学位论文详细信息
Residual stress modeling in machining processes
Incremental plasticity;Analytical modeling;Turning;Orthogonal cutting;Milling
Su, Jiann-Cherng ; Mechanical Engineering
University:Georgia Institute of Technology
Department:Mechanical Engineering
关键词: Incremental plasticity;    Analytical modeling;    Turning;    Orthogonal cutting;    Milling;   
Others  :  https://smartech.gatech.edu/bitstream/1853/14030/1/su_jiann-cherng_200612_phd.pdf
美国|英语
来源: SMARTech Repository
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【 摘 要 】

Residual stresses play an important role in the performance of machined components.Component characteristics that are influenced by residual stress include fatigue life, corrosion resistance, and part distortion.The functional behavior of machined components can be enhanced or impaired by residual stresses.Because of this, understanding the residual stress imparted by machining is an important aspect of understanding machining and overall part quality.Machining-induced residual stress prediction has been a topic of research since the 1950s.Research efforts have been primarily composed of experimental findings, analytical modeling, finite element modeling, and various combinations of those efforts.Although there has been significant research in the area, there are still opportunities for advancing predictive residual stress methods.The objectives of the current research are as follows: (1) develop a method of predicting residual stress based on an analytical description of the machining process and (2) validate the model with experimental data.The research focuses on predicting residual stresses in machining based on first principles.Machining process output parameters such as cutting forces and cutting temperatures are predicted as part of the overall modeling effort.These output parameters serve as the basis for determining the loads which generate residual stresses due to machining.The modeling techniques are applied to a range of machining operations including orthogonal cutting, broaching, milling, and turning.The strengths and weaknesses of the model are discussed as well as opportunities for future work.

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