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Research Papers

Improving Tool-Life Stochastic Control Through a Tool-Life Model Based on Diffusion Theory

[+] Author and Article Information
Marcello Braglia

Dipartimento di Ingegneria Civile e Industriale,
Università di Pisa,
Largo Lucio Lazzarino, Pisa 56122, Italy

Davide Castellano

Dipartimento di Ingegneria Civile e Industriale,
Università di Pisa,
Largo Lucio Lazzarino, Pisa 56122, Italy
e-mail: davide.castellano@for.unipi.it

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1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received May 14, 2014; final manuscript received March 9, 2015; published online July 8, 2015. Assoc. Editor: Robert Gao.

J. Manuf. Sci. Eng 137(4), 041005 (Aug 01, 2015) (11 pages) Paper No: MANU-14-1281; doi: 10.1115/1.4030078 History: Received May 14, 2014; Revised March 09, 2015; Online July 08, 2015

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.

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References

Figures

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Fig. 1

Example of tool-life densities obtained for different initial data (u0,t0), with respect to a wear limit uf=30 × 10-2 mm, derived according to the diffusion-based tool-life model by Braglia and Castellano [23]

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Fig. 5

Distribution of the values of both R2 and RMSE coefficients among all fits

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Fig. 4

Third-degree curves fitting the experimental data

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Fig. 3

Experimental data (from Refs. [50,51])

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Fig. 2

Definition of the parameter VB used to evaluate the flank wear level (from Ref. [2])

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Fig. 6

Histogram plot and empirical c.d.f. of the experimental failure data relevant to a wear limit uf=30 × 10-2mm.

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Fig. 7

Wear prediction capabilities of the diffusion-based approach, with one inspection at time  1tI=6 min. Continuous line: actual wear. Dashed line: expected wear. Upper dotted line: expected wear +3σ˜u2(t;t0). Lower dotted line: expected wear −3σ˜u2(t;t0). t0 = 6 min.

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Fig. 8

Wear prediction capabilities of the diffusion-based approach, with one inspection at time  2tI=8 min. Continuous line: actual wear. Dashed line: expected wear. Upper dotted line: expected wear +3σ˜u2(t;t0). Lower dotted line: expected wear −3σ˜u2(t;t0). t0 = 8 min.

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