材料科学
机械加工
复合材料
分层(地质)
熔融沉积模型
表面粗糙度
钻探
超声波传感器
表面光洁度
机械工程
3D打印
声学
工程类
冶金
生物
物理
古生物学
构造学
俯冲
作者
Pedram Parandoush,Palamandadige Fernando,Hao Zhang,Chang Ye,Junfeng Xiao,Meng Zhang,Dong Lin
出处
期刊:Rapid Prototyping Journal
[Emerald (MCB UP)]
日期:2021-05-04
卷期号:27 (4): 754-768
被引量:7
标识
DOI:10.1108/rpj-10-2019-0260
摘要
Purpose Additively manufactured objects have layered structures, which means post processing is often required to achieve a desired surface finish. Furthermore, the additive nature of the process makes it less accurate than subtractive processes. Hence, additive manufacturing techniques could tremendously benefit from finishing processes to improve their geometric tolerance and surface finish. Design/methodology/approach Rotary ultrasonic machining (RUM) was chosen as a finishing operation for drilling additively manufactured carbon fiber reinforced polymer (CFRP) composites. Two distinct additive manufacturing methods of fused deposition modeling (FDM) and laser-assisted laminated object manufacturing (LA-LOM) were used to fabricate CFRP plates with continuous carbon fiber reinforcement. The influence of the feedrate, tool rotation speed and ultrasonic power of the RUM process parameters on the aforementioned quality characteristics revealed the feasibility of RUM process as a finishing operation for additive manufactured CFRP. Findings The quality of drilled holes in the CFRP plates fabricated via LA-LOM was supremely superior to the FDM counterparts with less pullout delamination, smoother surface and less burr formation. The strong interfacial bonding in LA-LOM proven to be superior to FDM was able to endure higher cutting force of the RUM process. The cutting force and cutting temperature overwhelmed the FDM parts and induced higher surface damage. Originality/value Overall, the present study demonstrates the feasibility of a hybrid additive and subtractive manufacturing method that could potentially reduce cost and waste of the CFRP production for industrial applications.
科研通智能强力驱动
Strongly Powered by AbleSci AI