Soft computing tools emerged as most reliable alternatives of traditional regression and statistical methods. In recent times, these tools can predict the optimum material compositions, mechanical and tribological properties of composite materials accurately without much experiment or even without experiment. In the present study, soft computing tools like fuzzy logic, Decision tree, genetic algorithms are employed to predict the reinforcement weight percentage of B4C(Boron Carbide) and Graphite(Gr) along with Aluminum (matrix material) weight percentage for Al2219 with B4C and graphite. The optimized material and tribological properties of Al2219 were also predicted using NSGA II genetic algorithms for multi-objective optimization. It is found that the predictions are at par with earlier ANN (artificial neural network) studies and experimental findings. It can be inferred that inclusion B4C has more impact on enhancement of mechanical properties as well as wear strength compared to Gr.

翻译：软计算工具已成为传统回归与统计方法最可靠的替代方案。近年来，这些工具能够准确预测复合材料的最佳材料组成、力学性能及摩擦学性能，而无需大量实验甚至无需实验。本研究采用模糊逻辑、决策树、遗传算法等软计算工具，预测Al2219基体与B4C（碳化硼）及石墨（Gr）增强体的重量百分比配比。同时，利用NSGA II遗传算法进行多目标优化，预测了Al2219复合材料优化的材料性能与摩擦学性能。结果表明，其预测结果与早期人工神经网络（ANN）研究及实验数据高度吻合。可以推断，与石墨相比，B4C的加入对提升材料力学性能及磨损强度具有更显著的影响。