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.
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Abbreviations
- ANN:
-
Artificial neural network
- ARM:
-
Arithmetic mean
- CNC:
-
Computer numerical control
- FA:
-
Fuzzy algorithm
- GA:
-
Genetic algorithm
- MIC:
-
Mutual information coefficient
- RMS:
-
Root mean square
- RA:
-
Regression analysis
- STD:
-
Standard deviation
- SVM:
-
Support vector machine
- TA:
-
Taguchi analysis
- Var:
-
Variance
- a k :
-
Elements of acceleration feature matrix
- a max :
-
Maximum of the fitting function g(l)
- a(l):
-
Monitored acceleration (g)
- a′(l):
-
Compensated acceleration at any position (g)
- A :
-
Acceleration feature matrix
- b :
-
Undetermined constant
- b 0 :
-
b0 = lg ψ
- \({{\hat b}_0}\) and \({\hat b}\) :
-
Regression coefficients
- C(l):
-
Compensation coefficient
- g(l):
-
Fitting function between vibration attenuation and distance (g)
- H :
-
Information entropy
- J :
-
Number of elements for surface roughness feature matrix
- K :
-
Number of elements for a certain acceleration feature matrix
- l :
-
Milling position
- ln :
-
Evaluation length (mm)
- lr :
-
Sampling length (mm)
- m :
-
Number sample data
- n :
-
Number of measuring points within the sampling length
- P(a k) and P(ra j):
-
Probabilities of ak and raj in features Ai and RA
- P(a k, ra j):
-
Joint distribution probability of ak and raj
- ra j :
-
Elements of surface roughness feature matrix (µm)
- Ra(l):
-
Surface roughness (µm)
- RA :
-
Surface roughness feature matrix
- x :
-
x = lga′(l)
- X :
-
Coefficient matrix of xi
- y :
-
y = lg Ra(l)
- ŷ:
-
Statistical variable
- Y :
-
Coefficient matrix of yi
- z(x) and z i :
-
Ordinate value from each point on the assessed contour line to midline (µm)
- α :
-
Coefficient matrix of undetermined constant b
- γ i :
-
Independent sample values
- \(\bar \gamma \) :
-
Arithmetic mean of sample values
- ε i :
-
Random error
- ε :
-
Random error matrix of εi
- ν i :
-
Residual error of sample values
- σ :
-
Standard deviation
- ψ :
-
Coefficient of cutting conditions and material
References
Urbikain Pelayo G, Olvera-Trejo D, Luo M, et al. Surface roughness prediction with new barrel-shape mills considering runout: modelling and validation. Measurement, 2021, 173: 108670
Sun W, Yao B, Chen B, et al. 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
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
Renaudin L, Bonnardot F, Musy O, et al. Natural roller bearing fault detection by angular measurement of true instantaneous angular speed. Mechanical Systems and Signal Processing, 2010, 24(7): 1998–2011
Kong D, Zhu J, Duan C, et al. Bayesian linear regression for surface roughness prediction. Mechanical Systems and Signal Processing, 2020, 142: 106770
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
Hessainia Z, Belbah A, Yallese M A, et al. On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations. Measurement, 2013, 46(5): 1671–1681
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
Sun W, Zhang D, Luo M. Machining process monitoring and application: a review. Journal of Advanced Manufacturing Science and Technology, 2021, 1(2): 2021001
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
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
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
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
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
Chang H K, Kim J H, Kim I H, et al. In-process surface roughness prediction using displacement signals from spindle motion. International Journal of Machine Tools and Manufacture, 2007, 47(6): 1021–1026
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
Salgado D R, Alonso F J, Cambero I, et al. In-process surface roughness prediction system using cutting vibrations in turning. International Journal of Advanced Manufacturing Technology, 2009, 43(1–2): 40–51
Quintana Q, Rudolf T, Ciurana J, et al. 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
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
ISO 4287: Geometrical Product Specifications (GPS)—Surface Texture: Profile Method—Terms, Definitions and Surface Texture Parameters, 1997
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
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
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
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
Chen Y, Li H, Hou L, et al. Feature extraction using dominant frequency bands and time-frequency image analysis for chatter detection in milling. Precision Engineering, 2019, 56: 235–245
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
Lamraoui M, Barakat M, Thomas M, et al. Chatter detection in milling machines by neural network classification and feature selection. Journal of Vibration and Control, 2015, 21(7): 1251–1266
Xue L, Li N, Lei Y, et al. Incipient fault detection for rolling element bearings under varying speed conditions. Materials (Basel), 2017, 10(6): 675–690
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
Nguyen D, Yin S, Tang Q, et al. 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
Agrawal A, Goel S, Rashid W B, et al. Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC). Applied Soft Computing, 2015, 30: 279–286
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
Zhang N, Shetty D. An effective LS-SVM-based approach for surface roughness prediction in machined surfaces. Neurocomputing, 2016, 198: 35–39
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
Ho W, Tsai J, Lin B, et al. 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
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
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
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
Zhang Z, Li H, Liu X, et al. Chatter mitigation for the milling of thin-walled workpiece. International Journal of Mechanical Sciences, 2018, 138–139: 262–271
Wan M, Dang X, Zhang W, et al. 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
Shi J, Song Q, Liu Z, et al. A novel stability prediction approach for thin-walled component milling considering material removing process. Chinese Journal of Aeronautics, 2017, 30(5): 1789–1798
Yao Z, Luo M, Mei J, et al. 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).
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Yao, Z., Fan, C., Zhang, Z. et al. Position-varying surface roughness prediction method considering compensated acceleration in milling of thin-walled workpiece. Front. Mech. Eng. 16, 855–867 (2021). https://doi.org/10.1007/s11465-021-0649-z
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DOI: https://doi.org/10.1007/s11465-021-0649-z