Position-varying surface roughness prediction method considering compensated acceleration in milling of thin-walled workpiece

Abstract

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.

Graphical abstract

Keywords

surface roughness prediction / compensated acceleration / milling / thin-walled workpiece

References

[1] Urbikain Pelayo G, Olvera-Trejo D, Luo M. Surface roughness prediction with new barrel-shape mills considering runout: modelling and validation. Measurement, 2021, 173 : 108670–

[2] Sun W, Yao B, Chen B. Noncontact surface roughness estimation using 2D complex wavelet enhanced ResNet for intelligent evaluation of milled metal surface quality. Applied Sciences (Basel, Switzerland), 2018, 8( 3): 381– 404

[3] Shi D, Gindy N N. Tool wear predictive model based on least squares support vector machines. Mechanical Systems and Signal Processing, 2007, 21( 4): 1799– 1814

[4] Renaudin L, Bonnardot F, Musy O. Natural roller bearing fault detection by angular measurement of true instantaneous angular speed. Mechanical Systems and Signal Processing, 2010, 24( 7): 1998– 2011

[5] Kong D, Zhu J, Duan C. Bayesian linear regression for surface roughness prediction. Mechanical Systems and Signal Processing, 2020, 142 : 106770–

[6] Asiltürk İ, Çunkaş M. Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Systems with Applications, 2011, 38( 5): 5826– 5832

[7] Hessainia Z, Belbah A, Yallese M A. On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations. Measurement, 2013, 46( 5): 1671– 1681

[8] Patel V D, Gandhi A H. Analysis and modeling of surface roughness based on cutting parameters and tool nose radius in turning of AISI D2 steel using CBN tool. Measurement, 2019, 138 : 34– 38

[9] Sun W, Zhang D, Luo M. Machining process monitoring and application: a review. Journal of Advanced Manufacturing Science and Technology, 2021, 1( 2): 2021001–

[10] García Plaza E, Núñez López P J. Analysis of cutting force signals by wavelet packet transform for surface roughness monitoring in CNC turning. Mechanical Systems and Signal Processing, 2018, 98 : 634– 651

[11] Risbood K A, Dixit U S, Sahasrabudhe A D. Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. Journal of Materials Processing Technology, 2003, 132( 1‒3): 203– 214

[12] Salgado D R, Alonso F J. An approach based on current and sound signals for in-process tool wear monitoring. International Journal of Machine Tools and Manufacture, 2007, 47( 14): 2140– 2152

[13] Li R, He D. Rotational machine health monitoring and fault detection using EMD-based acoustic emission feature quantification. IEEE Transactions on Instrumentation and Measurement, 2012, 61( 4): 990– 1001

[14] García Plaza E, Núñez López P J. Surface roughness monitoring by singular spectrum analysis of vibration signals. Mechanical Systems and Signal Processing, 2017, 84 : 516– 530

[15] Chang H K, Kim J H, Kim I H. In-process surface roughness prediction using displacement signals from spindle motion. International Journal of Machine Tools and Manufacture, 2007, 47( 6): 1021– 1026

[16] Sun W, Luo M, Zhang D. Machining vibration monitoring based on dynamic clamping force measuring in thin-walled components milling. International Journal of Advanced Manufacturing Technology, 2020, 107( 5‒6): 2211– 2226

[17] Salgado D R, Alonso F J, Cambero I. In-process surface roughness prediction system using cutting vibrations in turning. International Journal of Advanced Manufacturing Technology, 2009, 43( 1‒2): 40– 51

[18] Quintana Q, Rudolf T, Ciurana J. Surface roughness prediction through internal kernel information and external accelerometers using artificial neural networks. Journal of Mechanical Science and Technology, 2011, 25( 11): 2877– 2886

[19] García Plaza E, Núñez López P J, Beamud González E M. Efficiency of vibration signal feature extraction for surface finish monitoring in CNC machining. Journal of Manufacturing Processes, 2019, 44 : 145– 157

[20] ISO 4287: Geometrical Product Specifications (GPS)―Surface Texture: Profile Method―Terms, Definitions and Surface Texture Parameters, 1997

[21] Upadhyay V, Jain P K, Mehta N K. In-process prediction of surface roughness in turning of Ti–6Al–4V alloy using cutting parameters and vibration signals. Measurement, 2013, 46( 1): 154– 160

[22] Wang H, To S, Chan C. Investigation on the influence of tool-tip vibration on surface roughness and its representative measurement in ultra-precision diamond turning. International Journal of Machine Tools and Manufacture, 2013, 69 : 20– 29

[23] Gómez Muñoz C Q, Arcos Jiménez A, García Márquez F P. Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis. Renewable Energy, 2018, 116 : 42– 54

[24] Zhu K, Wong Y, Hong G. Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. International Journal of Machine Tools and Manufacture, 2009, 49( 7‒9): 537– 553

[25] Chen Y, Li H, Hou L. Feature extraction using dominant frequency bands and time-frequency image analysis for chatter detection in milling. Precision Engineering, 2019, 56 : 235– 245

[26] Wang G, Li W, Jiang C, et al. Simultaneous calibration of multi-coordinates for a dual-robot system by solving the AXB=YCZ problem. IEEE Transactions on Robotics, 2021, 37(4): 1172–1185

[27] Lamraoui M, Barakat M, Thomas M. Chatter detection in milling machines by neural network classification and feature selection. Journal of Vibration and Control, 2015, 21( 7): 1251– 1266

[28] Xue L, Li N, Lei Y. Incipient fault detection for rolling element bearings under varying speed conditions. Materials (Basel), 2017, 10( 6): 675– 690

[29] Han C, Luo M, Zhang D. Optimization of varying-parameter drilling for multi-hole parts using metaheuristic algorithm coupled with self-adaptive penalty method. Applied Soft Computing, 2020, 95 : 106489–

[30] Nguyen D, Yin S, Tang Q. Online monitoring of surface roughness and grinding wheel wear when grinding Ti-6Al-4V titanium alloy using ANFIS-GPR hybrid algorithm and Taguchi analysis. Precision Engineering, 2019, 55 : 275– 292

[31] Agrawal A, Goel S, Rashid W B. Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC). Applied Soft Computing, 2015, 30 : 279– 286

[32] Correa M, Bielza C, Pamies-Teixeira J. Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process. Expert Systems with Applications, 2009, 36( 3): 7270– 7279

[33] Zhang N, Shetty D. An effective LS-SVM-based approach for surface roughness prediction in machined surfaces. Neurocomputing, 2016, 198 : 35– 39

[34] Suresh P V S, Venkateswara Rao P, Deshmukh S G. A genetic algorithmic approach for optimization of surface roughness prediction model. International Journal of Machine Tools and Manufacture, 2002, 42( 6): 675– 680

[35] Ho W, Tsai J, Lin B. Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm. Expert Systems with Applications, 2009, 36( 2): 3216– 3222

[36] Kirby E D, Chen J C. Development of a fuzzy-nets-based surface roughness prediction system in turning operations. Computers & Industrial Engineering, 2007, 53( 1): 30– 42

[37] Wibowo A, Desa M I. Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process. Expert Systems with Applications, 2012, 39( 14): 11634– 11641

[38] Davim P J, Gaitonde V N, Karnik S R. Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models. Journal of Materials Processing Technology, 2008, 205( 1‒3): 16– 23

[39] Zhang Z, Li H, Liu X. Chatter mitigation for the milling of thin-walled workpiece. International Journal of Mechanical Sciences, 2018, 138‒139 : 262– 271

[40] Wan M, Dang X, Zhang W. Optimization and improvement of stable processing condition by attaching additional masses for milling of thin-walled workpiece. Mechanical Systems and Signal Processing, 2018, 103 : 196– 215

[41] Shi J, Song Q, Liu Z. A novel stability prediction approach for thin-walled component milling considering material removing process. Chinese Journal of Aeronautics, 2017, 30( 5): 1789– 1798

[42] Yao Z, Luo M, Mei J. Position dependent vibration evaluation in milling of thin-walled part based on single-point monitoring. Measurement, 2021, 171 : 108810–

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 52022082 and 52005413), and the 111 Project (Grant No. B13044).

RIGHTS & PERMISSIONS

2021 Higher Education Press 2021.