-Decision-Making (MCDM) technique; computationally simple and easy to understand) in-order-to rank the composites formulations. The order of different PDCs as per AHP is: Coefficient-of-friction > Specific wear rate > Tensile strength ~ Cost > Hardness > Impact strength > Elongation ~ Flexural strength > Voids content ~ Actual density > Fracture toughness > Storage modulus > Thermal conductivity ~ Thermo-gravimetric analysis > Tan δ. The ranking order as per TOPSIS is: ASC-6 > ASC-4 > ASC-2 > ASC-0. The sensitivity analysis study reveals robust ranking-order or priority-order of PDCs as obtained by AHP analysis, when the weights changes from ±30%. The obtained results are in tune with the ranking of the formulations on subjective ground. This proves that MCDM techniques like Hybrid AHP-TOPSIS aids in taking skillful decision by ranking the formulations based on performance measures.

中文翻译：

---

混合AHP-TOPSIS技术在碳化硅陶瓷颗粒增强AA2024合金复合材料材料性能分析中的应用

在这项研究工作中，使用以下方法分析了AA2024-SiC合金复合材料（即ASC-0; ASC-2; ASC-4; ASC-6）的性能确定标准（PDC）措施，如机械，摩擦学和热机械等。混合分析层次结构（AHP）和与理想解决方案相似的订单偏好技术（TOPSIS）技术（一种多标准决策方法（MCDM）技术；计算简单易懂），以便对复合材料进行排序配方。根据AHP，不同PDC的顺序为：摩擦系数>比磨损率>抗拉强度〜成本>硬度>冲击强度>伸长率〜弯曲强度>空隙率〜实际密度>断裂韧性>储能模量>导热系数〜热重分析> Tanδ。根据TOPSIS的排名顺序为：ASC-6> ASC-4> ASC-2> ASC-0。灵敏度分析研究显示，当权重从±30％变化时，通过AHP分析获得的PDC的稳健排序或优先顺序。所获得的结果与基于主观理由的配方排名一致。这证明，像Hybrid AHP-TOPSIS这样的MCDM技术可通过根据性能指标对配方进行排名来帮助做出熟练的决策。