The Effect of PVD and CVD Coating Structures on the Durability of Sintered Cutting Edges

Abstract In the work it was demonstrated that the exploitative stability of edges from tool ceramics and sintered carbides coated with gradient and multilayer PVD and CVD coatings depends mainly on the adherence of the coatings to the substrate, while the change of coating microhardness from 2300 to 3500 HV0.05, the size of grains and their thickness affect the durability of the edges to a lesser extent. It was found that some coatings showed a fine-grained structure. The coatings which contained the AlN phase with hexagonal lattice showed a considerably higher adhesion to the substrate from sialon ceramics rather than the coatings containing the TiN phase. Better adherence of the coatings containing the AlN phase with hexagonal lattice is connected with the same kind of interatomic bonds (covalent) in material of both coating and ceramic substrate. In the paper the exploitative properties of the investigated coatings in the technological cutting trials were also determined. The models of artificial neural network, which demonstrate a relationships between the edge stability and coating properties such as: critical load, microhardness, thickness and size of grains were worked out.

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Influence of Substrate Bias Voltage on Properties of Medium-Frequency Magnetron Sputtering WS2 Soft Coatings

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.229-231.2427 ◽

2012 ◽

Vol 229-231 ◽

pp. 2427-2431

Author(s):

Yun Song Lian ◽

Jian Xin Deng ◽

Shi Peng Li ◽

Guang Yuan Yan ◽

Hong Wei Cheng

Keyword(s):

Magnetron Sputtering ◽

Critical Load ◽

Bias Voltage ◽

Ion Beam ◽

Substrate Bias ◽

Coating Properties ◽

Medium Frequency ◽

Substrate Bias Voltage ◽

Fine Grained ◽

Fine Grained Structure

WS2 soft coatings were deposited by medium-frequency magnetron sputtering, multi-arc ion plating and ion beam assisted deposition technique on the YT15 cemented carbide substrates. The influence of the substrate bias voltage on the coating properties has been studied. The coatings exhibited a dense and fine grained structure. WS2 soft coatings with substrate bias voltage of -100 V revealed a better coating properties than other four samples, as the best crystallization of (002) preferred crystal orientation of Ⅱ texture, the most smooth and uniform microstructure of the coating, the largest critical load of 43.25 N and the largest coating thickness of 1.5 μm. But the microhardness of sample S1 was only 515.4 Hv. The critical load and the thickness of WS2 coatings decreased firstly, then increased, and finally reduced with the increasement of substrate bias voltage. And the microhardness of the coatings decreased firstly, then increased, and decreased, finally increased with the increasement of substrate bias voltage.

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Research on the Application of Artificial Neural Network in the Fine-Grained Software Rejuvenation of Computing System

Chinese Journal of Computers ◽

10.3724/sp.j.1016.2008.01268 ◽

2009 ◽

Vol 31 (7) ◽

pp. 1268-1275

Author(s):

Zhan WANG ◽

Cheng-Hao GUO ◽

Feng-Yu LIU ◽

Hong ZHANG

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Computing System ◽

Fine Grained ◽

Software Rejuvenation ◽

Artificial Neural

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Influence of the Deposition Temperature on the Properties of Medium-Frequency Magnetron Sputtering WS2 Soft Coated Tools

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.472-475.44 ◽

2012 ◽

Vol 472-475 ◽

pp. 44-49 ◽

Cited By ~ 2

Author(s):

Yun Song Lian ◽

Jian Xin Deng ◽

Shi Peng Li

Keyword(s):

Magnetron Sputtering ◽

Crystal Orientation ◽

Deposition Temperature ◽

Ion Beam ◽

Coating Properties ◽

Medium Frequency ◽

Fine Grained ◽

Coated Tools ◽

Ion Beam Assisted Deposition ◽

Fine Grained Structure

WS2 soft coated tools were deposited by medium-frequency magnetron sputtering, multi-arc ion plating and ion beam assisted deposition technique on the YT15 cemented carbide substrates. The influence of the deposition temperature on the coating properties has been studied. The coatings exhibited a dense and fine grained structure. WS2 coatings revealed a (002) preferred crystal orientation. WS2 soft coatings with deposition temperature of 200°C revealed a better crystallization of (002) preferred crystal orientation of Ⅱ texture than other three deposition temperatures. And the adhesive strength could get to 43.25N which was the largest. The thickness of WS2 coatings decreased firstly, then increased, and finally reduced with the increasement of deposition temperature. And the microhardness of the coatings increased firstly, then decreased, and finally increased with the increasement of deposition temperature.

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Artificial neural network implementation on a fine-grained FPGA

Field-Programmable Logic Architectures, Synthesis and Applications - Lecture Notes in Computer Science ◽

10.1007/3-540-58419-6_126 ◽

1994 ◽

pp. 421-431 ◽

Cited By ~ 25

Author(s):

P. Lysaght ◽

J. Stockwood ◽

J. Law ◽

D. Girma

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fine Grained ◽

Artificial Neural

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Developing New Empirical Formulae for the Resilient Modulus of Fine-Grained Subgrade Soils Using a Large Long-Term Pavement Performance Dataset and Artificial Neural Network Approach

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211057054 ◽

2021 ◽

pp. 036119812110570

Author(s):

Halil Ibrahim Fedakar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Resilient Modulus ◽

Constitutive Models ◽

Pavement Performance ◽

Dry Density ◽

Fine Grained ◽

Ann Models ◽

Artificial Neural

Artificial neural network (ANN) has been successfully used for developing prediction models for resilient modulus (Mr). However, no reliable Mr formula derived from these models has been proposed in previous studies, although engineers/researchers need empirical formulae for hand calculation of Mr. Therefore, this study aimed to propose reliable empirical formulae for the Mr of fine-grained soils using ANN. For this purpose, thousands of ANN models were developed using the long-term pavement performance (LTPP) and external datasets. The input parameters were the percentage of soil particles passing through #200 sieve (P200), silt percentage (SP), clay percentage (CP), liquid limit (LL), plasticity index (PI), maximum dry density ([ρdry]max), optimum moisture content (wopt), confining pressure (σc), and nominal maximum axial stress (σz). The ANN models were compared with several constitutive models. The results indicate that the constitutive models failed to predict the Mr, and the best Mr predictions were obtained by the ANN-C9 (P200, SP, CP, LL, PI, σc, and σz), ANN-C10 (P200, SP, CP, [ρdry]max, wopt, σc, and σz), and ANN-C11 (P200, SP, CP, LL, PI, [ρdry]max, wopt, σc, and σz) models. Thus, the structures of these ANN models were formulated and proposed as the new empirical formulae for the Mr of fine-grained soils. Sensitivity analysis was also performed on these ANN models. It was determined that (ρdry)max is the most influential parameter in the ANN-C10 model, and LL is the most influential parameter in the ANN-C9 and ANN-C11 models. On the other hand, σc and σz are the least influential parameters.

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