Wavelet and fractal approach to surface roughness characterization after finish turning of different workpiece materials

Grzesik, Wit; Brol, S.

doi:10.1016/j.jmatprotec.2008.06.009

Cited by 64 publications

(21 citation statements)

References 11 publications

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“…16,18. Calculation algorithm for the fractal dimension D was basically introduced by Sevcik (1998) and further it was applied by Grzesik and Brol (2009) for roughness analysis purposes. The fractal dimensional value D is an index of the complicated morphological surface.…”

Section: Power Spectrum Density (Psd) Methodsmentioning

confidence: 99%

“…The Rms roughness estimated from the AFM data did not show either monotonic increase or decrease with ion influences. Grzesik et al (2009) used the surface profiles generated in longitudinal turning operations characterized using continuous wavelet transform (CWT) and normalized fractal dimension Dn. The wavelet transform together with fractal dimension can be capable of the detection of local selfsimilarity in the surface profile.…”

Section: Introductionmentioning

confidence: 99%

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Fractal dimensional surface analysis of AISI D2 Tool steel material with nanofluids in grinding process using atomic force microscopy

Prabhu¹,

Vinayagam²

2011

J. Braz. Soc. Mech. Sci. & Eng.

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Section: Power Spectrum Density (Psd) Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fractal dimensional surface analysis of AISI D2 Tool steel material with nanofluids in grinding process using atomic force microscopy

Prabhu¹,

Vinayagam²

2011

J. Braz. Soc. Mech. Sci. & Eng.

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“…The fractal dimensions of the EDM machined surface with CNT based dielectric fluids are evaluated from the slope of the plot of log P versus log(S) shown in Figures 8-11. Calculation algorithm for the fractal dimension (D) was applied by Grzesik and Brol [17] for roughness analysis purposes. The fractal dimensional value D is an index of the complicated morphological surface.…”

Section: Fractal Analysismentioning

confidence: 99%

Multiresponse Optimization of Edm Process with Nanofluids Using Topsis Method and Genetic Algorithm

Prabhu¹,

Vinayagam²

2016

Archive of Mechanical Engineering

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Electrical Discharge Machining (EDM) process with copper tool electrode is used to investigate the machining characteristics of AISI D2 tool steel material. The multi-wall carbon nanotube is mixed with dielectric fluids and its end characteristics like surface roughness, fractal dimension and metal removal rate (MRR) are analysed. In this EDM process, regression model is developed to predict surface roughness. The collection of experimental data is by using L9 Orthogonal Array. This study investigates the optimization of EDM machining parameters for AISI D2 Tool steel using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Analysis of variance (ANOVA) and F-test are used to check the validity of the regression model and to determine the significant parameter affecting the surface roughness. Atomic Force Microscope (AFM) is used to capture the machined image at micro size and using spectroscopy software the surface roughness and fractal dimensions are analysed. Later, the parameters are optimized using MINITAB 15 software, and regression equation is compared with the actual measurements of machining process parameters. The developed mathematical model is further coupled with Genetic Algorithm (GA) to determine the optimum conditions leading to the minimum surface roughness value of the workpiece.

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“…Moreover, there are many kinds of wavelet bases for modeling wave-front aberration. For example, wavelet method can find its application in signal reconstruction of optical systems in [4][5][6][7] wave-front aberration, Xie et al [8][9][10] studies the mapping of wavelet factor and practical parameters in optical systems, and then get the quantitative relationship between wavelet factor and system performance. As the primary element in wavelet method, the selection of bases functions is of great importance since modeling accuracy is largely depended on the selection of bases functions.…”

Section: Introductionmentioning

confidence: 99%

Wave-front aberration modeling based on wavelet methods

Liu

Xie

Lin

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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Wave-front aberration has serious adverse influence on the efficiency of optical system whose modeling problem is of great importance for further research. In this paper, wavelet method, which has a broad application in signal analysis, is used to model wave-front aberration. As the basis of wavelet method, the selection of basis function is the primary step for signal reconstruction, and two types of wave-front aberration are studied in this paper. First, for general wave-front aberration, a selection algorithm is given by comparing the minimum Mean Square Error (MSE).Secondly we take into account the primary wave-front aberration represented by finite Zernike polynomials, a simplified selection algorithm is proposed via minimum Maximum Mean Error Ratio (MMER) criterion. Numerical results shows that basis function selection algorithm via minimum MMER criterion can ensure the modeling accuracy, and it costs much smaller calculation than minimum MSE criterion.

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Wavelet and fractal approach to surface roughness characterization after finish turning of different workpiece materials

Cited by 64 publications

References 11 publications

Fractal dimensional surface analysis of AISI D2 Tool steel material with nanofluids in grinding process using atomic force microscopy

Fractal dimensional surface analysis of AISI D2 Tool steel material with nanofluids in grinding process using atomic force microscopy

Multiresponse Optimization of Edm Process with Nanofluids Using Topsis Method and Genetic Algorithm

Wave-front aberration modeling based on wavelet methods

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