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Research Papers

Tool Wear in Cutting Operations: Experimental Analysis and Analytical Models

[+] Author and Article Information
A. Attanasio

e-mail: aldo.attanasio@ing.unibs.it

E. Ceretti

e-mail: elisabetta.ceretti@ing.unibs.it
University of Brescia,
Via Branze 38,
Brescia 25123, Italy

C. Giardini

University of Bergamo,
Via Marconi 5, Dalmine (BG) 24044, Italy
e-mail: claudio.giardini@unibg.it

C. Cappellini

University of Brescia,
Via Branze 38,
25123 Brescia, Italy
e-mail: cristian.cappellini@ing.unibs.it

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the Journal of Manufacturing Science and Engineering. Manuscript received August 28, 2012; final manuscript received June 11, 2013; published online September 13, 2013. Assoc. Editor: Y. B. Guo.

J. Manuf. Sci. Eng 135(5), 051012 (Sep 13, 2013) (11 pages) Paper No: MANU-12-1255; doi: 10.1115/1.4025010 History: Received August 28, 2012; Revised June 11, 2013

The possibility of predicting the amount of the tool wear in machining processes is an interesting topic for industries, since tool wear affects surface integrity of the final parts and tool life is strictly connected with substitution policy and production costs. The definition of models able to correctly forecast the tool wear development is an important topic in the research field. For this reason in the present work, a comparison between response surface methodology (RSM) and artificial neural networks (ANNs) fitting techniques in tool wear forecasting was performed. For developing these predictive models, experimental values of tool wear, obtained by longitudinal turning operations with variable cutting parameters, were collected. Once selected, the best configuration of the two previously mentioned techniques, the resultant errors with respect to experimental data were estimated and then compared. The results showed that the developed models are able to predict the amount of wear. The comparison demonstrated that ANNs give better approximation than RSM in the prediction of the amount of the flank wear (VB) and of the crater wear (KT) depth. The obtained results are interesting not only from a scientific point of view but also for industries. In fact, it should be possible to implement the best model into a production manager software in order to correctly define the tool change during the lot production.

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References

Figures

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Fig. 1

Wear parameters defined in ISO Standard 3685

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Fig. 2

Experimental test set-up

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Fig. 3

Experimental tests performed with relative varying process parameters

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Fig. 4

(a) Tool sections for crater profile measurement, (b) typical acquired crater profile, (c) filtered profile, and (d) 3D representation of the crater of the insert

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Fig. 5

Measuring technique for VB: test 8, t = 4 min

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Fig. 6

Evolution of (a) VB and (b) KT

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Fig. 7

Probability plots for (a) VB, (b) √VB, (c) KT, and (d) ln(KT)

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Fig. 8

Main effect plot for (a) √VB and (b) ln(KT)

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Fig. 9

Interaction plot for (a) √VB and (b) ln(KT)

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Fig. 10

VB and KT absolute errors after 7 min of cut; (a), (b) 2nd order RSM models; (c), (d) 3rd order RSM models, and (e), (f) ANNs models (tests from 1 to 9 in light grey, tests from 10 to 12 in dark grey)

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Fig. 11

Fitting surface of experimental data (a), (b), 2nd order RSM model (c), (d), 3rd order RSM model (e), (f), and ANNs model (g), (h) for VB and for KT after 7 min of cut

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