喷嘴
材料科学
挤压
复合材料
磨料
表面粗糙度
3D打印
表面光洁度
极限抗拉强度
3d打印
扫描电子显微镜
聚合物
熔融沉积模型
机械工程
生物医学工程
工程类
医学
作者
Iacopo Bianchi,Tommaso Mancia,Chiara Mignanelli,Michela Simoncini
标识
DOI:10.1007/s00170-024-13035-7
摘要
Abstract A widespread problem of manufacturing processes is represented by the occurrence of tool wear that can lead to both poor surface finish and poor mechanical properties in the workpiece. This issue affects also additive manufacturing technologies such as the material extrusion technique. In this process, the wear mechanisms of the extrusion nozzle can be severe, in particular when materials with a high abrasive capacity, such as carbon fiber-reinforced polymers, are 3D printed. Despite the significance of this problem, scientific literature lacks systematic evaluations of nozzle wear and its correlation with parts mechanical properties. In this framework, the present paper aims at investigating the effect of the nozzle wear evolution on the mechanical properties of additively manufactured parts in short carbon fiber-reinforced polyamide. To this purpose, 3D printing processes were performed. The time dependence of the nozzle wear was analyzed by interrupting the additive manufacturing process at fixed time intervals. To analyze the effect of nozzle wear on the mechanical performances of printed parts, tensile specimens were 3D printed and tested at room temperature. A reduction in mechanical performances of the printed samples and a worsening in the surface quality were observed with increasing the nozzle wear. Optical microscopy investigation and X-ray computed tomography were used to monitor the external and internal nozzle wear evolution. The surface roughness measurements were performed to evaluate the surface quality of the 3D printed parts. Furthermore, the scanning electron microscopy was used to observe the three-dimensional topography of the longitudinal sections of filament in Carbon PA, at different printing time values, and fractured surfaces of tensile samples. This study can help to better understand nozzle wear and to predict tool service life for industrial applications. In addition, it can prompt future studies focused on the reduction of tool wear.
科研通智能强力驱动
Strongly Powered by AbleSci AI