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In-process identification of milling parameters based on digital twin driven intelligent algorithm

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Abstract

The potential benefits of Industry 4.0 have led to an increased interest in smart manufacturing. To facilitate the self-diagnosis and adaptive ability in smart milling system, a digital twin–driven intelligent algorithm for monitoring in-process milling parameters is proposed here. The algorithm can extract the radial width of cut, axial depth of cut, cutter runout parameters, and cutting constants in the end milling process at the same time only by using force sensor. It is an important breakthrough in this paper to converge two different force models to realize cyber-physical fusion for identifying milling parameters in the milling process. By using the convolution force model, digital twin technology can extract the approximate solution of milling parameters in the machining process in advance, so as to narrow the range of solution. Furthermore, the subsequent artificial intelligence algorithm can find the accurate solution of the current milling parameters in a short calculation time by cyber-physical fusion with the numerical force model considering cutter runout effect. Milling experiments are carried out to validate the proposed algorithm. It is shown that due to the complementary advantages of the convolution force model and numerical force model, the algorithm proposed in this paper can give consider to the identification accuracy and calculation efficiency.

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Abbreviations

A :

Vectors of the Fourier series coefficients for the milling force

α, N, D:

Helix angle, number of cutter flutes and cutter diameter

β a :

Axial angular range of cut

CWD :

Fourier transform of the chip width density function

d a, :

Axial depth of cut

d r :

Radial width of cut

\(\Delta \phi\) :

Phase shift between the starting angle position of force measurement and the origin of force model coordinate

\(\phi\) :

Cutter angular displacement

k t, k r :

Cutting constants of lumped shearing force model in the tangential and radial directions

k ts, k rs :

Shearing force constants in the tangential and radial directions

k tp, k rp :

Ploughing force constants in the tangential and radial directions

λ :

Angular location of the cutter runout

θ :

Angular position of cutting edge at the workpiece

θ 1 , θ 2 :

Cutting angles of entry and exit

ρ :

Magnitude of cutter offset

N z :

Numbers of axial disk element

P 1(Nk), P 2(Nk):

Fourier transforms of local tangential forces per unit width at the tooth passing frequency Nk.

\(t_{c} (i,j,k)\) :

The chip thickness of the ith disk element and the kth flute in the cut at the cutter angular position \(\phi_{j}\)

\(t_{x}\) :

Feed per tooth

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Funding

The authors received financial support from the National Natural Science Foundation of China (Grant No. 51775113) and Fujian Provincial Department of Science and Technology (Grant No. 2022H0024).

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Authors and Affiliations

  1. Fujian Key Laboratory of Intelligent Machining Technology and Equipment, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Charles Ming Zheng, Lu Zhang & Youji Zhan

  2. School of Materials Science and Engineering, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Yongchao Xu

  3. Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan, Republic of China

    Yaw-Hong Kang

Authors
  1. Charles Ming Zheng
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  2. Lu Zhang
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  3. Yaw-Hong Kang
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  4. Youji Zhan
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  5. Yongchao Xu
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Contributions

The name and the contribution of each author are listed below. Dr. Charles Ming Zheng: first author, interpreting data, completing the equation for the identification of milling parameters based on convolution force model. Mr. Lu Zhang: completing the equation for the identification of milling parameters based on numerical force model. Dr. Yaw-Hong Kang: corresponding author, checking the proposed method, editing the manuscript. Dr. Youji Zhan: completing the PSO program for the identification of milling parameters. Dr. Yongchao Xu: drafting, performing the milling experiments.

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Correspondence to Yaw-Hong Kang.

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Zheng, C.M., Zhang, L., Kang, YH. et al. In-process identification of milling parameters based on digital twin driven intelligent algorithm. Int J Adv Manuf Technol 121, 6021–6033 (2022). https://doi.org/10.1007/s00170-022-09685-0

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  • DOI: https://doi.org/10.1007/s00170-022-09685-0

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