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Analysis and prediction of residual stresses based on cutting temperature and cutting force in rough turning of Ti–6Al–4V

残余应力 机械加工 残余物 材料科学 GSM演进的增强数据速率 半径 压力(语言学) 冶金 工程类 数学 计算机科学 算法 计算机安全 语言学 电信 哲学
作者
Li Guo,Wanqiu Lu,Shuchun Huang,Xingyu Zhang,Shuiting Ding
出处
期刊:Heliyon [Elsevier BV]
卷期号:8 (11): e11661-e11661 被引量:25
标识
DOI:10.1016/j.heliyon.2022.e11661
摘要

Turning is a typical machining process, which is widely used in the manufacturing process of parts. The residual stress introduced by turning has a significant influence on the mechanical properties, fatigue performance, and service safety, and is one of the key factors affecting the fatigue life of parts. Conventional residual stress prediction models based on cutting parameters cannot consider all the influencing factors of turning and are strongly dependent on the specific cutting environment and tool, so they have limitations. Therefore, a residual stress analysis and prediction method based on cutting temperature and cutting force is proposed in this paper for the rough turning process of Ti-6Al-4V. Firstly, the sensitivity analysis of turning residual stress is carried out on eight cutting variables to determine the key cutting variables affecting the residual stress. Subsequently, the influence of the above key variables on residual stress is analyzed from the perspective of cutting temperature and cutting force. Finally, the residual stress prediction model based on cutting temperature and cutting force is established. The results show that the three variables that have the greatest influence on residual stresses are friction coefficient, tool edge radius, and cutting speed. The friction coefficient and tool edge radius affect the thickness of the residual stress layer. The cutting speed has little effect on the thickness of the residual stress layer, but increasing the cutting speed will lead to the transformation of residual stress to tensile stress. The relative error between the predicted value and the simulated value of residual stress is less than 6%, indicating that the prediction model has high accuracy and can effectively predict the residual stress. The prediction method proposed in this paper is not limited by the specific turning condition and provides a new perspective for the analysis and prediction of turning residual stress.

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