Milling Tool Wear Diagnosis by Feed Motor Current Signal

using an Optimized Artificial Neural Network

Abstract

In this paper, Multi-Layer Perceptron (MLP) neural network have been used for prediction of tool wear

in face milling. For this purpose, a series of experiment were conducted in a milling machine on ck45

workpiece. Tool wear was measured by an optical microscope. To improve accuracy and reliability of the

designed tool condition monitoring system, tool wear state were categorized into five groups namely: no

wear, slight wear, normal wear, severe wear and broken tool. Experiments with aforementioned tool wear

category and different machining conditions were conducted and data were extracted. It was observed that

there was an increase in current amplitude with increasing tool wear. Also effects of parameters such as

tool wear, feed and depth of cut on motor current consumption is analyzed. Due to the complexity of the

problem of classifying tool wear state intelligently, an optimized multi-layer neural network was used.

RMS of motor current, feed rate, depth of cut, rpm of tool were chosen as inputs and amount of flank wear

as output of MLP. In this paper, a script code in MATLAB was developed to determine structure of a

neural network with best performance, by examining the structure of a large number of neural networks.

Hence the proposed tool wear diagnosis system is optimized. Results show good performance of the

designed tool wear monitoring system.

Keywords: Tool wear, Milling, motor current, multi-layer neural networks.

1. Introduction

The milling process is one of the important machining

processes besides turning. In the milling process the

chips are removed by feeding a workpiece past a

rotating multiple tooth cutter. It is a complex process,

where analytical models cannot give the best accurate

results in modeling.

Traditional methods like statistical regression and

response surface methodology approaches have been

used by some researchers in modeling the milling

process. But these methods cannot overcome the

nonlinearity of relationships between cutting

conditions and the output response.

Artificial neural network models are able to solve these

problems encountered in the machining process

through its massive parallelization to solve complex

nonlinear problems. These networks are able to solve

many problems in many fields such as pattern

recognition, classification, prediction, optimization

and control systems [1]. Information of various sensors

such as thrust force, torque, acoustic emission, current,

power, vibration individually or in combination with other sensors signals can be used as input to a tool

condition monitoring system using artificial neural

networks. Signal information is extracted through

signal processing. Among the various sensors used,

current sensor is considered one of the most effective

means of monitoring tool wear and adaptive control of

machining processes. The advantage of using current

sensors, which are mounted to the external power

supply lines, is that these sensors do not hinder the

machining process and are cost effective.

They can also provide the foundation for on-line tool

condition monitoring [2]. Therefore, in this study it

was decided that the current sensor is used to monitor

the status of the tool wear.

Jacob and joseph chen [3] applied neural network on

cutting force signals with cutting condition to detect

tool wear in the milling operation. They used average

cutting force and cutting condition (spindle speed, feed

rate and depth of cut) as the input and tool wear as

output of neural network. Natarajan et al [4] estimated

tool life by using neural network. Speed, feed, depth of

cut and flank wear were taken as input parameters and

tool life as an output parameter. They used particle

swarm optimization instead of a back-propagation

algorithm. They found that the computational time is

greatly reduced by this method. Dutta et al [5]

predicted tool wear with the help of a multilayer neural

network in the milling. Cutting force and vibration

features were taken as input parameters and tool life as

an output parameter. Kaya et al [6] applied neural

network to predict tool wear in the milling. They used

cutting condition, machining time, root mean square of

torque and cutting force as the input and tool wear is

used as output of neural network.

Very few studies were performed to monitor tool in

milling using Current Sensor. Gosh et al [7] applied

Combining data from multiple sensors: cutting force,

vibration, sound and spindle current to monitor tool

wear in the milling with neural network. Mostafaour

asl and razfar [8] Used an ammeter to obtains spindle

motor current data. With the aid of artificial neural

network, tool wear were predicted in milling operation

and categorized in two state namely: good condition

and worn tool.

In this study, to increase accuracy and reliability, tool

wear status were classified into five states (no wear,

slight wear (0.15 mm), normal wear (0.3 mm), severe

wear (0.5 mm) and broken (0.8 mm)). Then machining

has been done with aforementioned tool wear and

different cutting conditions. Also effects of parameters

such as tool wear, feed rate and depth of cut on motor

current consumption is analyzed. Because tool wear is

correlated with root mean square of motor current.

RMS of motor current, feed rate, depth of cut, spindle

speed were chosen as inputs and amount of flank wear

as output of multi-layer perceptron. A script code were

developed in MATLAB software, to evaluate the

performance of a large number of neural network

structure and the best neural network structure is

selected.

2. Experimental setup

In this study, the relationship between the tool wear

and feed motor current was investigated during face

milling. For this purpose, a series of experiments were

conducted in a vertical milling machine. All

experimental were carried out by using TPGN 16 03

08 inserts clamped on a tool holder with diameter of

50 mm. Where T represents a triangular shaped tool, P

represents 11- degree for clearance angle, G,

represents the dimensional tolerances of insert and N

indicates no hole is on the insert. Also 16 is length of

the cutting edge, 03 shows the insert thickness and 08

represent the tool tip radius. This insert will be used for

rough and finish machining. The work piece material

was a block of CK45 steel. Machining was carried out

under different cutting condition and various tool

wears. Details of work piece and tooling material were

given in Table 1.

Table1. Details of the work, Tool and machining

parameters.

Tool wear was measured by an optical microscope

with accuracy of 0.005 mm. the experiments were

conducted with various wear (no wear (new), slight

wear (0.15 mm), normal wear (0.3 mm), severe wear

(0.5) and broken (0.8 mm)) tools under different

cutting condition. Different tool wear is shown in fig.

1.

Fig. 1. Type of tool wear state in the tests

Then with each wear under different machining

conditions by changing one parameter and keeping the

other parameters, data were taken. The experimental

set up is shown in fig. 2.

Vertical milling machine Machine Type

SANDVIK TPGN 16 03 08 Tool type

315-500-630-800-1000 Spindle speed(rpm)

63-100-160-200-250 Feed rate(mm/min)

0-0.25-0.5-0.75-1-1.25-1.5-2 Depth of cut(mm)

0-0.15-0.3-0.5-0.8 Tool wear rate(mm)

50 Tool diameter(mm)

1 Number of tooth

Without coolant Coolant

Ck45(3005085) Workpiece(mm)

500 Sampling Frequency

Fig. 2. Schematic of excremental setup

3. The measured feed motor current consumption

An electronic circuit was designed and constructed to

measure current consumption. This device is capable

of measuring current consumption up to 5 A with

accuracy of 0.01 ampere. The current passes from

basis of the IP + and IP- and is converted to a voltage

in VIOUT7.

The functions of C18, C19, and C20 capacitors are

filtering and noise removal. Finally by lm358 that

performs buffering, data are transferred to the

microcontroller. Calculations are performed in the

microcontroller. To communicate with Computer, a

MAX232 interface IC is used. This circuit is connected

in series to the feed motor and 500 samples are taken

per second that is seen in Figs. 3 and 4.

Fig. 3. Circuit current measurement

Fig. 4. Components of the measuring circuit

4. Results of experimental tests

Examples of signals obtained in the time domain for a

new tool and broken tool is shown in Figs. 5 and 6. It

is observed that with increasing tool wear, the motor

current consumption increases.

Fig. 5. Feed motor current signal for new tool

Fig. 6. Feed motor current signal for broken tool

Fig. 7. Change of the current amplitude with the tool

wear increasing

Also the effects of various individual factors (such as

tool wear and cutting conditions) on the feed motor

current are investigated. The effect of each factor is

tested, keeping all other factors constant. Fig. 8 shows

that the RMS value of the motor current signal

increases with increase in feed-rate and tool wear when

spindle speed is 400 rpm and depth of cut 1 mm. Depth

of cut has also the similar effect on the current.

Fig. 8. Effect of feed rate on feed motor current

Fig. 9 shows the effect of depth of cut and tool wears

on the amplitude of the motor current signal while

spindle speed was 500 rpm and feed rate was 100

mm/min. The reason for increment of motor current is

that in both cases cutting force increases.

0 50 100 150 200 250-2000

-1500

-1000

-500

0

500

1000

1500

2000

0 50 100 150 200 250

-2000

-1000

0

1000

2000

Time (m s)

Cu

rren

t (m

A)

Cu

rren

t (m

A)

Time (m s)

RM

S c

urr

ent

(A)

New

Slight Normal

Severe

Broken

Feed rate (mm)

Fig. 9. Effect of depth of cut on feed motor current

5. Neural networks for tool wear prediction

Inspired by biological neural networks, artificial neural

networks used for solving engineering problems in

different fields such as automatic control, condition

monitoring and model identification [9]. The most

common type of neural network is multilayer

perceptron, which is applied for more than 90% of

condition monitoring cases [10]. Hence, in this study

we have used multilayer perceptron neural network.

In this paper, the performance of the neural network is

determined by the mean square error. MSE is achieved

by sum of square errors of each neuron divided by the

number of neurons in the output layer, Equation 1

shows how the mean square error is achieved.

=1

(

=1 )

2 =1

(

=1 )

2 (1)

Where and are th actual output and target respectively. And is the number of neurons in the output layer. Before starting the design of neural networks and the use of data for

learning, network, two steps must be taken. Data need to be

preprocessed. And also needs to be divided into several

categories. If pre-processing steps were performed on the input data and the target, efficiency of neural network will be

increased. Normalization is the first process of data

preprocessing. Generally, normalization process should be

done for both input and target vectors [11]. In the second step

the acquired data will be divided into three categories

namely: training, validation and test. Learning category is used for calculation of error gradient and updating weights

and bias. Validation category is used for learning process. As

usual, at first validation error data set will decrease.

However, when the network starts over fitting, validation

error data set will typically start to grow, at this time, the

training should be stopped to avoid over fitting. Network

weights and bias are stored when validation error data is

lowest. Test data have no effect on the neural network

training. And is used to determine efficiency of the neural

network after training.

The data of 86 experiments conducted, randomly were

divided into three category. So, training, validation and test

data were 60, 10 and 16 respectively. In this study, root mean

square of current feed rate, spindle speed and depth of

cut were used as input and tool wear states as output,

with five output vectors, without wear, slight wear,

normal wear, severe wear and broken tool, which were

represented as the following vectors [1 0 0 0 0], [0 1 0

0 0], [0 0 1 0 0], [0 0 0 1 0] and [0 0 0 0 1] respectively.

In this research fulfillment of one of the following

criteria is sufficient for stopping the training algorithm

of the proposed neural network:

1) Exceeding number of 1000 epochs.

2) Fifteen consecutive increase in mean square error

of validation data set.

3) Gradient decrement of less than 1e-15.

To obtain the optimized structure of the Neural

Network a script code in MATLAB software has been

developed. The developed code was capable of

determining the best neural network among networks

with one or two hidden layer and networks with

different transfer function such as linear, hyperbolic

tangent, sigmoid and logsigmoid.

RM

S c

urr

ent

(A)

New

Slight

Normal

Severe

Broken

Depth of cut (mm)

Also the code has the ability of determining the best

number of neurons in the hidden layer.

Accordingly, 9846 neural network was assessed. Each

of the network structures trained thirty times with

random weights.

And efficiency of different structures were determined

by averaging the mean squared error of validation data

in thirty training. Finally, Structure and learning

function was selected. The average minimum mean

squared error on the thirty training. Fig. 10 Schematic

structure of the neural network is chosen to show

estimated wear in this paper.

Structure of the best neural network is 4: 12: 2: 5 in

which sigmoid and hyperbolic tangent function were

used in first hidden layer and in the second hidden

layer logsigmoid transfer function was used. Levnberg

- Marquardt learning algorithm was used for learning.

Mean and mean square error of training and validation

data were 0.11806 and 0.11893 respectively.

Fig. 10. Schematic structure of proposed neural network model

After determining the best structure of the neural

network, the best weights for the network were chosen.

Fig. 11 shows variation of error during the learning

process. After 15 epochs in which validation error did

not decrease, training would be stopped and weights

and bias are stored. In this state the network has the

most generalization property.

The training and validation mean square error of this

optimum neural network are 0.03126 and 0.07780

respectively. The classification accuracy of neural

network training and validation data were 98/3 and

90%, respectively, which is shown in Figs. 12 and 13.

Fig. 11. Error change during learning process. Circle

represents when the network has the highest

generalization.

RMS current

Depth of cut

Feed rate

Spindle speed

Normal wear

Severe wear

Slight wear

New tool

Broken tool

Mea

n S

qu

are

Err

or

(MS

E)

Epochs

Fig.12. Confusion Matrix for Training data set. (1: New,

2: Slight, 3: Normal, 4: Severe, 5: Broken)

Fig.13. Confusion Matrix for Validation data set. (1:

New, 2: Slight, 3: Normal, 4: Severe, 5: Broken)

6. Neural network results evaluation

Sixteen test data set were used for evaluation of the

designed neural network. The mean square error on

test data was 0.0917. As is depicted on fig. 12 the

classification accuracy was 93.8% and all different

fault conditions were classified correctly except one.

Fig.14. confusion matrix for test data set. (1: New,

2: Slight, 3: Normal, 4: Severe, 5: Broken)

7. Results

Proper use of intelligent methods for tool wear

detection is necessary for tool condition monitoring.

To increase classification accuracy and reliability five

milling tool wear categories were considered in this

research. It was shown that there was an increase in

motor current amplitude with increasing tool wear. By

using feed motor current RMS, spindle speed, feed

rate, depth of cut as input and tool wear state as output

of neural network, cutting tool wear states can be

classified in a wide range of different cutting

conditions with high accuracy. A code script in

MATLAB software have been developed to evaluate

the performance of a large number of neural network

structures. Finally, a multi-layer neural network is

designed with structure 4: 12: 2: 5, the optimized

neural network was capable of separating different tool

wear state with 93.8% accuracy. This method can be

used as a suitable method for smart classification of

milling tool wear state.

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