Saleh Mustafa, Anwar Saqib, Al-Ahmari Abdulrahman M, AlFaify Abdullah Yahia
Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia.
Polymers (Basel). 2023 Mar 30;15(7):1720. doi: 10.3390/polym15071720.
This study investigates the influence of design, relative density (RD), and carbon fiber (CF) incorporation parameters on mechanical characteristics, including compressive modulus (E), strength, and specific energy absorption (SEA) of triply periodic minimum surface (TPMS) lattice structures. The TPMS lattices were 3D-printed by fused filament fabrication (FFF) using polylactic acid (PLA) and carbon fiber-reinforced PLA(CFRPLA). The mechanical properties of the TPMS lattice structures were evaluated under uniaxial compression testing based on the design of experiments (DOE) approach, namely, full factorial design. Prediction modeling was conducted and compared using mathematical and intelligent modeling, namely, adaptive neuro-fuzzy inference systems (ANFIS). ANFIS modeling allowed the 3D printing imperfections (e.g., RD variations) to be taken into account by considering the actual RDs instead of the designed ones, as in the case of mathematical modeling. In this regard, this was the first time the ANFIS modeling utilized the actual RDs. The desirability approach was applied for multi-objective optimization. The mechanical properties were found to be significantly influenced by cell type, cell size, CF incorporation, and RD, as well as their combination. The findings demonstrated a variation in the E (0.144 GPa to 0.549 GPa), compressive strength (4.583 MPa to 15.768 MPa), and SEA (3.759 J/g to 15.591 J/g) due to the effect of the studied variables. The ANFIS models outperformed mathematical models in predicting all mechanical characteristics, including E, strength, and SEA. For instance, the maximum absolute percent deviation was 7.61% for ANFIS prediction, while it was 21.11% for mathematical prediction. The accuracy of mathematical predictions is highly influenced by the degree of RD deviation: a higher deviation in RD indicates a lower accuracy of predictions. The findings of this study provide a prior prediction of the mechanical behavior of PLA and CFRPLA TPMS structures, as well as a better understanding of their potential and limitations.
本研究调查了设计、相对密度（RD）和碳纤维（CF）掺入参数对三重周期极小曲面（TPMS）晶格结构力学特性的影响，这些力学特性包括压缩模量（E）、强度和比能量吸收（SEA）。TPMS晶格通过熔融长丝制造（FFF）工艺，使用聚乳酸（PLA）和碳纤维增强聚乳酸（CFRPLA）进行3D打印。基于实验设计（DOE）方法，即全因子设计，在单轴压缩试验下评估TPMS晶格结构的力学性能。使用数学建模和智能建模，即自适应神经模糊推理系统（ANFIS）进行预测建模并比较。与数学建模不同，ANFIS建模通过考虑实际相对密度而非设计相对密度，将3D打印缺陷（如相对密度变化）纳入考虑范围。在这方面，这是ANFIS建模首次使用实际相对密度。采用合意性方法进行多目标优化。发现力学性能受胞元类型、胞元尺寸、碳纤维掺入量、相对密度及其组合的显著影响。研究结果表明，由于所研究变量的影响，压缩模量（0.144吉帕至0.549吉帕）、抗压强度（4.583兆帕至15.768兆帕）和比能量吸收（3.759焦/克至15.591焦/克）存在变化。在预测包括压缩模量、强度和比能量吸收在内的所有力学特性方面，ANFIS模型优于数学模型。例如，ANFIS预测的最大绝对百分比偏差为7.61%，而数学预测为21.11%。数学预测的准确性受相对密度偏差程度的高度影响：相对密度偏差越大，预测准确性越低。本研究结果为PLA和CFRPLA TPMS结构的力学行为提供了预先预测，并有助于更好地理解其潜力和局限性。
