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Drilling performance analysis of drill column machine using proposed neural networks

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Abstract

In spite of advanced material cutting technology, there are still some problems due to unpredicted vibrations on horizontal and vertical directions on column drilling machines. This paper presents an investigation for drilling condition of drill column machines performance using proposed neural networks. The investigation is divided into two parts. First, the drill column machine is employed to analyze vibrations with steel and aluminum materials for increased drilling speeds. During the working of the system, some measuring points are indicated to analyses of drilling conditions. Finally, two types of proposed neural networks predictors are used to predict vibration variation for both cases of steel and aluminum materials of drilling systems. The experimental and simulation result is improved that radial basis neural network has superior performance to adapt experimental applications for drill column machines.

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Abbreviations

\(A_{sz}\) :

Cutting face (mm2)

a m :

Measured acceleration value (m/s2)

a n :

Neural networks acceleration value (m/s2)

BPNN:

Back propagation neural network

b :

Chip width per a tooth (mm)

\(c_{f}\) :

Stiffness of the work piece (N/m)

d :

Tool diameter (mm)

E 1 :

Error between experimental and neural network output signals

E 2 :

Propagation error between hidden and input layers

F rz :

Radial force (N)

F sz :

Cutting force (N)

F vz :

Feed force (N)

\(f_{z}\) :

Feed per tooth (mm/rev)

h :

Chip thickness per a tooth (mm)

\(k_{r}\) :

All other influences

k s :

Specific feed force (N/mm2)

k s 1:1 :

Basic specific feed force (N/mm2)

\(k_{\text{w}}\) :

Influence of tool wear

\(l_{b}\) :

Height of chip (mm)

\(m\) :

Influence of material type

n :

Velocity of drill (rpm)

N :

Iteration numbers

n I :

Number of neurons in input layer

n H :

Number of neurons in hidden layer

n O :

Number of neurons in output layer

RBNN:

Radial basis neural network

\(s_{z}\) :

Feed per a tooth (mm/rev)

t :

Time (s)

u :

Feed velocity (mm/min)

\(v\) :

Cutting speed (m/min)

\(W_{ij}\) :

Weights between input and the hidden layers

\(W_{ji}\) :

Weights between hidden and the output layers

\(\alpha\) :

Momentum term

α fv :

Function of the feed velocity

\(\eta\) :

Learning rate

\(\theta\) :

Tool angle (°)

Ø :

Drill angle (°)

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Authors and Affiliations

  1. Mechatronics Engineering Department, Engineering Faculty, Erciyes University, Kayseri, Turkey

    Emir Esim & Şahin Yıldırım

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  1. Emir Esim
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  2. Şahin Yıldırım
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Correspondence to Şahin Yıldırım.

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Esim, E., Yıldırım, Ş. Drilling performance analysis of drill column machine using proposed neural networks. Neural Comput & Applic 28 (Suppl 1), 79–90 (2017). https://doi.org/10.1007/s00521-016-2322-8

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  • DOI: https://doi.org/10.1007/s00521-016-2322-8

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