Stepanov Dmitry Yu, Dontsov Yuri V, Panin Sergey V, Buslovich Dmitry G, Alexenko Vladislav O, Bochkareva Svetlana A, Batranin Andrey V, Kosmachev Pavel V
Microelectronics of Multispectral Quantum Introscopy Laboratory of the R&D Center "Advanced Electronic Technologies", National Research Tomsk State University, 634050 Tomsk, Russia.
Department of Materials Science, Engineering School of Advanced Manufacturing Technologies, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia.
Polymers (Basel). 2024 Sep 14;16(18):2601. doi: 10.3390/polym16182601.
The aim of this study was to optimize a set of technological parameters (travel speed, extruder temperature, and extrusion rate) for 3D printing with a PEEK-based composite reinforced with 30 wt.% glass fibers (GFs). For this purpose, both Taguchi and finite element methods (FEM) were utilized. The artificial neural networks (ANNs) were implemented for computer simulation of full-scale experiments. Computed tomography of the additively manufactured (AM) samples showed that the optimal 3D printing parameters were the extruder temperature of 460 °C, the travel speed of 20 mm/min, and the extrusion rate of 4 rpm (the microextruder screw rotation speed). These values correlated well with those obtained by computer simulation using the ANNs. In such cases, the homogeneous micro- and macro-structures were formed with minimal sample distortions and porosity levels within 10 vol.% of both structures. The most likely reason for porosity was the expansion of the molten polymer when it had been squeezed out from the microextruder nozzle. It was concluded that the mechanical properties of such samples can be improved both by changing the 3D printing strategy to ensure the preferential orientation of GFs along the building direction and by reducing porosity via post-printing treatment or ultrasonic compaction.
本研究的目的是优化一组用于3D打印的工艺参数（行进速度、挤出机温度和挤出速率），该3D打印采用的是含30 wt.%玻璃纤维（GFs）增强的聚醚醚酮基复合材料。为此，同时运用了田口方法和有限元方法（FEM）。采用人工神经网络（ANNs）对全尺寸实验进行计算机模拟。对增材制造（AM）样品的计算机断层扫描显示，最佳的3D打印参数为：挤出机温度460℃、行进速度20mm/min以及挤出速率4rpm（微型挤出机螺杆转速）。这些值与使用人工神经网络通过计算机模拟获得的值高度相关。在这种情况下，可以形成均匀的微观和宏观结构，样品变形和孔隙率水平最小，两种结构的孔隙率均在10体积%以内。孔隙产生的最可能原因是熔融聚合物从微型挤出机喷嘴挤出时发生膨胀。得出的结论是，通过改变3D打印策略以确保玻璃纤维沿构建方向优先取向，以及通过后处理或超声压实降低孔隙率，均可改善此类样品的机械性能。
