Fractal analysis and atomic force microscopy measurements of surface roughness for Hastelloy C276 substrates and amorphous alumina buffer layers in coated conductors

Feng, Feng; Shi, Kai; Xiao, Shaozhu; Zhang, Y.-Y.; Zhao, Z.J.; Wang, Zhi; Wei, Jun; Han, Zhonghe

doi:10.1016/j.apsusc.2011.11.103

Cited by 30 publications

(14 citation statements)

References 28 publications

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“…In RSE algorithm, R q -L relationships are calculated by segmenting the profiles into sub-sequences with various L, and the procedure of flatten modification on the sub-sequences plays an important role. As shown in Figure 2, the R q -L curves obtained without flatten modification (denoted by f0) are not entirely linear in double logarithm coordinates, which was consistent with our previous studies on surface morphologies [31], [40].…”

Section: A Fractal Analysis On Artificial Profiles Optimization Of Rsupporting

confidence: 91%

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Fractal Analysis on Artificial Profiles and Electroencephalography Signals by Roughness Scaling Extraction Algorithm

et al. 2019

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Electroencephalography (EEG) was widely investigated in brain status detection and disease diagnosis, in which the fractal analysis played an important role. In this paper, the roughness scaling extraction (RSE) algorithm proposed in our previous study on surface morphologies was applied to calculate the fractal dimensions (FDs) of artificial profiles and EEG signals. Fractal profiles with ideal FDs ranging from 1.01 to 1.99 were generated through the Weierstrass-Mandelbrot function. The RSE algorithm and the traditional algorithms, including the Higuchi algorithm, the Katz algorithm, and the box counting algorithm, were compared by analyzing the artificial profiles. Based on the mean relative errors and mean square errors, it was found that the RSE algorithm was more accurate than the traditional algorithms. To investigate the influence of noise on FD calculation, noise with different levels was added to the fractal profiles. The RSE and Higuchi algorithms were found reliable at signal-to-noise ratios of 50 and 40 dB, while the accuracy of RSE was also superior to that of the Higuchi. The RSE, Higuchi, and Katz algorithms were utilized to analyze the EEG signals of epilepsy events. The significant FD increasing, which corresponded to the seizure onset, could be detected, and the overlapping between the seizure and non-seizure statuses was small by using the RSE algorithm, indicating its feasibility for the EEG fractal analysis.

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Section: A Fractal Analysis On Artificial Profiles Optimization Of Rsupporting

confidence: 91%

“…RSE algorithm was proposed based on the scaling behavior of roughness values [31], [40], which is usually quantified by root-mean-squared roughness (R q , also known as RMS).…”

Section: Rse Algorithmmentioning

confidence: 99%

Fractal Analysis on Artificial Profiles and Electroencephalography Signals by Roughness Scaling Extraction Algorithm

et al. 2019

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“…AFM measurements were conducted using the tapping mode, and the AFM data was processed with the second order flatten to eliminate the unwanted features caused by the incline of the measured samples and other factors. 21 Scanning electronic microscope (SEM) of Leo 1530 was employed to reveal the surface morphologies of both CeO 2 and YBCO films at scales larger than AFM. And the thickness of YBCO film was also obtained by observing the cross-section using SEM.…”

Section: Methodsmentioning

confidence: 99%

Surface characterization of as-grown CeO<SUB>2</SUB> cap layer morphology evolution and critical current density of post-deposited YBCO films

Huang¹,

Shi²,

Zhang³

et al. 2015

Mat Express

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Cerium dioxide (CeO 2 ) cap layers for high temperature superconducting coated conductors were fabricated on yttria-stabilized zirconia (YSZ) single crystal substrates via the unbalanced radio frequency (RF) magnetron sputtering method. YBa 2 Cu 3 O 7− (YBCO) films were epitaxially deposited on the as-grown CeO 2 /YSZ (001) stacks using water-free metal organic deposition (MOD) method. The surface morphology evolution of CeO 2 cap layers could be tuned by adjusting the sputtering pressure, from smooth and uniformly granular, to rough and composed of spindle-like islands. According to the atomic force microscopy (AFM) data, a monotonic relationship could be found between the root mean squared (RMS) roughness value and flat area fraction. The critical current density (J c ) of the MOD-YBCO film varied significantly along with the CeO 2 morphology evolution, caused by the degradation of YBCO film's epitaxial growth. It was found that the as-grown CeO 2 cap layer deposited at 0.5 Pa could serve as a favorable template and did not require post-modification, on which YBCO film with J c of 1.92 MA/cm 2 (77 K, self-field) could be obtained. The MOD process would be improved in our future study by parameter optimization to enhance the J c property of YBCO films.

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“…(2015) proposed a hybrid scheme of difference analysis and dynamic weights anchored on a feedback concept for prediction of surface roughness of machined surfaces. Feng et al. (2012) undertook a study focused at measuring both RMS and Ra readings for a cast alloy substrate with alumina buffered layer surfaces while Bajić et al.…”

Section: Introductionmentioning

confidence: 99%

Surface roughness prediction using a hybrid scheme of difference analysis and adaptive feedback weights

Ayomoh

Abou-El-Hossein

2021

Heliyon

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This research has presented an optimum model for surface roughness prediction in a shop floor machining operation. The proposed solution is premised on difference analysis enhanced with a feedback control model capable of generating transient adaptive weights until a converging set point is attained. The surface roughness results utilized herein were adopted from two prior experiments in the literature. The design of experiment herein is premised on three cutting parameters in both experimental scenarios viz: feed rate, cutting speed and depth of cut for experimental dataset one and cutting speed, feed rate and flow rate for experimental dataset two. Three experimental levels were considered in both scenarios resulting in twenty-seven outcomes each. The simulation trial anchored on Matlab software was divided into two sub-categories viz: prediction of surface roughness for cutting combinations with vector points off the edges of the mesh referred to as off-edge cutting combinations (Off-ECC) and recovery of cutting combinations with positions on the edges of the mesh referred to as on-edge cutting combinations (On-ECC). The proposed hybrid scheme of difference analysis with feedback control premised on the use of dynamic weights produced an accurate output in comparison with the abductive, regression analysis and artificial neural network techniques as earlier utilized in the literature. The novelty of the proposed hybrid model lies in its high degree of prediction and recovery of existing datasets with an error margin approximately zero. This predictive efficacy is premised on the use of set points and transient dynamic weights for feedback iterations. The proposed solution technique in this research is quite consistent with its outputs and capable of working with very small to complex datasets.

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Fractal analysis and atomic force microscopy measurements of surface roughness for Hastelloy C276 substrates and amorphous alumina buffer layers in coated conductors

Cited by 30 publications

References 28 publications

Fractal Analysis on Artificial Profiles and Electroencephalography Signals by Roughness Scaling Extraction Algorithm

Fractal Analysis on Artificial Profiles and Electroencephalography Signals by Roughness Scaling Extraction Algorithm

Surface characterization of as-grown CeO<SUB>2</SUB> cap layer morphology evolution and critical current density of post-deposited YBCO films

Surface roughness prediction using a hybrid scheme of difference analysis and adaptive feedback weights

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