In this work, a machine vision system has been utilized to capture the images and then the quantification of the surface roughness of machined surfaces (ground, milled and shaped) is done by the application of regression analysis, Subsequently, original images have been magnified using Cubic Convolution interpolation technique and improved (edge enhancement) through Linear Edge Crispening algorithm. Based on the surface image features, a parameter called Ga has been estimated using regression analysis, for the original images and for the magnified quality improved images. Finally, a comparison has been carried to establish correlation between magnification index, Ga and surface roughness. © 2004 Elsevier Ltd. All rights reserved.

B. Dhanasekar

B. Ramamoorthy

Algorithms

Computer vision

Correlation methods

Fiber optics

Fourier transforms

interpolation

quality control

Regression analysis

Sensors

Surface roughness

CUBIC CONVOLUTION

GREY LEVEL AVERAGE

Magnification factors

Process monitoring

Image processing

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

International Journal of Machine Tools and Manufacture

Appropriate lighting is one of the indispensable elements in inspection using machine vision system. Illumination variation affects the accuracy and robustness of an inspection method that employs a machine vision system. The lighting inhomogeneity is the disturbing signal that needed to be suppressed to achieve accuracy and consistency in surface roughness quantification. In this work, the illumination compensation techniques are used for ground surface roughness evaluation by statistical texture parameters using machine vision method. The three-dimensional (3-D) surface roughness parameters are compared with the texture parameters. The experimental results are based on the ground surface images that are machined at different machining parameters. After the grinding process, the images are captured under halogen lighting. The acquired images of ground specimens are used for illumination compensation using: homomorphic filtering, Discrete Cosine Transform (DCT) based filtering and Fourier Transform (FT) based filtering techniques. This helps to suppress the low frequency components and amplify the high frequency components in order to extract the texture information. Owing the fact that the ground surfaces were weaker anisotropic surfaces, the second order statistical evaluation methods are used to extract the changes in the image texture due to the variation in surface roughness of the component. The texture parameters evaluated using these methods are correlated with the 3-D surface roughness parameters measured using an optical profiler. The texture parameters showed better correlation with the measured roughness values and this can be an integral part of any grinding system to inspect the machined components. In order to establish the homogeneity achieved after compensation of images, the inhomogeneity indicator and harmonic distortion values are calculated for the ground images. © 2019 The Authors. Published by Elsevier B.V.

Fulltext

Procedia Manufacturing

Illumination Compensated images for surface roughness evaluation using machine vision in grinding process

In this paper, a method is proposed for the evaluation of image based Abbott-Firestone curve parameters aiming to characterize the cylinder bore surface topography using machine vision. Plateau honing experiments are performed to generate sixteen cylinder liners with different surface topographies and the 2-D and 3-D Abbott-Firestone parameters are measured using a stylus instrument and Coherence Scanning Interferometer (CSI), respectively. The images are captured from the corresponding portions of the cylinder liner surfaces using a Charge Coupled Device (CCD) camera connected with different microscopic attachments. The captured images are filtered using a Butterworth high pass filter followed by the adaptation of the double step Gaussian filtering procedure specified by the ISO 13565-1. An Abbott-Firestone curve is constructed by finding the cumulative of the intensity histogram of the filtered images. Five image based parameters are evaluated from the constructed Abbott curve by adapting the procedures presented in ISO 13565-2. The computed image based Abbott-Firestone curve parameters are observed to bear a statistically significant correlation with the measured 2-D and 3-D Abbott-Firestone curve parameters. An artificial neural network (ANN) is trained and tested to arrive at the actual values of the Abbott-Firestone curve parameters using the computed image based feature parameters. The results indicate that the multiple surface topography parameters of the cylinder bore surface could be estimated/predicted with a reasonable accuracy using machine vision technique coupled with ANN. © 2014 Elsevier Ltd. All rights reserved.

Measurement: Journal of the International Measurement Confederation

Evaluation of image based Abbott-Firestone curve parameters using machine vision for the characterization of cylinder liner surface topography

In this work, an attempt has been made to use a super resolution image processing algorithm for preprocessing the images over and above existing image quality enhancement techniques. The improved quality images processed using a machine vision system have been used to assess the quality of the surfaces. To ensure the validity of the approach the roughness values quantified using these images are then compared with widely accepted standard mechanical stylus instrument values. Quantification of digital images for surface roughness is performed using two Fourier transform parameters (major peak frequency and principal component magnitude squared value) and the standard deviation of gray level intensity values. Then the group method of data handling (GMDH) technique was used to obtain an analytical relationship of the roughness parameters calculated using the digital surface image and the stylus instrument values. We present in this paper an analysis based on the comparison to make sure that the present approach of estimation of surface finish based on the digital processed image could be implemented in practice. © 2006 Springer-Verlag London Limited.

International Journal of Advanced Manufacturing Technology

Assessment of surface roughness based on super resolution reconstruction algorithm

The surfaces produced by electrical discharge machine (EDM) were illuminated using a redline laser source and the speckle line images of the specimens were captured using a CMOS camera. Signal vectors were generated from the speckle line images which are 1 × 2592 matrices, represented the grey scale intensity of the speckle line images. The image signal vectors were then decomposed using a wavelet transform by key local intensity variation method. The fourth and the fifth levels of the six-level decomposition of bi-orthogonal wavelet were expected to bear the details about the surface roughness. The root mean square (RMS) and variance of the 4th and the 5th level decomposition were obtained and were compared with the surface roughness parameters roughness average (Ra), arithmetic mean slope (Rda), and root mean square slope (Rdq). RMS and variance of the 4th level decomposed signals were found to correlate well with surface roughness parameters. © 2019 Elsevier Ltd

Measurement

Surface roughness evaluation of electrical discharge machined surfaces using wavelet transform of speckle line images

An experimental approach for surface roughness measurement based on the speckle images caused by a laser beam on the milled surfaces and the white-light images of the same surfaces is presented. Since the surface slope at every point of the surface influences the speckle pattern, the surface roughness parameters Rda and Rdq were used for comparison. A CMOS camera, LASER and LED light sources were used for capturing speckle and white-light images of the milled surfaces. From the image pixel intensity matrix, a signal vector was generated and was used for the image metric. It is found that standard deviation and mean of the image signal vector correlate well with Ra, Rda and Rdq values measured by a standard Taylor Hobson surface roughness tester. The correlation was found to be better for speckle images than the white-light images. © Springer Nature Singapore Pte Ltd. 2019.

Lecture Notes in Mechanical Engineering Advances in Manufacturing Processes

Surface Roughness Evaluation of Milled Surfaces by Image Processing of Speckle and White-Light Images

Conventional measurement of finish using stylus instruments has been in vogue for over half a century. Though these instruments have excellent capabilities and wide range, they are normally used for inspecting machined surfaces of good finish. Further the procedure is a post process approach which is not amenable for automation. In the recent past, higher levels of automation in the shop floor has focused the attention on the application of fast, reliable and cost effective procedures for evaluating surfaces of medium finished parts. This paper deals with the possibility of doing this using a vision system. It briefly reviews a few approaches and examines one - texture unit spectra - in more detail. © 1998 Elsevier Science Ltd.

Evaluation of surface roughness by vision system

Tool wear detection has traditionally restricted itself to 2D study and measurement. A new technique for the measurement and visualisation of tool wear pattern has been presented in this paper. This method provides visualisation of the tool wear geometry using a pair of stereo images and generates the volume of crater wear as a new parameter for inspection. The results demonstrate that the volume of crater wear can be effectively used to characterise the tool wear. The average depth of the tool wear and surface area of the crater are also obtained as parameters for measurement. The technique provides a fast and a possible on-line method of tool wear analysis and measurement. © 1997 Elsevier Science Ltd.

3D tool wear measurement and visualisation using stereo imaging

With the advent of high speed general-purpose digital computers and vision systems, it is becoming easier to digitize any image and to analyse them for various applications. There are basically three possible approaches for characterizing the textural features from the digitized images: statistical, structural and spectral. Here in this work an attempt is made to characterize some of these features of texture produced by various machining operations, namely grinding, milling and shaping by processing the digital image of the texture pattern using statistical methods. © 1993.

Journal	International Journal of Machine Tools and Manufacture
ISSN	08906955
Open Access	No