材料科学
体积分数
机械加工
复合材料
振动
刀具磨损
超声波传感器
激光器
热的
表面完整性
航空航天
机械工程
声学
冶金
光学
物理
工程类
气象学
政治学
法学
作者
Peicheng Peng,Daohui Xiang,Zhaojie Yuan,Zhiqiang Zhang,Chaosheng Song,Guofu Gao,Xiaobin Cui,Bo Zhao
标识
DOI:10.1016/j.ijthermalsci.2024.108995
摘要
High volume fraction SiCp/Al composite materials have excellent material properties. Therefore, it is mainly used in aerospace equipment, precision guided weapons, electronic packaging and other fields. However, when machining it with large hardness using traditional methods (TM), tool wear is severe, and processing efficiency is low. At the same time, the thermal softening characteristics of laser assisted machining (LAM) and the variable cutting depth and tool detachment features of two-dimensional (2D) ultrasonic elliptical vibration turning (UEVT) have unique advantages in reducing tool wear. Therefore, a novel technique involving their combination (LUEVT) is suggested. As tool temperature directly impacts on the tool wear, and the characteristics of the two machining processes make changes in cutting temperature more complex. We have developed an analytical model and a finite element model to predict the tool temperature. This model considers the influence of ultrasonic parameters, laser parameters, particle effects, etc. First, the temperature distribution and temperature variation characteristics of the transient tool are analyzed. Subsequently, the temperature values of the models are validated experimentally, and the effects of machining parameters on the average temperature of the tool and the wear width of the tool are analyzed. According to the prediction models, the maximum error is 16.7%, demonstrating good agreement with experimental data. The effective reduction of tool temperature depends on the effective combination of ultrasonic, laser and cutting parameters, etc. Compared with TM, LUEVT can significantly decrease tool wear, but the tool temperature is higher than TM. The developed models are important for the temperature control and tool life study of SiCp/Al composites in UEVT and LAM.
科研通智能强力驱动
Strongly Powered by AbleSci AI