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Prediction and optimization of machining results and parameters in drilling by using Bayesian networks

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Abstract

Tool wear and borehole quality are two critical issues for high precision drilling processes. In this paper, several drilling experiments in terms of different drilling parameters and drill bit with and without coating are conducted according to the Taguchi orthogonal arrays. Thrust force and moment were measured during the drilling process. The cutting edge radius depending on the wear, roughness and roundness of the borehole were also aquired. By combining the experiment dataset with the expert knowledge, a Bayesian prediction network of tool wear radius, surface roughness and borehole roundness is established through structure learning and parameter learning algorithms based on GeNIe, a disposable software to create Bayesian networks. Up to \(89\,\%\) accuracy were achieved using this approach. The research described in this paper can provide a new approach to multivariate prediction and parameter optimization in drilling.

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Abbreviations

d :

Diameter in mm

f :

Feed rate in mm/rev

r e :

Cutting edge radius in \(\mu \)m

v c :

Cutting speed in m/min

F z :

Thrust force in N

M c :

Drilling moment in Ncm

R :

Roundness in \(\mu \)m

Ra :

Roughness in \(\mu \)m

\(\theta \) :

Probability

\(\textit{L}(\theta )\) :

Likelyhood

AI:

Artificial intelligence

ANN:

Artificial neural network

ANOVA:

Analysis for variance

BDe:

Bayesian Dirichlet equivalence

BN:

Bayesian network

BPNN:

Backpropagation neural network

BS:

Bayesian search

CPT:

Conditional probability table

DAG:

Directed acyclic graph

EM:

Expectations maximum

GA:

Generic algorithm

HC:

Hill climbing g algorithm

MLPNN:

Multilayer perceptions neural network

NB:

Naive Bayes

RBFN:

Radial basis function network

RBFNN:

Radial basis function neural network

RSM:

Response surface methodology

TAN:

Tree-augmented Naive Bayes

TCP:

Tool center point

WPT:

Wavelet packet transform

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Acknowledgements

This work was supported by Talent Training Program of the Ministry of Education and State Administration of Foreign Experts Affairs of China  (No. P173008021) and the colleagues at the Institute for Machine Tools  (IfW) of the University of Stuttgart, Germany. The authors would like to acknowledge the supports.

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Authors and Affiliations

  1. School of Automotive Engineering, Hubei Key Laboratory of Adv. Tech. for Automotive Components, Wuhan University of Technology, No. 205 Luoshi Road, Wuhan, 430070, Hubei, China

    X. Wang

  2. Institute for Machine Tools (IfW), University of Stuttgart, Holzgartenstrasse 17, 70174, Stuttgart, Germany

    R. Eisseler & H.-C. Moehring

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  1. X. Wang
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  2. R. Eisseler
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  3. H.-C. Moehring
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Correspondence to R. Eisseler.

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Wang, X., Eisseler, R. & Moehring, HC. Prediction and optimization of machining results and parameters in drilling by using Bayesian networks. Prod. Eng. Res. Devel. 14, 373–383 (2020). https://doi.org/10.1007/s11740-020-00965-w

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  • DOI: https://doi.org/10.1007/s11740-020-00965-w

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