Variance-based sensitivity analysis for the influence of residual stress on machining deformation

2021 ◽  
Vol 68 ◽  
pp. 1072-1085
Author(s):  
Xiaoyue Li ◽  
Liang Li ◽  
Yinfei Yang ◽  
Guolong Zhao ◽  
Ning He ◽  
...  
2013 ◽  
Vol 37 (3) ◽  
pp. 387-395 ◽  
Author(s):  
Hong Yeol Bae ◽  
Chang Young Oh ◽  
Yun Jae Kim ◽  
Kwon Hee Kim ◽  
Soo Won Chae ◽  
...  

Materials ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3121 ◽  
Author(s):  
Xiaoli Qiu ◽  
Xianqiang Cheng ◽  
Penghao Dong ◽  
Huachen Peng ◽  
Yan Xing ◽  
...  

The Johnson-Cook (J-C) constitutive model, including five material constants (A, B, n, C, m), and the Coulomb friction coefficient (μ) are critical preprocessed data in machining simulations. Before they become reliable preprocessed data, investigating these parameters’ effect on simulation results benefits parameter-selecting. This paper aims to investigate the different influence of five settings of the J-C constitutive equation and Coulomb friction coefficient on the turning simulation results of Inconel 718 under low-high cutting conditions, including residual stress, chip morphology, cutting force and temperature. A three-dimensional (3-D) finite element model was built, meanwhile, the reliability of the model was verified by comparing the experiment with the simulation. Sensitivity analysis of J-C parameters and friction coefficient on simulation results at low-high cutting conditions was carried out by the hybrid orthogonal test. The results demonstrate that the simulation accuracy of Inconel 718 is more susceptible to strain hardening and thermal softening in the J-C constitutive model. The friction coefficient only has significant effects on axial and radial forces in the high cutting condition. The influences of the coefficient A, n, and m on the residual stress, chip thickness, cutting force and temperature are especially significant. As the cutting parameters increase, the effect of the three coefficients will change visibly. This paper provides direction for controlling simulation results through the adjustment of the J-C constitutive model of Inconel 718 and the friction coefficient.


Author(s):  
Xia Ji ◽  
Steven Y Liang

This article presents a sensitivity analysis of residual stress based on the verified residual stress prediction model. The machining-induced residual stress is developed as a function of cutting parameters, tool geometry, material properties, and lubrication conditions. Based on the residual stress predictive model, the main effects of the cutting force, cutting temperature, and residual stress are quantitatively analyzed through the cosine amplitude method. The parametric study is carried out to investigate the effects of minimum quantity lubrication parameters, cutting parameters, and tool geometry on the cutting performances. Results manifest that the cutting force and residual stress are more sensitive to the heat transfer coefficient and the depth of cut, while the cutting temperature is more sensitive to the cutting speed. Large maximum compressive residual stress is obtained under a lower flow rate of minimum quantity lubrication, small depth of cut, and the proper air–oil mixture ratio. This research can support the controlling and optimization of residual stress in industrial engineering by strategically adjusting the application parameters of minimum quantity lubrication.


2011 ◽  
Vol 317-319 ◽  
pp. 386-392
Author(s):  
Yin Fei Yang ◽  
Ning He ◽  
Liang Li

The unknown and uneven macro-residual stresses in blanks will cause deformation on large-scale component, especially in non-prestretched plates. Based on the retrieval of stress field by measuring stress changes due to the rebalance of stresses after machining, a new idea is proposed in this paper to predict and control the machining deformation of large-scale components. It consists of analysis of the machining deformation, retrieval of macro-residual stress field, and finally optimization of following cutting process. In the retrieval process, the stresses are measured with an improved hole-drilling method and the measured data are then interpolated to 3D stress field.


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