Measurement and evaluation of surface roughness based on optic system using image processing and artificial neural network

The traditional devices, used to measure the surface roughness, are very sensitive, and they are obtained by scratching the surface of materials. Therefore, the optic systems are used as alternatives to these devices to avoid the unwanted processes that damage the surface. In this study, face milling process was applied to American Iron and Steel Institute (AISI) 1040 carbon steel and aluminium alloy 5083 materials using the different tools, cutting speeds and depth of cuts. After these processes, surface roughness values were obtained by the surface roughness tester, and the machined surface images were taken using a polarise microscope. The obtained images were converted into binary images, and the images were used as input data to train network using the MATLAB neural network toolbox. For the training networks, log-sigmoid function was selected as transfer function, scaled conjugate gradient (SCG) algorithm was used as training algorithm, and performance of the trained networks was achieved as an average of 99.926 % for aluminium alloy (AA) 5083 aluminium and as an average of 99.932 % for AISI 1040 steel. At the end of the study, a prediction programme for optical surface roughness values using MATLAB m-file and GUI programming was developed. Then, the prediction programme and neural network performance were tested by the trial experiments. After the trial experiments, surface roughness values obtained with stylus technique for the carbon steel and aluminium alloy materials were compared with the developed programme values. When the developed programme values were compared with the experimental results, the results were confirmed each other at a rate of 99.999 %.

[1]  D. Dowson,et al.  Experimental methods in tribology , 2004 .

[2]  Mani Maran Ratnam,et al.  Edge detection and measurement of nose radii of cutting tool inserts from scanned 2-D images , 2012 .

[3]  D. P. Caetano,et al.  Effect of metallic surface roughness on the speckle pattern formation at diffraction plane , 2012 .

[4]  Colin Bradley,et al.  Surface Texture Indicators of Tool Wear - A Machine Vision Approach , 2001 .

[5]  Zhongyu Wang,et al.  Novel method for evaluating surface roughness by grey dynamic filtering , 2010 .

[6]  Lihui Wang,et al.  Machine-vision-based surface finish inspection for cutting tool replacement in production , 2004 .

[7]  Fouad El-Diasty,et al.  Analysis of speckle images to assess surface roughness , 2004 .

[8]  G A H Al-Kindi,et al.  An application of machine vision in the automated inspection of engineering surfaces , 1992 .

[9]  B. Dhanasekar,et al.  Restoration of blurred images for surface roughness evaluation using machine vision , 2010 .

[10]  U. Persson,et al.  Surface Roughness Measurement on Machined Surfaces using Angular Speckle Correlation , 2006 .

[11]  Xuezeng Zhao,et al.  Roughness measurement of moving weak-scattering surface by dynamic speckle image , 2012 .

[12]  B. Ramamoorthy,et al.  The influence of component inclination on surface finish evaluation using digital image processing , 2007 .

[13]  Surjya K. Pal,et al.  Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique , 2012 .

[14]  Chen Zhang,et al.  On-line tool wear measurement for ball-end milling cutter based on machine vision , 2013, Comput. Ind..

[15]  Du-Ming Tsai,et al.  A vision system for surface roughness assessment using neural networks , 1998 .

[16]  Hasan Öktem,et al.  Measurement of surface roughness of metals using binary speckle image analysis , 2010 .

[17]  F. Luk,et al.  Measurement of surface roughness by a machine vision system , 1989 .

[18]  K. Palanikumar,et al.  Surface Roughness Parameters Evaluation in Machining GFRP Composites by PCD Tool using Digital Image Processing , 2009 .

[19]  O. Borysenko,et al.  Binary Image Compression Based on Binomial Numbers , 2010 .

[20]  E. S. Gadelmawla,et al.  A vision system for surface roughness characterization using the gray level co-occurrence matrix , 2004 .

[21]  S. Jetley,et al.  Applying machining vision to surface texture analysis , 1993, Proceedings of 36th Midwest Symposium on Circuits and Systems.

[22]  Zahide Yilbas,et al.  Surface roughness measurement using an optical system , 1999 .

[23]  Bijan Shirinzadeh,et al.  Feasibility assessment of vision-based surface roughness parameters acquisition for different types of machined specimens , 2009, Image Vis. Comput..

[24]  Kwang Ho Kim,et al.  Fractal dimension analysis of machined surface depending on coated tool wear , 2005 .

[25]  T. Jeyapoovan,et al.  Surface roughness classification using image processing , 2013 .

[26]  F. Klocke.  Manufacturing Processes 1 , 2011 .

[27]  Ahmet Murat Pinar,et al.  Optimization of Process Parameters with Minimum Surface Roughness in the Pocket Machining of AA5083 Aluminum Alloy via Taguchi Method , 2013 .

[28]  Xiang Zhang,et al.  Automatic classification of defects on the product surface in grinding and polishing , 2006 .

[29]  Ship-Peng Lo,et al.  Rapid measurement of surface roughness for face-milling aluminum using laser scattering and the Taguchi method , 2005 .

[30]  Christophe Tournier,et al.  In situ non-contact measurements of surface roughness , 2012 .

[31]  Jakkapol Visessamit,et al.  3-D SURFACE ROUGHNESS PROFILE OF 316-STAINLESS STEEL USING VERTICAL SCANNING INTERFEROMETRY WITH A SUPERLUMINESCENT DIODE , 2012 .

[32]  John D. Kechagias,et al.  Parameter Optimization during Finish End Milling of Al Alloy 5083 using Robust Design , 2011 .

[33]  Basanta Bhaduri,et al.  Evaluation of surface roughness based on monochromatic speckle correlation using image processing , 2008 .

[34]  Hans Nørgaard Hansen,et al.  A non-contact 3D method to characterize the surface roughness of castings , 2013 .

[35]  Mohd Ruzaimi Mat Rejab,et al.  Surface Roughness Prediction Model of 6061-T6 Aluminium Alloy Machining Using Statistical Method , 2009 .

[36]  Yiin Kuen Fuh,et al.  Roughness measurement of metals using a modified binary speckle image and adaptive optics , 2012 .

[37]  M. B. Kiran,et al.  Evaluation of surface roughness by vision system , 1998 .

[38]  Zulkiflle Leman,et al.  Experimental Investigation of HSS Face Milling to AL6061 using Taguchi Method , 2012 .