TY - JOUR
T1 - Milling distortion prediction for thin-walled component based on the average MIRS in specimen machining
AU - Zhang, Zhongxi
AU - Zhang, Zhao
AU - Zhang, Dinghua
AU - Luo, Ming
N1 - Publisher Copyright:
© 2020, Springer-Verlag London Ltd., part of Springer Nature.
PY - 2020/12
Y1 - 2020/12
N2 - Machining-induced residual stress (MIRS) has significant effects on the distortion of thin-walled part, especially for the titanium alloy and superalloy parts with complex shape. However, the MIRS is hard to accurately predict through analytical modeling and finite element simulation. In addition, the measurement of MIRS is time-consuming and costly. Therefore, the MIRS caused distortion is hard to accurately predict. In the present study, a novel method is introduced to calculate the average value of MIRS by milling a thin-walled specimen and measuring the distortion, and predict the distortion of the thin-walled component with the calculated average MIRSs. Firstly, the mathematical relationship between the average MIRS and the distortion of specimen is established by analyzing the distribution of residual stress after machining and distortion. In order to estimate the average MIRS accurately, the curvatures of the distorted specimen in both directions are calculated by processing the specimen and measuring the distortion. Based on the calculated average MIRS and the finite element method (FEM), the distortion of the thin-walled component caused by MIRS can be accurately predicted. After that, a relative error calculation model for equivalent bending moment is constructed to assess the algorithm. Subsequently, two-piece of thin-walled specimens are milled to calculate the average MIRSs. Finally, the thin-walled plate and a simplified blade are milled with the same machining parameters to verify the presented method. The result demonstrates the satisfactory agreement between the measured and simulated distortion with the error less than 20%.
AB - Machining-induced residual stress (MIRS) has significant effects on the distortion of thin-walled part, especially for the titanium alloy and superalloy parts with complex shape. However, the MIRS is hard to accurately predict through analytical modeling and finite element simulation. In addition, the measurement of MIRS is time-consuming and costly. Therefore, the MIRS caused distortion is hard to accurately predict. In the present study, a novel method is introduced to calculate the average value of MIRS by milling a thin-walled specimen and measuring the distortion, and predict the distortion of the thin-walled component with the calculated average MIRSs. Firstly, the mathematical relationship between the average MIRS and the distortion of specimen is established by analyzing the distribution of residual stress after machining and distortion. In order to estimate the average MIRS accurately, the curvatures of the distorted specimen in both directions are calculated by processing the specimen and measuring the distortion. Based on the calculated average MIRS and the finite element method (FEM), the distortion of the thin-walled component caused by MIRS can be accurately predicted. After that, a relative error calculation model for equivalent bending moment is constructed to assess the algorithm. Subsequently, two-piece of thin-walled specimens are milled to calculate the average MIRSs. Finally, the thin-walled plate and a simplified blade are milled with the same machining parameters to verify the presented method. The result demonstrates the satisfactory agreement between the measured and simulated distortion with the error less than 20%.
KW - Distortion prediction
KW - Machining-induced residual stress
KW - Milling
KW - Thin-walled component
UR - http://www.scopus.com/inward/record.url?scp=85095997077&partnerID=8YFLogxK
U2 - 10.1007/s00170-020-06281-y
DO - 10.1007/s00170-020-06281-y
M3 - 文章
AN - SCOPUS:85095997077
SN - 0268-3768
VL - 111
SP - 3379
EP - 3392
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
IS - 11-12
ER -