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An image-based approach to predict instantaneous cutting forces using convolutional neural networks in end milling operation

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Abstract

Cutting force detection can contribute to predicting the productivity and quality of end milling operations. Instantaneous cutting force prediction of digital twins in end milling operations should be near real-time and accurate. This paper proposes an image-based approach that can contain more useful information due to a higher dimension and simplify the complexity of computing geometric data. The cutter frame image (CFI) is utilized as one of the inputs of a convolutional neural network (CNN) to predict instantaneous cutting forces. Considering the convenience of capturing massive data, the approach uses cutting forces generated from a mechanistic force model instead of experimental cutting forces to train the CNN. The correlation coefficient R2 value between predicted results and simulated results is 0.9999 and the average time cost per image is 0.057 s in a cutting condition, which validates the possibility to use the image-based method to predict instantaneous cutting forces accurately and efficiently in the digital twin.

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Abbreviations

NN :

Neural network

ANN :

Artificial neural network

CNN :

Convolutional neural network

CWEs :

Cutter-workpiece engagements

CFI :

Cutter frame image

FEM :

Finite element method

AABB :

Axis-aligned bounding box

ADOC :

Axial depth of cutting

RDOC :

Radial depth of cutting

MSE :

Mean squared error

RMSE :

Root mean squared error

R 2 :

Correlation coefficient

SGD :

Stochastic gradient descent

Adam :

Adaptive moment estimation

φ(i,j,k):

Immersion angle

φ st :

Reference immersion angle

Ω :

Spindle speed

φ p :

Pitch angle between each tooth

ψ p :

Lag angle

dF t :

Differential tangential cutting force

dF r :

Differential radial cutting force

dF a :

Differential axial cutting force

K tc :

Tangential shear force coefficient

K rc :

Radial shear force coefficient

K ac :

Axial shear force coefficient

K te :

Tangential edge force coefficient

K re :

Radial edge force coefficient

K ae :

Axial edge force coefficient

h :

Instantaneous cutting thickness

dz :

Depth of the infinitesimal flute segment

ds :

Length of the infinitesimal flute segment

S :

Initial width of the CFI

T :

Initial height of the CFI

W :

Final width and height of the CFI

λ :

Resolution of the CFI

β 0 :

Helix angle

c :

Feed rate

ρ j :

jth cutting tool position

ρ j+ 1 :

j + 1th cutting tool position

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Funding

This work was supported by the Ministry of Industry and Information Technology of the People’s Republic of China.

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Authors and Affiliations

  1. School of Mechanical Engineering & Automation, Beihang University, Beijing, 100191, China

    Shuo Su, Gang Zhao, Wenlei Xiao, Yiqing Yang & Xian Cao

  2. MIIT Key Laboratory of Aeronautics Intelligent Manufacturing, Beihang University, Beijing, 100191, China

    Gang Zhao, Wenlei Xiao & Yiqing Yang

Authors
  1. Shuo Su
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  2. Gang Zhao
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  3. Wenlei Xiao
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  4. Yiqing Yang
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  5. Xian Cao
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Correspondence to Wenlei Xiao.

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Su, S., Zhao, G., Xiao, W. et al. An image-based approach to predict instantaneous cutting forces using convolutional neural networks in end milling operation. Int J Adv Manuf Technol 115, 1657–1669 (2021). https://doi.org/10.1007/s00170-021-07156-6

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  • DOI: https://doi.org/10.1007/s00170-021-07156-6

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