Ulkir Osman, Ersoy Sezgin
Department of Electric and Energy, Mus Alparslan University, Mus 49210, Türkiye.
Ersoy Advanced Research in Mechatronics and Artificial Intelligence, Marmara University, Istanbul 34722, Türkiye.
Polymers (Basel). 2025 Jul 23;17(15):2012. doi: 10.3390/polym17152012.
This study explores the mechanical behavior of polymer and composite specimens fabricated using fused deposition modeling (FDM), focusing on three material configurations: acrylonitrile butadiene styrene (ABS), carbon fiber-reinforced polyphthalamide (PPA/Cf), and a sandwich-structured composite. A systematic experimental plan was developed using the Box-Behnken design (BBD) to investigate the effects of material type (MT), infill pattern (IP), and printing direction (PD) on tensile and flexural strength. Experimental results showed that the PPA/Cf material with a "Cross" IP printed "Flat" yielded the highest mechanical performance, achieving a tensile strength of 75.8 MPa and a flexural strength of 102.3 MPa. In contrast, the lowest values were observed in ABS parts with a "Grid" pattern and "Upright" orientation, recording 37.8 MPa tensile and 49.5 MPa flexural strength. Analysis of variance (ANOVA) results confirmed that all three factors significantly influenced both outputs ( < 0.001), with MT being the most dominant factor. Machine learning (ML) algorithms, Bayesian linear regression (BLR), and Gaussian process regression (GPR) were employed to predict mechanical performance. GPR achieved the best overall accuracy with R = 0.9935 and MAPE = 11.14% for tensile strength and R = 0.9925 and MAPE = 12.96% for flexural strength. Comparatively, the traditional BBD yielded slightly lower performance with MAPE = 13.02% and R = 0.9895 for tensile strength. Validation tests conducted on three unseen configurations clearly demonstrated the generalization capability of the models. Based on actual vs. predicted values, the GPR yielded the lowest average prediction errors, with MAPE values of 0.54% for tensile and 0.45% for flexural strength. In comparison, BLR achieved 0.79% and 0.60%, while BBD showed significantly higher errors at 1.76% and 1.32%, respectively.
本研究探讨了采用熔融沉积建模（FDM）制造的聚合物和复合材料试样的力学行为，重点关注三种材料配置：丙烯腈-丁二烯-苯乙烯（ABS）、碳纤维增强聚邻苯二甲酰胺（PPA/Cf）和一种三明治结构复合材料。采用Box-Behnken设计（BBD）制定了系统的实验方案，以研究材料类型（MT）、填充图案（IP）和打印方向（PD）对拉伸强度和弯曲强度的影响。实验结果表明，采用“十字”填充图案并“平放”打印的PPA/Cf材料具有最高的力学性能，其拉伸强度达到75.8MPa，弯曲强度达到102.3MPa。相比之下，具有“网格”图案和“直立”方向的ABS零件的力学性能最低，拉伸强度为37.8MPa，弯曲强度为49.5MPa。方差分析（ANOVA）结果证实，所有三个因素均对两个输出结果有显著影响（<0.001），其中MT是最主要的因素。采用机器学习（ML）算法、贝叶斯线性回归（BLR）和高斯过程回归（GPR）来预测力学性能。GPR在预测拉伸强度方面总体准确率最高，R = 0.9935，平均绝对百分比误差（MAPE）= 11.14%；预测弯曲强度时，R = 0.9925，MAPE = 12.96%。相比之下，传统的BBD在预测拉伸强度时性能略低，MAPE = 13.02%，R = 0.9895。对三种未见过的配置进行的验证测试清楚地证明了模型的泛化能力。根据实际值与预测值，GPR产生的平均预测误差最低，拉伸强度的MAPE值为0.54%，弯曲强度的MAPE值为0.45%。相比之下，BLR分别为0.79%和0.60%，而BBD的误差明显更高，分别为1.76%和1.32%。
