TY - JOUR
T1 - Position-varying surface roughness prediction method considering compensated acceleration in milling of thin-walled workpiece
AU - Yao, Zequan
AU - Fan, Chang
AU - Zhang, Zhao
AU - Zhang, Dinghua
AU - Luo, Ming
N1 - Publisher Copyright:
© 2021, Higher Education Press.
PY - 2021/12
Y1 - 2021/12
N2 - Machined surface roughness will affect parts’ service performance. Thus, predicting it in the machining is important to avoid rejects. Surface roughness will be affected by system position dependent vibration even under constant parameter with certain toolpath processing in the finishing. Aiming at surface roughness prediction in the machining process, this paper proposes a position-varying surface roughness prediction method based on compensated acceleration by using regression analysis. To reduce the stochastic error of measuring the machined surface profile height, the surface area is repeatedly measured three times, and Pauta criterion is adopted to eliminate abnormal points. The actual vibration state at any processing position is obtained through the single-point monitoring acceleration compensation model. Seven acceleration features are extracted, and valley, which has the highest R-square proving the effectiveness of the filtering features, is selected as the input of the prediction model by mutual information coefficients. Finally, by comparing the measured and predicted surface roughness curves, they have the same trends, with the average error of 16.28% and the minimum error of 0.16%. Moreover, the prediction curve matches and agrees well with the actual surface state, which verifies the accuracy and reliability of the model. [Figure not available: see fulltext.]
AB - Machined surface roughness will affect parts’ service performance. Thus, predicting it in the machining is important to avoid rejects. Surface roughness will be affected by system position dependent vibration even under constant parameter with certain toolpath processing in the finishing. Aiming at surface roughness prediction in the machining process, this paper proposes a position-varying surface roughness prediction method based on compensated acceleration by using regression analysis. To reduce the stochastic error of measuring the machined surface profile height, the surface area is repeatedly measured three times, and Pauta criterion is adopted to eliminate abnormal points. The actual vibration state at any processing position is obtained through the single-point monitoring acceleration compensation model. Seven acceleration features are extracted, and valley, which has the highest R-square proving the effectiveness of the filtering features, is selected as the input of the prediction model by mutual information coefficients. Finally, by comparing the measured and predicted surface roughness curves, they have the same trends, with the average error of 16.28% and the minimum error of 0.16%. Moreover, the prediction curve matches and agrees well with the actual surface state, which verifies the accuracy and reliability of the model. [Figure not available: see fulltext.]
KW - compensated acceleration
KW - milling
KW - surface roughness prediction
KW - thin-walled workpiece
UR - http://www.scopus.com/inward/record.url?scp=85114700257&partnerID=8YFLogxK
U2 - 10.1007/s11465-021-0649-z
DO - 10.1007/s11465-021-0649-z
M3 - 文章
AN - SCOPUS:85114700257
SN - 2095-0233
VL - 16
SP - 855
EP - 867
JO - Frontiers of Mechanical Engineering
JF - Frontiers of Mechanical Engineering
IS - 4
ER -