Dry cutting performance of partially filtered arc deposited titanium aluminium nitride coatings with various metal nitride base coatings

Harris, S. G.; Doyle, E.D.; Vlasveld, A.C; Dolder, P.J.

doi:10.1016/s0257-8972(01)01421-9

Cited by 46 publications

(12 citation statements)

References 10 publications

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“…Thus, although these fluids can increase tool life, the current dominant trend in machining processes consists in avoiding their use [12,14,15]. Because of this, dry or near to dry based on the use of the minimum quantity of lubricant (MQL)-machining processes are designed and carried out [3,[14][15][16][17][18][19][20]. As a consequence of all that, high research efforts are currently being made in order to optimise tool life in highly aggressive conditions.…”

Section: Introductionmentioning

confidence: 99%

SEM and EDS Characterisation of Layering TiO_xGrowth onto the Cutting Tool Surface in Hard Drilling Processes of Ti-Al-V Alloys

Álvarez

Salguero

Sánchez

et al. 2011

Advances in Materials Science and Engineering

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Scanning electron microscopy (SEM) has been used to identify and analyse the secondary adhesion effect precursors formed during the dry drilling processes of Ti-6Al-4V alloy over the rake face and flute of the drilling tools. Subsequent analysis with energy dispersive spectroscopy (EDS) was enabled to distinguish its compositional characteristics. Thus, according to the EDS obtained data, a stratified multi built-up layer (MBUL) composed by TiOxis formed over the rake face of the tool. Furthermore, this multi-layer adhered allows initially the built-up edge (BUE) development close to the edge of the tool by a mechanical adhesion mechanism. In a second step, it is responsible for the formation of a thicker secondary BUL which avoids the chip flow, and it provokes the tool collapse. These mechanisms are different from those observed in the dry machining of other alloys such as steels, nickel-based alloys, or aluminium alloys.

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Section: Introductionmentioning

confidence: 99%

SEM and EDS Characterisation of Layering TiO_xGrowth onto the Cutting Tool Surface in Hard Drilling Processes of Ti-Al-V Alloys

Álvarez

Salguero

Sánchez

et al. 2011

Advances in Materials Science and Engineering

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“…However, these cutting fluids can pollute and increase the total cost of the process considerably [13][14][15][16]. This, along with the growing social preoccupation about the environmental conservation, has made necessary to develop new cleaner production technologies.…”

Section: Introductionmentioning

confidence: 99%

“…According to this philosophy, the most radical method to carry out a sustainable manufacturing is to eliminate cutting fluids, giving rise to the so-known dry machining. However, the total suppression of these fluids involves to work under very aggressive conditions [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Surface roughness of AA7050 alloy turned bars

Rubio

Camacho

Sanchez-Sola

et al. 2005

Journal of Materials Processing Technology

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“…Researchers use various methods for determination of surface and hole quality in drilling of AISI D2 cold work tool steel with uncoated TiN and TiAlN monolayer-and TiAlN/TiN multilayer-coated-cemented carbide drills. [1][2][3][4][5][6][7][8][9] Akıncıoglu et al 10 successfully adopted Artificial Neural Network (ANN) for prediction of surface and hole quality in drilling of AISI D2 cold work tool steel with uncoated TiN and TiAlN monolayer-and TiAlN/TiN multilayercoated-cemented carbide drills. However, ANN has some limitations such as black box approach, low generalization capability, arriving at local minima, overtraining problem, etc.…”

Section: Introductionmentioning

confidence: 99%

Determination of Surface and Hole Quality in Drilling of AISI D2 Cold Work Tool Steel Using MPMR, MARS and LSSVM

Samui

2014

J. Adv. Manuf. Syst.

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This paper adopts Minimax Probability Machine Regression (MPMR), Multivariate Adaptive Regression Spline (MARS), and Least Square Support Vector Machine (LSSVM) for prediction of surface and hole quality in drilling of AISI D2 cold work tool steel with uncoated titanium nitride (TiN) and titanium aluminum nitride (TiAlN) monolayer-and TiAlN/TiN multilayer-coated-cemented carbide drills. MPMR is a probabilistic model. MARS is a nonparametric regression technique. LSSVM is developed based on statistical learning algorithm. Cutting tool (t), Feed rate (fr)(mm/rev), and Cutting speed (v)(m/min) have been adopted as inputs of MPMR, MARS, and LSSVM. The output of MPMR, MARS, and LSSVM is Surface roughness (rs) (µm) and Roundness error (re) (µm). A comparative study has been presented between the developed models. The results show that the developed model gives excellent performance.

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Dry cutting performance of partially filtered arc deposited titanium aluminium nitride coatings with various metal nitride base coatings

Cited by 46 publications

References 10 publications

SEM and EDS Characterisation of Layering TiO_xGrowth onto the Cutting Tool Surface in Hard Drilling Processes of Ti-Al-V Alloys

SEM and EDS Characterisation of Layering TiO_xGrowth onto the Cutting Tool Surface in Hard Drilling Processes of Ti-Al-V Alloys

Surface roughness of AA7050 alloy turned bars

Determination of Surface and Hole Quality in Drilling of AISI D2 Cold Work Tool Steel Using MPMR, MARS and LSSVM

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Dry cutting performance of partially filtered arc deposited titanium aluminium nitride coatings with various metal nitride base coatings

Cited by 46 publications

References 10 publications

SEM and EDS Characterisation of Layering TiOxGrowth onto the Cutting Tool Surface in Hard Drilling Processes of Ti-Al-V Alloys

SEM and EDS Characterisation of Layering TiOxGrowth onto the Cutting Tool Surface in Hard Drilling Processes of Ti-Al-V Alloys

Surface roughness of AA7050 alloy turned bars

Determination of Surface and Hole Quality in Drilling of AISI D2 Cold Work Tool Steel Using MPMR, MARS and LSSVM

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Product

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SEM and EDS Characterisation of Layering TiO_xGrowth onto the Cutting Tool Surface in Hard Drilling Processes of Ti-Al-V Alloys

SEM and EDS Characterisation of Layering TiO_xGrowth onto the Cutting Tool Surface in Hard Drilling Processes of Ti-Al-V Alloys