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CHAPTER 9

CONCLUSION AND SCOPE FOR FUTURE WORK

9.1 INTRODUCTION

The studies on tool wear classification and prediction of surface roughness in turning

using machine learning approach consisted of two phases. The first phase sets out to find

the best feature-classifier combination suitable for tool wear classification. The second

phase assesses the predictability of the surface roughness by different regression

techniques. In either of the cases, the data consisted of features extracted from the

vibration signals acquired during the turning operation. This forms the rationale of the

thesis.

Machine learning approach was used in both the phases to extract features and select the

prominent features from the extracted feature set. In the first phase of the study, three

different features were tried out with various classifiers and the best combination that

gives the highest classification accuracy in the supervised learning environment was

found. This study contributes towards finding the status of tool wear in automated

manufacturing.

The second phase of the study discusses the different algorithms that may be used to

predict the surface roughness and finds the most appropriate one, based on the results of

RMSE (root mean square error) values. Surface roughness prediction study was carried

out for carbide and coated carbide tipped tool inserts. A comparison of results obtained

between the coated and non-coated carbide is carried out to find the effect of coating on

the surface roughness. Such studies are relevant to monitor remote machines and

processes, especially when the conditions of machine tools are diagnosed remotely and

over the internet.

This study helps to optimise the tool insert change time and can evolve a mechanised and

automated signalling to the operator i.e. it may be used to signal at appropriate time to

change the tool insert for a preset range of surface roughness either by “glowing a light

indicator” or “a beep sound”. Online prediction and diagnostics can lead to providing an

“integrated circuit chip” with the use of embedded technology once the feature-classifier

combination has been established.

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9.2 CONCLUSIONS BASED ON TOOL WEAR CLASSIFICATION

Here, different sets of features were extracted from the time-domain signal obtained

during the experiment. The experiment consisted of collecting the vibration signals for

different tool wear states and classifying them. The four classes were, Good, Tool Blunt

low (TB1), Tool Blunt high (TB2) and Tool tip loose (TTL). C4.5 algorithm was used as

it is simple and good in feature selection. The various kinds of features that were

considered for study are

Statistical Features

Histogram Features

Discrete Wavelet Transform Features

The significance of the first two features was brought out in Chapter 5 and the third was

discussed in Chapter 6.

Feature selection:

The comparative study between ID3 algorithm and PCA on feature reduction using

statistical features showed that ID3 is better than PCA in feature reduction.

From the results and discussion in section 5.2 we find that among the 11 statistical

features that were considered, only two features , namely,. Standard Deviation and

Kurtosis contribute towards enhancement of classification accuracy while keeping the

computational effort low.

Similarly, among the twenty histogram features that were defined, only four features

namely, h9, h10, h13 and h14 were found to be contributing to the classification

accuracy. (refer section 5.2).

Among DWT-energy wavelet features, the SVM classifier determined that only four

features viz. V5, V6, V7 and V9 were found to be contributing to the classification

accuracy (refer section 6.3).

Among DWT-entropy features where, four features, namely V2, V3, V4 and V6 were

selected using decision tree.

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Feature Classification

With reference to Table 9.1, the following conclusions may be obtained:

Among statistical features ν-SVM with kernel function as ‘Polynomial Kernel’ with

‘degree 2’ has the highest classification accuracy (88.06%).

ANN has 88.05% classification accuracy.

Among histogram features C-SVM with kernel function as ‘Polynomial Kernel’ with

‘degree 2’ has the highest classification accuracy (88.61%).

Statistical features are consistently high among all classifiers and are more reliable

Table 9.1 Consolidated classification accuracy of time-domain features

Classifier/Accuracy

(%)ANN C4.5 Fuzzy Naïve

Bayes

Bayes

NetC-SVM Nu-

SVMStatistical Features 88.05 87.5 81.38 85.27 86.38 87.50

(Poly2)

88.06

(Poly2)Histogram Features 78.05 76.66 77 66.66 75 88.61

(RBF)

63.33

(Poly3)

Fig 9.1 Feature-Classifier comparison – summary

0

10

20

30

40

50

60

70

80

90

100

ANN C4.5 Fuzzy Naïve Bayes Bayes Net C-SVM ν-SVM

Cla

ssifi

catio

n A

ccur

acy

%

Classifiers

Feature - Classifier comparison Statistical Features

Histogram Features

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Study using wavelet features:

Study was to find which wavelet amongst the various types of wavelets along with

their respective child wavelets (a total of 52 wavelets) is best suited for condition

monitoring of single point cutting tool.

Two different definitions of DWT features (DWT-energy and DWT-entropy) were

taken and a better one among them was found.

The wavelets considered in the present study were Coiflet wavelets, Daubechies

wavelets, bi-orthogonal wavelet, reverse bi-orthogonal wavelet, symlet wavelet (along

with their child wavelets), and Meyer wavelet.

For each family, the wavelet which gave highest classification accuracy was found as

for DWT-energy features, bior2.2 and for, DWT-entropy features bior1.1.

Further, the classification accuracy of these two features, in combination with

different classifiers viz., C4.5 algorithm, SVM, Naïve Bayes, and Bayes net were

studied as shown in Table 9.2 and ‘bior2.2’ wavelets and SVM classifier was found

the best.

Table 9.2 Consolidated results of wavelet features

WaveletFamily

% Accuracy (J48)% Accuracy

(SVM)% Accuracy

(Naive Bayes)% Accuracy(Bayes Net)

DWT-energy

DWT-entropy

DWT-energy

DWT-entropy

DWT-energy

DWT-entropy

DWT-energy

DWT-entropy

sym6 66 80.28 97.25 47.92 87.75 45.42 92.5 48.89

rbio5.5 64.5 80.83 97.5 41.39 87 40.14 91 42.64

db9 66 82.08 97 40.97 88.25 41.81 93 44.17

coif2 62.25 83.89 97.5 44.86 86.5 43.06 91 43.75

bior2.2 80.5 82.50 97.75 44.58 89.75 38.33 92 44.86

dmey 25.28 81.94 95.97 46.94 90.83 40.97 85.86 50.97

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Since J48 is a decision tree based classifier based on the concept of information

entropy, higher classification accuracy for DWT-entropy feature is found.

For all other classifiers, it can be observed that DWT-energy is a much better feature

as DWT-entropy gives much lower classification accuracy.

Thus, the combination of bior2.2 wavelets with DWT-energy as feature definition and

SVM as the classifier is found to be the most suited for the present study.

9.3 CONCLUSIONS BASED ON PREDICTION OF SURFACE ROUGHNESS

Online prediction of surface roughness has been attempted in many ways and by using

different parameters like, cutting tool parameter variations, geometry variations, coating

of inserts with different materials, study using different signals like, acoustic signals,

vibration signals etc. From the literature study, it was found that prediction of surface

roughness using cutting parameters and mean value of the amplitude of the vibration

signals are generally studied. However, a machine learning approach to using the

statistical features extracted from vibration signals, along with the other cutting

parameters and flank wear have not been attempted by researchers, especially in a single

point cutting tool environment. This has been examined in this study.

It has been established by this study that surface roughness could be predicted by

using statistical features extracted from the vibration signals acquired during turning.

The present study has generated surface roughness prediction models.

It uses the statistical features and the corresponding flank wear to give the prediction

models when the other cutting parameters remain constant.

The study has also been extended to compare the RMSE (Root mean square error)

values of the regression results using three techniques viz., multiple regression,

support vector regression and radial basis network function.

As discussed in Chapter 8, referring to Fig. 8.13 and 8.14 we find that the MLR

results have relatively lower RMSE values than the predicted values of SVR and

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RBF. This is true for both carbide and coated carbide tipped tool. However, taking the

computational time and ease of prediction it is found that SVR gives an acceptable

result. Hence, it is recommended for online prediction of surface roughness.

27 models consisting of only the statistical features of vibration signal have been

formed for different variations in the cutting parameters and flank wear of carbide

tipped tool to be integrated in the prediction system. The same has been formulated

for coated carbide tipped tool also.

The RMSE values of coated and non-coated carbide tipped tool was determined and

found that the variation between them was minimal (Referring to the Fig 8.16 in

Chapter 8). It was observed that, when the flank wear is generated in the tool, the

effect of coating is minimal and the tool behaves as non-coated tool.

The contributions of every statistical feature vary towards the predicting regression

equation. As expected the R-Sq is the highest (80.8%) when all the features were

considered, but the computational time being the highest. The feature reduction using

PCA was carried out and only ‘kurtosis’ and ‘range’ were the features found to be

significantly contributing to the R_Sq value of 74.9%. The RMSE value is the lowest

when PCA was considered. The results are encouraging than when only ‘mean’ or

when the first four moments of the statistical features are considered.

Referring to Table 8.14 a final conclusion is reached as to which technique is most

suitable for prediction of surface roughness. SVR models show significantly high

coefficients of determination compared to MLR and RBF models.

This conclusion is reached with only three techniques and on applying further

techniques the results and conclusions may vary. Further techniques can be applied to

improve the prediction models and reliability of the same.

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9.4 SUGGESTIONS FOR FUTURE SCOPE OF WORK

During the experimental study, in order to create the different tool wear conditions, it

was decided to have 0.3 mm as tool blunt low and 0.6 mm as tool blunt high. The

intention was to create two representative classes. This study can be extended to

continuous monitoring of the progressive wear of the tool.

The study has been carried out using C 4.5 algorithm for dimensionality reduction and

feature selection. However, there are many other ways like PCA, Linear discriminant

analysis, factor analysis, fisher’s linear discriminant analysis, feature subset selection,

Minimum Description Length Method, Probability of Error and Average Correlation

Coefficient method, Koller and Sahami’s method etc., that can be tried out.

Statistical features, histogram features and wavelet features have been tried out in this

study. Other types of features like frequency domain features, poly spectral (higher

order spectral features, etc., can be extracted and studied.

DWT features were considered here. The study can be extended to wavelet packet

features and second generation wavelet transform features.

Wavelet basis selection can also be done using maximum energy and minimum

entropy criteria.

Many other algorithms or classification could be attempted like, Proximal Support

Vector Machine, Artificial Immune System etc.

Here, the surface roughness prediction was carried out using multiple regression,

support vector regression and radial basis network function. The study can be

extended to non-linear regression, Principle component regression, PLS, using Neural

Network, Hidden Markov Method etc.