The study evaluated the performance of various machine learning methods in predicting tensile strength of microwave post-cured composite tailored for weight-sensitive applications. Using forty six training data pairs from the Box-Behnken design plan, an Adaptive Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) were built to predict the optimum tensile strength of polyurethane wood ash composite. Numerical optimization was done using the graft of ANFIS-MOGA method. The process control factors considered were particle size, curing time, power level, volume fraction and curing angle. The predictive accuracy of the evaluated machine learning methods were assessed using Coefficient of Determination (R²), Root Mean Square Error (RMSE), Mean Average Error (MAE) and Standard Error of Prediction (SEP). RSM (RMSE = 0.0339, MAE = 0.0002, SEP = 0.0295, R² = 0.994) and ANFIS (RMSE = 0.0307, MAE = 0.0098, SEP = 0.0267, R² = 0.995) models gave higher degree of accuracy than ANN model (RMSE = 0.0827, MAE = 0.0124, SEP = 0.0719, R² = 0.988). The optimization exercise gave optimal tensile strength of 2.27 MPa at optimum process setting of 177µm particle size, 33 min of curing time, power level at 411 Watt, 42% volume fraction and 11° curing angle. Complementary trial at the named optimum process setting conveyed workable results. Furthermore, selecting microwave post-cured composite for weight-sensitive applications was justified considering that the desirability factor for polyurethane (42% wood ash) is fairly higher than other material in the same class, this points to the fact that deployment of the microwave post-cured composite in weigh-sensitive applications could benefit weight reduction.

该研究评估了各种机器学习方法在预测为重量敏感应用量身定制的微波后固化复合材料的拉伸强度方面的性能。使用 Box-Behnken 设计计划中的 46 个训练数据对，构建了基于自适应网络的模糊推理系统 (ANFIS) 和人工神经网络 (ANN)，以预测聚氨酯木灰复合材料的最佳拉伸强度。数值优化采用ANFIS-MOGA方法的嫁接。考虑的过程控制因素是颗粒大小、固化时间、功率水平、体积分数和固化角度。评估的机器学习方法的预测准确性使用确定系数（R ²)、均方根误差 (RMSE)、平均误差 (MAE) 和标准预测误差 (SEP)。RSM (RMSE = 0.0339, MAE = 0.0002, SEP = 0.0295, R ²  = 0.994) 和 ANFIS (RMSE = 0.0307, MAE = 0.0098, SEP = 0.0267, R ²  = 0.995) 模型比 ANN 模型 (RMSE = 0.0827，MAE = 0.0124，SEP = 0.0719，R ² = 0.988）。优化练习在 177µm 粒径、33 分钟固化时间、411 瓦功率水平、42% 体积分数和 11° 固化角的最佳工艺设置下给出了 2.27 兆帕的最佳拉伸强度。在指定的最佳工艺设置下的补充试验传达了可行的结果。此外，考虑到聚氨酯（42% 木灰）的可取性因素远高于同级别的其他材料，因此选择微波后固化复合材料用于重量敏感的应用是合理的，这表明微波柱的部署是合理的。重量敏感应用中的固化复合材料可能有利于减轻重量。