Effects of Initial Defect Morphology upon Corrosion Resistance of TiN Thin Film Coating Made by Dynamic Ion Mixing Method

2004 ◽  
Vol 53 (8) ◽  
pp. 923-929 ◽  
Author(s):  
Michio OHATA ◽  
Yuji KIMURA
Author(s):  
E E Suslov ◽  
A S Larionov ◽  
S B Kislitsin ◽  
I I Chernov ◽  
M S Staltsov ◽  
...  

2020 ◽  
Vol 41 (2) ◽  
pp. 160-168
Author(s):  
I. A. Rastegaev ◽  
I. I. Rastegaeva ◽  
D. L. Merson ◽  
V. A. Korotkov

2021 ◽  
Vol 1892 (1) ◽  
pp. 012016
Author(s):  
Nur Nabilah Samsudin ◽  
Muhammad Firdaus Omar

RSC Advances ◽  
2019 ◽  
Vol 9 (56) ◽  
pp. 32683-32690 ◽  
Author(s):  
Meijun Song ◽  
Haidong Zhu ◽  
Lin Ye ◽  
Chengxiang Liu ◽  
Zhaojun Xu

Biomaterial-associated infections (BAIs) remain a major challenge in clinical surgery because they can potentially cause serious disabilities in patients.


1996 ◽  
Author(s):  
S. W. Jansen ◽  
Philip J. Hatchett ◽  
S. W. Hughes ◽  
D. Paul Jones ◽  
Desmond R. Gibson

2012 ◽  
Vol 1 (1) ◽  
pp. 46 ◽  
Author(s):  
Amir Mahyar Khorasani ◽  
Mohammad Reza Solymany yazdi ◽  
Mehdi Faraji ◽  
Alex Kootsookos

Thin-film coating plays a prominent role on the manufacture of many industrial devices. Coating can increase material performance due to the deposition process. Having adequate and precise model that can predict the hardness of PVD and CVD processes is so helpful for manufacturers and engineers to choose suitable parameters in order to obtain the best hardness and decreasing cost and time of industrial productions. This paper proposes the estimation of hardness of titanium thin-film layers as protective industrial tools by using multi-layer perceptron (MLP) neural network. Based on the experimental data that was obtained during the process of chemical vapor deposition (CVD) and physical vapor deposition (PVD), the modeling of the coating variables for predicting hardness of titanium thin-film layers, is performed. Then, the obtained results are experimentally verified and very accurate outcomes had been attained.


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