Machining industry has been evolving toward implementation of automation into the processes for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence of the nonuniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective tool condition monitoring (TCM) system to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process are analyzed by state-of-the-art artificial intelligent techniques, support vector machine (SVM) and convolutional neural network (CNN), to predict the tool wear and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and the hardness variation of the workpiece. This study also involves the comparative analysis between two employed artificial intelligent techniques to evaluate the performance of the model in prediction. The proposed TCM system has shown a high prediction accuracy in detecting the tool wear from the audible sound into the proposed multiclassification wear level in end milling of the nonuniform hardened workpiece.

References

References
1.
Teti
,
R.
,
Jemielniak
,
K.
,
O'Donnell
,
G.
, and
Dornfeld
,
D.
,
2010
, “
Advanced Monitoring of Machining Operations
,”
CIRP Ann. Manuf. Technol.
,
59
(
2
), pp.
717
739
.
2.
Axinte
,
D.
, and
Gindy
,
N.
,
2004
, “
Assessment of the Effectiveness of a Spindle Power Signal for Tool Condition Monitoring in Machining Processes
,”
Int. J. Prod. Res.
,
42
(
13
), pp.
2679
2691
.
3.
Dutta
,
S.
,
Pal
,
S. K.
, and
Sen
,
R.
,
2016
, “
Tool Condition Monitoring in Turning by Applying Machine Vision
,”
ASME J. Manuf. Sci. Eng.
,
138
(
5
), p.
051008
.
4.
Axinte
,
D. A.
,
2006
, “
Approach Into the Use of Probabilistic Neural Networks for Automated Classification of Tool Malfunctions in Broaching
,”
Int. J. Mach. Tools Manuf.
,
46
(
12–13
), pp.
1445
1448
.
5.
Wang
,
W. H.
,
Hong
,
G. S.
,
Wong
,
Y. S.
, and
Zhu
,
K. P.
,
2007
, “
Sensor Fusion for Online Tool Condition Monitoring in Milling
,”
Int. J. Prod. Res.
,
45
(
21
), pp.
5095
5116
.
6.
Lu
,
M.-C.
, and
Kannatey-Asibu
,
E.
,
2002
, “
Analysis of Sound Signal Generation Due to Flank Wear in Turning
,”
ASME J. Manuf. Sci. Eng.
,
124
(
4
), p.
799
.
7.
Kuljanic
,
E.
,
Sortino
,
M.
, and
Totis
,
G.
,
2006
, “
Application of Wavelet Transform of AE Signal for Tool Condition Monitoring in Face Milling
,”
39th CIRP Int. Sem. on Manuf. Systems
, Ljubljana, Slovenia, pp.
39
44
.
8.
Kamarthi
,
S. V.
,
Kumara
,
S. R. T.
, and
Cohen
,
P. H.
,
2000
, “
Flank Wear Estimation in Turning Through Wavelet Representation of Acoustic Emission Signals
,”
ASME J. Manuf. Sci. Eng.
,
122
(
1
), pp.
12
19
.
9.
Obikawa
,
T.
, and
Shinozuka
,
J.
,
2004
, “
Monitoring of Flank Wear of Coated Tools in High Speed Machining With a Neural Network ART2
,”
Int. J. Mach. Tools Manuf.
,
44
(
12–13
), pp.
1311
1318
.
10.
Silva
,
R. G.
,
Reuben
,
R. L.
,
Baker
,
K. J.
, and
Wilcox
,
S. J.
,
1998
, “
Tool Wear Monitoring of Turning Operations by Neural Network and Expert System Classification of a Feature Set Generated From Multiple Sensors
,”
Mech. Syst. Signal Process.
,
12
(
2
), pp.
319
332
.
11.
Yao
,
Y.
,
Fang
,
X. D.
, and
Arndt
,
G.
,
1990
, “
Comprehensive Tool Wear Estimation in Finish-Machining Via Multivariate Time-Series Analysis of 3-D Cutting Forces
,”
CIRP Ann. Manuf. Technol.
,
39
(
1
), pp.
57
60
.
12.
Scheffer
,
C.
,
Kratz
,
H.
,
Heyns
,
P. S.
, and
Klocke
,
F.
,
2003
, “
Development of a Tool Wear-Monitoring System for Hard Turning
,”
Int. J. Mach. Tools Manuf.
,
43
(
10
), pp.
973
985
.
13.
Dinakaran
,
D.
,
Sampathkumar
,
S.
, and
Sivashanmugam
,
N.
,
2009
, “
An Experimental Investigation on Monitoring of Crater Wear in Turning Using Ultrasonic Technique
,”
Int. J. Mach. Tools Manuf.
,
49
(
15
), pp.
1234
37
.
14.
Shahabi
,
H. H.
, and
Ratnam
,
M. M.
,
2010
, “
In-Cycle Detection of Built-Up Edge (BUE) From 2-D Images of Cutting Tools Using Machine Vision
,”
Int. J. Adv. Manuf. Technol.
,
46
(
9–12
), pp.
1179
1189
.
15.
Sukvittayawong
,
S.
, and
Inasaki
,
I.
,
1994
, “
Detection of Built-Up Edge in Turning Process
,”
Int. J. Mach. Tools Manuf.
,
34
(
6
), pp.
829
840
.
16.
Fang
,
N.
,
Pai
,
P. S.
, and
Edwards
,
N.
,
2010
, “
Prediction of Built-Up Edge Formation in Machining With round Edge and Sharp Tools Using a Neural Network Approach
,”
Int. J. Comput. Integr. Manuf.
,
23
(
11
), pp.
1002
1014
.
17.
Boothroyd
,
G.
,
1988
,
Fundamentals of Metal Machining and Machine Tools
,
3rd ed.
,
CRC Press
, Boca Raton, FL.
18.
Moriwaki, T.
, and
Tobito, M.
, 1990, “
A New Approach to Automatic Detection of Life of Coated Tool Based on Acoustic Emission Measurement
,”
ASME J. Eng. Ind.
,
112
(3), p. 212.
19.
Al-khalid, H. K.
,
Alaskari, A. M.
, and
Oraby, S. E.
, 2011, “
Hardness Variations as Affected by Bar Diameter of AISI 4140 Steel
,”
Int. J. Mech. Mechatronics Eng.
,
5
(3), pp. 284–290.https://waset.org/publications/9476/hardness-variations-as-affected-by-bar-diameter-of-aisi-4140-steel
20.
Al-Khalid
,
H.
,
Alaskari
,
A.
, and
Oraby
,
S.
,
2012
, “
Statistical and Graphical Assessment of Circumferential and Radial Hardness Variation of AISI 4140, AISI 1020 and AA 6082 Aluminum Alloy
,”
Materials
,
5
(
1
), pp.
12
26
.
21.
Teti
,
R.
, and
La Commare
,
U.
,
1991
, “
Cutting Process Monitoring of Aeronautical Aluminum Alloys Through AE Techniques
,”
International Conference on Innovation Metal Cutting Processes & Materials
, pp.
2
4
.
22.
Teti
,
R.
, and
La Commare
,
U.
,
1992
, “
Cutting Conditions and Work Material State Identification Through Acoustic Emission Methods
,”
CIRP Ann.Manuf. Technol.
,
41
(
1
), pp.
89
92
.
23.
Teti
,
R.
, and
Buonadonna
,
P.
,
1994
, “
Work Material State Monitoring Using Spectral Features
,”
Tenth International Conference on CAPE
, pp.
51
63
.
24.
Ghasempoor
,
A.
,
Jeswiet
,
J.
, and
Moore
,
T. N.
,
1999
, “
Real Time Implementation of On-Line Tool Condition Monitoring in Turning
,”
Int. J. Mach. Tools Manuf.
,
39
(
12
), pp.
1883
1902
.
25.
Abellan-Nebot
,
J. V.
, and
Subirón
,
F. R.
,
2010
, “
A Review of Machining Monitoring Systems Based on Artificial Intelligence Process Models
,”
Int. J. Adv. Manuf. Technol.
,
47
(
1–4
), pp.
237
257
.
26.
Siddhpura
,
A.
, and
Paurobally
,
R.
,
2013
, “
A Review of Flank Wear Prediction Methods for Tool Condition Monitoring in a Turning Process
,”
Int. J. Adv. Manuf. Technol.
,
65
(
1–4
), pp.
371
393
.
27.
Roth
,
J. T.
,
Djurdjanovic
,
D.
,
Yang
,
X.
,
Mears
,
L.
, and
Kurfess
,
T.
,
2010
, “
Quality and Inspection of Machining Operations: Tool Condition Monitoring
,”
ASME J. Manuf. Sci. Eng.
,
132
(
4
), p.
041015
.
28.
Rafezi
,
H.
,
Behzad
,
M.
, and
Akbari
,
J.
,
2012
, “
Time Domain and Frequency Spectrum Analysis of Sound Signal for Drill Wear Detection
,”
Int. J. Comput. Electr. Eng.
,
4
(
5
), pp.
722
725
.
29.
Raja, J. E.
,
Lim, W. S.
, and
Venkataseshaiah, C.
, 2012, “
Emitted Sound Analysis for Tool Flank Wear Monitoring using Hilbert Huang Transform
,”
Int. J. Comput. Electr. Eng.
,
4
(2), pp. 110–114.http://www.ijcee.org/papers/460-E1224.pdf
30.
Botsaris
,
P. N.
, and
Tsanakas
,
J. A.
,
2008
, “
State of the Art in Methods Applied to Tool Condition Monitoring (TCM) in Unmanned Machining Operations
,”
International Conference of COMADEM
, Prague, Czech Republic, Feb. 25–Mar. 13, pp.
73
87
.
31.
Shi
,
D.
, and
Gindy
,
N. N.
,
2007
, “
Tool Wear Predictive Model Based on Least Squares Support Vector Machines
,”
Mech. Syst. Signal Process.
,
21
(
4
), pp.
1799
1814
.
32.
Zhang
,
B.
,
Katinas
,
C.
, and
Shin
,
Y. C.
,
2018
, “
Robust Tool Wear Monitoring Using Systematic Feature Selection in Turning Processes With Consideration of Uncertainties
,”
ASME J. Manuf. Sci. Eng.
,
140
(8), pp.
1
12
.
33.
Lela
,
B.
,
Bajić
,
D.
, and
Jozić
,
S.
,
2009
, “
Regression Analysis, Support Vector Machines, and Bayesian Neural Network Approaches to Modeling Surface Roughness in Face Milling
,”
Int. J. Adv. Manuf. Technol.
,
42
(
11–12
), pp.
1082
1088
.
34.
Kothuru
,
A.
,
Nooka
,
S. P.
, and
Liu
,
R.
,
2017
, “
Cutting Process Monitoring System Using Audible Sound Signals and Machine Learning Techniques: An Application to End Milling
,”
ASME
Paper No. MSEC2017-3069.
35.
Sun
,
J.
,
Rahman
,
M.
,
Wong
,
Y. S.
, and
Hong
,
G. S.
,
2004
, “
Multiclassification of Tool Wear With Support Vector Machine by Manufacturing Loss Consideration
,”
Int. J. Mach. Tools Manuf.
,
44
(
11
), pp.
1179
1187
.
36.
LeCun
,
Y.
,
Bengio
,
Y.
, and
Hinton
,
G.
,
2015
, “
Deep Learning
,”
Nature
,
521
, p. 436.
37.
Nooka
,
S. P.
,
2016
,
Fusion of Mini-Deep Nets
,
Rochester Institute of Technology
, Rochester, NY.
38.
Girshick, R.
,
Donahue, T.
,
Darrell, T.
, and
Malik, J.
, “
Rich Feature Hierarchies for Accurate Object detection and Smantic Sgmentation
,” e-print arXiv:1311.2524.
39.
Long
,
J.
,
Shelhamer
,
E.
, and
Darrell
,
T.
,
2015
, “
Fully Convolutional Networks for Semantic Segmentation
,”
IEEE
Computer Society Conference on Computer Vision and Pattern Recognition
, Boston, MA, June 7–12, pp.
3431
–34
40
.
40.
Ess
,
A.
,
Mueller
,
T.
,
Grabner
,
H.
, and
van Gool
,
L.
,
2009
, “
Segmentation-Based Urban Traffic Scene Understanding
,”
British Machine Vision Conference
(
BMVC 2009
), London, Sept. 7–10, pp.
84.1
84.11
.
41.
Dolinšek
,
S.
, and
Kopač
,
J.
,
2006
, “
Mechanism and Types of Tool Wear; Particularities in Advanced Cutting Materials
,”
J. Achiev. Mater.
,
19
(
1
), pp.
11
18
.http://jamme.acmsse.h2.pl/papers_vol19_1/1285.pdf
42.
Rehorn
,
A. G.
,
Jiang
,
J.
, and
Orban
,
P. E.
,
2005
, “
State-of-the-Art Methods and Results in Tool Condition Monitoring: A Review
,”
Int. J. Adv. Manuf. Technol.
,
26
(
7–8
), pp.
693
710
.
43.
Refaeilzadeh
,
P.
,
Tang
,
L.
, and
Liu
,
H.
,
2009
, “
Cross-Validation
,”
Encyclopedia of Database Systems
,
Springer
, Boston, MA, pp.
532
38
.
44.
Schalkoff
,
R. J.
,
1997
,
Artificial Neural Networks
, Vol.
1
,
McGraw-Hill
,
New York
.
45.
Karpathy
,
A.
,
Toderici
,
G.
,
Shetty
,
S.
,
Leung
,
T.
,
Sukthankar
,
R.
, and
Fei-Fei
,
L.
,
2014
, “
Large-Scale Video Classification With Convolutional Neural Networks
,”
IEEE Conference on Computer Vision and Pattern Recognition
(
CVPR
), Columbus, OH, June 23–28, pp.
1725
1732
.
46.
Goodfellow
,
I. J.
,
Warde-Farley
,
D.
,
Mirza
,
M.
,
Courville
,
A.
, and
Bengio
,
Y.
,
2013
, “
Maxout Networks
,” e-print arXiv:1302.4389.
47.
Nair
,
V.
, and
Hinton
,
G. E.
,
2010
, “
Rectified Linear Units Improve Restricted Boltzmann Machines
,”
27th International Conference on Machine Learning
, Haifa, Israel, June 21–24, pp.
807
–8
14
.
48.
Srivastava
,
N.
,
Hinton
,
G.
,
Krizhevsky
,
A.
,
Sutskever
,
I.
, and
Salakhutdinov
,
R.
,
2014
, “
Dropout: A Simple Way to Prevent Neural Networks From Overfitting
,”
J. Mach. Learn. Res.
,
15
, pp.
1929
1958
.
49.
Mikolov
,
T.
,
Karafiat
,
M.
,
Burget
,
L.
,
Cernocky
,
J.
, and
Khudanpur
,
S.
,
2015
, “
Recurrent Neural Network Based Language Model
,” Eleventh Annual Conference of the International Speech Communication Association.
50.
Kingma, D. P.
, and
Ba, J. L.
, 2015, “
Adam: A Method for Stochastic Optimization
,” International Conference on Learning Representations (
ICLR
), San Diego, CA, May 7–9.https://arxiv.org/pdf/1412.6980.pdf
51.
Ripley
,
B. D.
,
2007
,
Pattern Recognition and Neural Networks
,
Cambridge University Press
, Cambridge, UK.
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