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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Veröffentlicht in:	Measurement : journal of the International Measurement Confederation Jg. 164; S. 108011
Hauptverfasser:	Prabhakar, D.V.N., Sreenivasa Kumar, M., Gopala Krishna, A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Elsevier Ltd 01.11.2020 Elsevier Science Ltd
Schlagworte:	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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Abstract	•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.
AbstractList	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. •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.
ArticleNumber	108011
Author	Sreenivasa Kumar, M. Prabhakar, D.V.N. Gopala Krishna, A.
Author_xml	– sequence: 1 givenname: D.V.N. surname: Prabhakar fullname: Prabhakar, D.V.N. email: dvnprabhakarvasavi@gmail.com organization: Department of Mechanical Engineering, Sri Vasavi Engineering College (JNTUK), Tadepalligudem, West Godavari Dist., 534101, India – sequence: 2 givenname: M. surname: Sreenivasa Kumar fullname: Sreenivasa Kumar, M. organization: Department of Mechanical Engineering, Narasaraopeta Engineering College, Narasaraopeta, Guntur Dist., 522601, India – sequence: 3 givenname: A. surname: Gopala Krishna fullname: Gopala Krishna, A. organization: Department of Mechanical Engineering, University College of Engineering, JNTUK, Kakinada, East Godavari Dist., 533001, India
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Snippet	•A novel hybrid shearlet application to characterize the surface integrity is proposed.•Integrated Fourier-Wavelet-Shearlet transform is used for predicting... Milled surfaces contain features of geometrically similar appearances under various magnifications in different orientations. Fourier transform is useful in...
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SubjectTerms	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
Title	A Novel Hybrid Transform approach with integration of Fast Fourier, Discrete Wavelet and Discrete Shearlet Transforms for prediction of surface roughness on machined surfaces
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