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

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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

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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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Authors and Affiliations

  1. Key Laboratory of High Performance Manufacturing for Aero Engine (Ministry of Industry and Information Technology), Northwestern Polytechnical University, Xi’an, 710072, China

    Zequan Yao, Chang Fan, Dinghua Zhang & Ming Luo

  2. Engineering Research Center of Advanced Manufacturing Technology for Aero Engine (Ministry of Education), Northwestern Polytechnical University, Xi’an, 710072, China

    Zhao Zhang, Dinghua Zhang & Ming Luo

Authors
  1. Zequan Yao
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  2. Chang Fan
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  3. Zhao Zhang
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  4. Dinghua Zhang
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  5. Ming Luo
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Correspondence to Zhao Zhang or Ming Luo.

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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

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