It is known that estimating the wear level at a future time instant and obtaining an updated evaluation of the tool-life density is essential to keeping machined parts at the desired quality level, reducing material waste, increasing machine availability, and guaranteeing the safety requirements. In this regard, the present paper aims at showing that the tool-life model that Braglia and Castellano (Braglia and Castellano, 2014, “Diffusion Theory Applied to Tool-Life Stochastic Modeling Under a Progressive Wear Process,” ASME J. Manuf. Sci. Eng., 136(3), p. 031010) developed can be successfully adopted to probabilistically predict the future tool wear and to update the tool-life density. Thanks to the peculiarities of a stochastic diffusion process, the approach presented allows deriving the density of the wear level at a future time instant, considering the information on the present tool wear. This makes it therefore possible updating the tool-life density given the information on the current state. The method proposed is then experimentally validated, where its capability to achieve a better exploitation of the tool useful life is also shown. The approach presented is based on a direct wear measurement. However, final considerations give cues for its application under an indirect wear estimate.

References

References
1.
Gray
,
A. E.
,
Seidmann
,
A.
, and
Stecke
,
K. E.
,
1993
, “
A Synthesis of Decision Models for Tool Management in Automated Manufacturing
,”
Manage. Sci.
,
39
(
5
), pp.
549
567
.10.1287/mnsc.39.5.549
2.
Kalpakjian
,
S.
, and
Schmid
,
S. R.
,
2009
,
Manufacturing Engineering and Technology
,
6th ed.
,
Prentice Hall
,
Upper Saddle River, NJ
.
3.
Rossetto
,
S.
, and
Levi
,
R.
,
1977
, “
Fracture and Wear as Factors Affecting Stochastic Tool-Life Models and Machining Economics
,”
ASME J. Manuf. Sci. Eng.
,
99
(
1
), pp.
281
286
.10.1115/1.3439156
4.
Natarajan
,
U.
,
Periasamy
,
V. M.
, and
Saravanan
,
R.
,
2007
, “
Application of Particle Swarm Optimisation in Artificial Neural Network for the Prediction of Tool Life
,”
Int. J. Adv. Manuf. Technol.
,
31
(
9-10
), pp.
871
876
.10.1007/s00170-005-0252-1
5.
Vagnorius
,
Z.
,
Rausand
,
M.
, and
Sørby
,
K.
,
2010
, “
Determining Optimal Replacement Time for Metal Cutting Tools
,”
Euro. J. Oper. Res.
,
206
(
2
), pp.
407
416
.10.1016/j.ejor.2010.03.023
6.
Taylor
,
F. W.
,
1907
, “
On the Art of Cutting Metals
,”
ASME Trans.
,
28
, pp.
310
350
.
7.
Lamond
,
B. F.
, and
Sodhi
,
M. S.
,
1997
, “
Using Tool Life Models to Minimize Processing Time on a Flexible Machine
,”
IIE Trans.
,
29
(
7
), pp.
611
621
.10.1080/07408179708966370
8.
Marksberry
,
P. W.
, and
Jawahir
,
I. S.
,
2008
, “
A Comprehensive Tool-Wear/Tool-Life Performance Model in the Evaluation of NDM (Near Dry Machining) for Sustainable Manufacturing
,”
Int. J. Mach. Tools Manuf.
,
48
(
7-8
), pp.
878
886
.10.1016/j.ijmachtools.2007.11.006
9.
Xiong
,
J.
,
Guo
,
Z.
,
Yang
,
M.
,
Wan
,
W.
, and
Dong
,
G.
,
2012
, “
Tool Life and Wear of WC-TiC-Co Ultrafine Cemented Carbide During Dry Cutting of AISI H13 Steel
,”
Ceram. Int.
,
39
(
1
), pp.
337
346
.10.1016/j.ceramint.2012.06.031
10.
Li
,
C. R.
, and
Sarker
,
B. R.
,
2013
, “
Lifespan Prediction of Cutting Tools for High-Value-Added Products
,”
Int. J. Adv. Manuf. Technol.
,
69
(
5–8
), pp.
1887
1894
.10.1007/s00170-013-5160-1
11.
Karandikar
,
J. M.
,
Abbas
,
A. E.
, and
Schmitz
,
T. L.
,
2014
, “
Tool Life Prediction Using Bayesian Updating. Part 1: Milling Tool Life Model Using a Discrete Grid Method
,”
Precis. Eng.
,
38
(
1
), pp.
9
17
.10.1016/j.precisioneng.2013.06.006
12.
Karandikar
,
J. M.
,
Abbas
,
A. E.
, and
Schmitz
,
T. L.
,
2014
, “
Tool Life Prediction Using Bayesian Updating. Part 2: Turning Tool Life Using a Markov Chain Monte Carlo Approach
,”
Precis. Eng.
,
38
(
1
), pp.
18
27
.10.1016/j.precisioneng.2013.06.007
13.
Sekhon
,
G. S.
,
1983
, “
A Simulation Model of Machining Economics Incorporating Stochastic Variability of Work and Tool Properties
,”
Int. J. Mach. Tool Des. Res.
,
23
(
1
), pp.
61
70
.10.1016/0020-7357(83)90007-0
14.
Iakovou
,
E.
,
Ip
,
C. M.
, and
Koulamas
,
C.
,
1996
, “
Machining Economics With Phase-Type Distributed Tool Lives and Periodic Maintenance Control
,”
Comput. Oper. Res.
,
23
(
1
), pp.
53
62
.10.1016/0305-0548(94)00092-M
15.
Noël
,
M.
,
Lamond
,
B. F.
, and
Sodhi
,
M. S.
,
2009
, “
Simulation of Random Tool Lives in Metal Cutting on a Flexible Machine
,”
Int. J. Prod. Res.
,
47
(
7
), pp.
1835
1855
.10.1080/00207540701644169
16.
Kaspi
,
M.
, and
Shabtay
,
D.
,
2001
, “
Optimization of the Machining Economics Problem Under the Periodic Control Strategy
,”
Int. J. Prod. Res.
,
39
(
17
), pp.
3889
3900
.10.1080/00207540110068772
17.
Ramalingam
,
S.
, and
Watson
,
J. D.
,
1977
, “
Tool-Life Distributions—Part 1: Single-Injury Tool-Life Model
,”
ASME J. Manuf. Sci. Eng.
,
99
(
3
), pp.
519
522
10.1115/1.3439271
18.
Ramalingam
,
S.
,
1977
, “
Tool-Life Distributions—Part 2: Multiple-Injury Tool-Life Model
,”
ASME J. Manuf. Sci. Eng.
,
99
(
3
), pp.
523
528
.10.1115/1.3439272
19.
Makis
,
V.
,
1995
, “
Optimal Replacement of a Tool Subject to Random Failure
,”
Int. J. Prod. Econ.
,
41
(
1–3
), pp.
249
256
.10.1016/0925-5273(95)00061-5
20.
Liu
,
P. H.
,
Makis
,
V.
, and
Jardine
,
A. K. S.
,
2001
, “
Scheduling of the Optimal Tool Replacement Times in a Flexible Manufacturing System
,”
IIE Trans.
,
33
(
6
), pp.
487
495
.10.1023/A:1007664002697
21.
Galante
,
G.
,
Lombardo
,
A.
, and
Passannanti
,
A.
,
1998
, “
Tool-Life Modelling as a Stochastic Process
,”
Int. J. Mach. Tools Manuf.
,
38
(
10–11
), pp.
1361
1369
.10.1016/S0890-6955(98)00019-4
22.
Braglia
,
M.
,
Castellano
,
D.
, and
Frosolini
,
M.
,
2014
, “
Stochastic Theory of Tool Life—Theoretical Developments on the Injury Theory
,”
Int. J. Math. Modell. Numer. Optim.
,
5
(
4
), pp.
265
279
.10.1504/IJMMNO.2014.065398
23.
Braglia
,
M.
, and
Castellano
,
D.
,
2014
, “
Diffusion Theory Applied to Tool-Life Stochastic Modeling Under a Progressive Wear Process
,”
ASME J. Manuf. Sci. Eng.
,
136
(
3
), p.
031010
.10.1115/1.4026841
24.
Tobon-Mejia
,
D. A.
,
Medjaher
,
K.
, and
Zerhouni
,
N.
,
2012
, “
CNC Machine Tool's Wear Diagnostic and Prognostic by Using Dynamic Bayesian Networks
,”
Mech. Syst. Signal Process.
,
28
, pp.
167
182
.10.1016/j.ymssp.2011.10.018
25.
Karandikar
,
J. M.
,
Schmitz
,
T. L.
, and
Abbas
,
A. E.
,
2014
, “
Application of Bayesian Inference to Milling Force Modeling
,”
ASME J. Manuf. Sci. Eng.
,
136
(
2
), p.
021017
.10.1115/1.4026365
26.
Karandikar
,
J.
,
Traverso
,
M.
,
Abbas
,
A.
, and
Schmitz
,
T.
,
2014
, “
Bayesian Inference for Milling Stability Using a Random Walk Approach
,”
ASME J. Manuf. Sci. Eng.
,
136
(
3
), p.
031015
.10.1115/1.4027226
27.
Li
,
X.
, and
Zhang
,
Y.
,
2014
, “
Predictive Control for Manual Plasma Arc Pipe Welding
,”
ASME J. Manuf. Sci. Eng.
,
136
(
4
), p.
041017
.10.1115/1.4027627
28.
Hu
,
Y.
,
Li
,
Z.
,
Li
,
K.
, and
Yao
,
Z.
,
2014
, “
Predictive Modeling and Uncertainty Quantification of Laser Shock Processing by Bayesian Gaussian Processes With Multiple Outputs
,”
ASME J. Manuf. Sci. Eng.
,
136
(
4
), p.
041014
.10.1115/1.4027539
29.
Nelson
,
A. W.
,
Malik
,
A. S.
,
Wendel
,
J. C.
, and
Zipf
,
M. E.
,
2014
, “
Probabilistic Force Prediction in Cold Sheet Rolling by Bayesian Inference
,”
ASME J. Manuf. Sci. Eng.
,
136
(
4
), p.
041006
.10.1115/1.4027434
30.
Zhao
,
N.
,
Li
,
W.
,
Cai
,
W. W.
, and
Abell
,
J. A.
,
2014
, “
A Fatigue Life Study of Ultrasonically Welded Lithium-Ion Battery Tab Joints Based on Electrical Resistance
,”
ASME J. Manuf. Sci. Eng.
,
136
(
5
), p.
051003
.10.1115/1.4027878
31.
Karandikar
,
J. M.
,
Abbas
,
A.
, and
Schmitz
,
T. L.
,
2012
, “
Remaining Useful Tool Life Predictions in Turning Using Bayesian Inference
,”
Int. J. Prognostics Health Manag.
,
4
(
2
), pp.
25
36
.
32.
Geramifard
,
O.
,
Xu
,
J.-X.
,
Zhou
,
J.-H.
, and
Li
,
X.
,
2012
, “
A Physically Segmented Hidden Markov Model Approach for Continuous Tool Condition Monitoring: Diagnostics and Prognostics
,”
IEEE Trans. Ind. Inf.
,
8
(
4
), pp.
964
973
.10.1109/TII.2012.2205583
33.
Wang
,
M.
, and
Wang
,
J.
,
2012
, “
CHMM for Tool Condition Monitoring and Remaining Useful Life
,”
Int. J. Adv. Manuf. Technol.
,
59
(
5-8
), pp.
463
471
.10.1007/s00170-011-3536-7
34.
Ao
,
Y.
, and
Qiao
,
G.
,
2010
, “
Prognostics for Drilling Process With Wavelet Packet Decomposition
,”
Int. J. Adv. Manuf. Technol.
,
50
(
1-4
), pp.
47
52
.10.1007/s00170-009-2509-6
35.
Benkedjouh
,
T.
,
Medjaher
,
K.
,
Zerhouni
,
N.
, and
Rechak
,
S.
,
2013
, “
Health Assessment and Life Prediction of Cutting Tools Based on Support Vector Regression
,”
J. Intell. Manuf.
,
26
(
2
), pp.
213
223
.10.1007/s10845-013-0774-6
36.
Gokulachandran
,
J.
, and
Mohandas
,
K.
,
2015
, “
Prediction of Cutting Tool Life Based on Taguchi Approach With Fuzzy Logic and Support Vector Regression Techniques
,”
Int. J. Qual. Reliab. Manag.
,
32
(
3
), pp.
270
290
.10.1108/IJQRM-06-2012-0084
37.
Attanasio
,
A.
,
Ceretti
,
E.
,
Giardini
,
C.
, and
Cappellini
,
C.
,
2013
, “
Tool Wear in Cutting Operations: Experimental Analysis and Analytical Models
,”
ASME J. Manuf. Sci. Eng.
,
135
(
5
), p.
051011
.10.1115/1.4025010
38.
Brecher
,
C.
,
Klocke
,
F.
,
Brumm
,
M.
, and
Hardjosuwito
,
A.
,
2013
, “
Simulation Based Model for Tool Life Prediction in Bevel Gear Cutting
,”
Prod. Eng.
,
7
(
2–3
), pp.
223
231
.10.1007/s11740-012-0439-x
39.
Hsu
,
B.-M.
,
Shu
,
M.-H.
, and
Wu
,
L.
,
2013
, “
Dynamic Performance Modelling and Measuring for Machine Tools With Continuous-State Wear Processes
,”
Int. J. Prod. Res.
,
51
(
15
), pp.
4718
4731
.10.1080/00207543.2013.793858
40.
Wang
,
J.
,
Wang
,
P.
, and
Gao
,
R. X.
,
2013
, “
Tool Life Prediction for Sustainable Manufacturing
,”
Proceedings of 11th Global Conference on Sustainable Manufacturing
, Sept. 23–25, Berlin.
41.
Palanna
,
R.
, and
Bukkapatnam
,
S. T. S.
,
2001
, “
Concept of Model Based Tampering for Improving Process Performance: An Illustrative Application to Turning Process
,”
Mach. Sci. Technol.
,
6
(
2
), pp.
263
282
.10.1081/MST-120013166
42.
Palanna
,
R.
,
Bukkapatnam
,
S.
, and
Settles
,
F. S.
,
2003
, “
Model-Based Tampering for Improved Process Performance: An Application to Grinding of Shafts
,”
J. Manuf. Processes
,
5
(
1
), pp.
24
32
.10.1016/S1526-6125(03)70037-1
43.
Arsecularatne
,
J. A.
,
Zhang
,
L. C.
, and
Montross
,
C.
,
2006
, “
Wear and Tool Life of Tungsten Carbide, PCBN and PCD Cutting Tools
,”
Int. J. Mach. Tools Manuf.
,
46
(
5
), pp.
482
491
.10.1016/j.ijmachtools.2005.07.015
44.
Salsa
,
S.
,
2009
,
Partial Differential Equations in Action: From Modelling to Theory
,
Springer-Verlag
,
Italia, Milano
.
45.
Devillez
,
A.
,
Lesko
,
S.
, and
Mozer
,
W.
,
2004
, “
Cutting Tool Crater Wear Measurement With White Light Interferometry
,”
Wear
,
256
(
1–2
), pp.
56
65
.10.1016/S0043-1648(03)00384-3
46.
Wang
,
W. H.
,
Hong
,
G. S.
, and
Wong
,
Y. S.
,
2006
, “
Flank Wear Measurement by a Threshold Independent Method With Sub-Pixel Accuracy
,”
Int. J. Mach. Tools Manuf.
,
46
(
2
), pp.
199
207
.10.1016/j.ijmachtools.2005.04.006
47.
Mook
,
W. K.
,
Shahabi
,
H. H.
, and
Ratnam
,
M. M.
,
2009
, “
Measurement of Nose Radius Wear in Turning Tools From a Single 2D Image Using Machine Vision
,”
Int. J. Adv. Manuf. Technol.
,
43
(
3–4
), pp.
217
225
.10.1007/s00170-008-1712-1
48.
Zhang
,
C.
, and
Zhang
,
J.
,
2013
, “
On-Line Tool Wear Measurement for Ball-End Milling Cutter Based on Machine Vision
,”
Comput. Ind.
,
64
(
6
), pp.
708
719
.10.1016/j.compind.2013.03.010
49.
Li
,
W.
,
Singh
,
H. M.
, and
Guo
,
Y. B.
,
2013
, “
An Online Optical System for Inspecting Tool Condition in Milling of H13 Tool Steel and IN 718 Alloy
,”
Int. J. Adv. Manuf. Technol.
,
67
(
5–8
), pp.
1067
1077
.10.1007/s00170-012-4548-7
50.
Giardini
,
C.
,
Pellegrini
,
G.
,
Paganella
,
R.
, and
Bugini
,
A.
,
1991
, “
Experimental Results in Continuous Turning: The Feed Influence on Tool Wear
,”
Proceedings of ICIM’91 Conference
, Torino, Italy.
51.
Giardini
,
C.
,
Pellegrini
,
G.
,
Paganella
,
R.
, and
Bugini
,
A.
,
1990
, “
Sintered Carbide Tool Tip Behaviour in Continuous Turning Operations: Experimental Results
,” Internal Report of the Dipartimento di Ingegneria Meccanica, Università di Brescia.
52.
Blanks
,
H. S.
,
1992
,
Reliability in Procurement and Use: From Specification to Replacement
,
John Wiley & Sons
,
New York
.
53.
Gardiner
,
C. W.
,
1985
,
Handbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences
,
Springer-Verlag
,
Berlin
.
You do not currently have access to this content.