Roughness distribution of multiple hit and long surface diffusion length noise reduced discrete growth models

Disrattakit, P.; Chanphana, R.; Chatraphorn, P.

doi:10.1016/j.physa.2016.06.104

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…Moreover, the obtained exponents corroborate the existence of corrections in the one-loop RG [18]. For DT models, the ODFA method also provides values in agreement with nMBE class specially in the case of mild noise reductions [31][32][33]. In the original DT, two scaling regimes were observed: at short scales we report α (DT) loc ≈ 0.90 and at intermediary ones α (DT) loc ≈ 0.97, both considerably closer to the nMBE class than the values found with other methods.…”

Section: Introductionsupporting

confidence: 73%

“…However, the local roughness distributions [23] suggest that the one-dimensional DT model has normal scaling, in agreement with the predictions of dynamic RG analysis for local growth process without quenched disorder or additional symmetries [20]. Thus, the DT model remains controversial and a careful consideration regarding their local roughness exponents, especially without noise reduction techniques [31][32][33], is worthwhile.…”

Section: Introductionmentioning

confidence: 52%

“…At larger scales (2.00 r/ξ 0 2.51), α (DT) loc = 0.976(1) was observed. To the best of our knowledge, ODFA 2 method for DT model yields a first evidence, without noise reduction techniques [31][32][33], of a roughness exponent consistent with the nMBE class (see Fig. 3(c)).…”

Section: Scaling Of the Local Surface Roughnessmentioning

confidence: 87%

“…The scaling properties of one-dimensional interfaces obtained with simulations of lattice models belonging to nMBE universality class is an issue that has attracted considerably attention [12,13,28,[32][33][34][35], given the outstanding importance of diffusion for applications in thin film growth [3,4]. Differently from the Kardar-Parisi-Zhang [40] universality class that have a plenty of lattices models described by its scaling exponents [1,2] ( 2 ) the nMBE class, where diffusion is the ruling mechanism on the surface growth, has only a few basic prototypes.…”

Section: Discussionmentioning

confidence: 99%

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Local roughness exponent in the nonlinear molecular-beam-epitaxy universality class in one dimension

et al. 2019

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We report local roughness exponents, α loc , for three interface growth models in one dimension which are believed to belong the non-linear molecular-beam-epitaxy (nMBE) universality class represented by the Villain-Lais-Das Sarma (VLDS) stochastic equation. We applied an optimum detrended fluctuation analysis (ODFA) [Luis et al., Phys. Rev. E 95, 042801 (2017)] and compared the outcomes with standard detrending methods. We observe in all investigated models that ODFA outperforms the standard methods providing exponents in the narrow interval α loc ∈ [0.96, 0.98] consistent with renormalization group predictions for the VLDS equation. In particular, these exponent values are calculated for the Clarke-Vvdensky and Das Sarma-Tamborenea models characterized by very strong corrections to the scaling, for which large deviations of these values had been reported. Our results strongly support the absence of anomalous scaling in the nMBE universality class and the existence of corrections in the form α loc = 1 − of the one-loop renormalization group analysis of the VLDS equation.

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Section: Introductionsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 52%

Section: Scaling Of the Local Surface Roughnessmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

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Local roughness exponent in the nonlinear molecular-beam-epitaxy universality class in one dimension

et al. 2019

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“…DOI There's plenty of research that aims to predict the behaviour of steel after nitriding treatments, AISI 5140 [20][21][22][23][24][25], AISI 430 [21], AISI 1045 [22], AISI 4340 [23], AISI 410 [24], API X-65 [26], Fe-Cr alloys [27] and examine changes in the resulting structures of treatment nitrided components by plasma nitriding [28,29,32,33], ion nitriding [30,31,34], plasma nitrocarburizing [35] and salt bath nitriding [5]. On the other hand there's many studies interested to study surface roughness [36,37,38,39,40,41], methods of measurement of surface roughness parameters [43,44,45,47] and others [42,46,48]. AISI 4140 is a low-alloy steel widely used in various applications such as automotive driving elements (steering components, crankshafts).…”

Section: Introductionmentioning

confidence: 99%

Effect of Salt Bath Nitriding on Surface Roughness Behaviour of Aisi 4140 Steel

Ghelloudj

Hannachi

Djebaili

2017

Acta Metall Slovaca

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In the present research, AISI 4140 steel was nitrided in salt bath to study and analyze the behaviour of the surface roughness. The structural surface characterization behaviour of the nitrided steel was compared to the behaviour of the same steel which was untreated. The nitriding process was implemented in the salt bath component at ten different times (from 1 h to 10 h) when the temperature was constant at (580ºC). The influence of nitriding treatment on structural properties of the material was studied by scanning electron microscopy (SEM), microhardness tester and surface profilometer. It was found that salt bath nitriding was effective in improving the surface properties behaviour of this steel. Experimental results showed that the nitrides ε-Fe2-3(N,C) and γ'-Fe4(N,C) present in the compound layer increase the microhardness (406-502 HV0.3), the initial surface roughness values of nitrided samples were higher than those of unnitrided specimens, it also observed that the increasing the nitriding time increases the surface roughness parameters (Ra, Rq and Rz).Keywords: salt bath nitriding, AISI 4140 steel, microhardness, surface roughness IntroductionSurface topography is an important characteristic that determines, among other things, catalytic activity, electrochemical potential, adhesion, friction coefficient, susceptibility to wear and scuffing failure and aesthetic appearance [1][2]. Salt bath nitriding [3,4,5] is one of the most widely used thermo-chemical methods [6,7,8], which produces strong and shallow case with high compressive residual stresses on the surface of steel components such as gears, crankshafts, dies and tools [9,10,11]. Surface roughness of industrial components strongly affects their performance, i.e, many surface properties such as friction [12][13], surface wear [14], fluid flow in rough pipes [15] and the functioning of vacuum seals [16] are strictly dependent on surface roughness. In addition, surface roughness seems to be important in bioengineering for example in the joints of the bones [17]. Quite often small roughness is desired since small roughness reduces wear and energy loss, but when high friction is needed also the roughness should be larger. Hence, the inspection of surface roughness of the work piece is very important to assess the quality of a component. The quality of surface roughness is an important requirement for coated parts [18]. There are at least five different mechanisms by using the surface modification methods to increase the fretting resistance: (1) to induce a residual compressive stress; (2) to decrease the coefficient of friction; (3) to increase the surface hardness; (4) to alter the surface chemistry; (5) to increase the surface roughness [19].

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Skewness and kurtosis of height distribution of thin films simulated by larger curvature model with noise reduction techniques

Disrattakit

Chanphana

Chatraphorn

2017

Physica A: Statistical Mechanics and its Applications

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Roughness distribution of multiple hit and long surface diffusion length noise reduced discrete growth models

Cited by 6 publications

References 30 publications

Local roughness exponent in the nonlinear molecular-beam-epitaxy universality class in one dimension

Local roughness exponent in the nonlinear molecular-beam-epitaxy universality class in one dimension

Effect of Salt Bath Nitriding on Surface Roughness Behaviour of Aisi 4140 Steel

Skewness and kurtosis of height distribution of thin films simulated by larger curvature model with noise reduction techniques

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