A Novel Hybrid Transform approach with integration of Fast Fourier, Discrete Wavelet and Discrete Shearlet Transforms for prediction of surface roughness on machined surfaces

•A novel hybrid shearlet application to characterize the surface integrity is proposed.•Integrated Fourier-Wavelet-Shearlet transform is used for predicting surface roughness.•Prediction error of MLFFANN training with cutting and vision parameters is less.•Validation roughness error of proposed meth...

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Vydáno v:Measurement : journal of the International Measurement Confederation Ročník 164; s. 108011
Hlavní autoři: Prabhakar, D.V.N., Sreenivasa Kumar, M., Gopala Krishna, A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Elsevier Ltd 01.11.2020
Elsevier Science Ltd
Témata:
Aluminum
Artificial neural networks
Cutting parameters
Directional sensitivity
Discrete element method
Discrete Wavelet Transform
End milling
Experimentation
Fast Fourier transformations
Fourier transforms
High speed tool steels
Hybrid transform
Machine vision
Neural networks
Numerical controls
Orthogonal arrays
Shearlets
Studies
Surface roughness
Wavelet transforms
Hybrid transform
Surface roughness
Machine vision
Artificial neural networks
Shearlets
ISSN:0263-2241, 1873-412X
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Shrnutí:•A novel hybrid shearlet application to characterize the surface integrity is proposed.•Integrated Fourier-Wavelet-Shearlet transform is used for predicting surface roughness.•Prediction error of MLFFANN training with cutting and vision parameters is less.•Validation roughness error of proposed methodology is within acceptable limits.•FFT_DWT_DST model showed better prediction accuracy than conventional methods. Milled surfaces contain features of geometrically similar appearances under various magnifications in different orientations. Fourier transform is useful in obtaining accurate information from periodic data than wavelets, wavelets are effective in handling multi-resolution data than fourier transform, shearlets show much higher directional sensitivity than both fourier transform and wavelets but are computationally complex. Hence a novel hybrid transform approach with integration of Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT) and Discrete Shearlet Transform (DST) to characterize surface roughness on machined surfaces is presented. For the experimentation, Taguchi’s L9 orthogonal array was used on Computer Numerical Control (CNC) mill with High Speed Steel (HSS) end mill to machine aluminum 3025 alloy work samples. The Artificial Neural Network is trained with cutting parameters, vision parameters as input and the experimental surface roughness (Ra, Rq, Rz) as output. The validation results of proposed models show better performance with reasonable accuracy.
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ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2020.108011