On line surface roughness measurements using image processing towards an adaptive control

Younis, Mona

doi:10.1016/s0360-8352(98)00017-5

Cited by 42 publications

(19 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, real-time measuring of surface roughness is difficult. Second, direct physical contact with high contact pressure is indispensable and the line samplings that may not necessarily represent the real characteristics of the surface [2]. Furthermore, the surface topography can only reproduced as a one-or two-dimensional profile and the overall measurements are extremely time-consuming processes.…”

Section: Introductionmentioning

confidence: 99%

Improvement of the quality factor of RF integrated inductors by layout optimization

López-Villegas

Samitier

Cané

et al. 2000

IEEE Trans. Microwave Theory Techn.

239

107

View full text Add to dashboard Cite

Abstract-A systematic method to improve the quality ( ) factor of RF integrated inductors is presented in this paper. The proposed method is based on the layout optimization to minimize the series resistance of the inductor coil, taking into account both ohmic losses, due to conduction currents, and magnetically induced losses, due to Eddy currents. The technique is particularly useful when applied to inductors in which the fabrication process includes integration substrate removal. However, it is also applicable to inductors on low-loss substrates. The method optimizes the width of the metal strip for each turn of the inductor coil, leading to a variable strip-width layout. The optimization procedure has been successfully applied to the design of square spiral inductors in a silicon-based multichip-module technology, complemented with silicon micromachining postprocessing. The obtained experimental results corroborate the validity of the proposed method. A factor of about 17 have been obtained for a 35-nH inductor at 1.5 GHz, with values higher than 40 predicted for a 20-nH inductor working at 3.5 GHz. The latter is up to a 60% better than the best results for a single strip-width inductor working at the same frequency.Index Terms-Inductor layout optimization, integrated RF inductor, silicon micromachining for RF applications, silicon RFIC's.

show abstract

Section: Introductionmentioning

confidence: 99%

Improvement of the quality factor of RF integrated inductors by layout optimization

López-Villegas

Samitier

Cané

et al. 2000

IEEE Trans. Microwave Theory Techn.

239

107

View full text Add to dashboard Cite

show abstract

“…The non contact surface roughness measurement gains popularity among research community [2][3][4][5].In the recent years, the development of machine vision hardware and user friendly image processing technology has started new possibilities of surface roughness measurement. The surface roughness prediction is made possible with the features extracted from the surface image [6].…”

Section: Introductionmentioning

confidence: 99%

Study On the Relationship between Surface Roughness of AA6061 Alloy End Milling and Image Texture Features of Milled Surface

Nathan

Thanigaiyarasu²,

Vani

2014

Procedia Engineering

View full text Add to dashboard Cite

The Surface roughness and image texture features of milled surfaces are key parameters to study the surface characteristics of end milled AA 6061 alloy. A Machine vision system is employed to capture and store the images of the end milled workpieces.The stylus type instrument is used measure the surface roughness values of various milled workpieces for different cutting conditions such as speed, feed and depth of cut.The Grey Level Cooccurance Matrix [GLCM] is introduced to extract the image texture features of the end milled surfaces. Four Matrices about different sampling orientations are builtup to determine the various image texure features such as contrast, homogenity, correlation and energy. A regression analysis is performed between image texture features and surface roughness values of the machined surfaces. Finally, the relationship between surface roughness and image texture features has been established.

show abstract

“…When the surface roughness falls below 2.5 μm, the stylus instruments are affected by large system error. The major disadvantage for such methods is that they require directed physical contact and line sampling which may not represent the real characteristics of the surface [4].…”

Section: Introductionmentioning

confidence: 99%

Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform

Palani¹,

Natarajan

2010

Int J Adv Manuf Technol

View full text Add to dashboard Cite

This paper presents a system for automated, noncontact, and flexible prediction of surface roughness of endmilled parts through a machine vision system which is integrated with an artificial neural network (ANN). The images of milled surface grabbed by the machine vision system could be extracted using the algorithm developed in this work, in the spatial frequency domain using a twodimensional Fourier transform to get the features of image texture (major peak frequency F 1 , principal component magnitude squared value F 2 , and the average gray level G a ). Since F1 is the distance between the major peak and the origin, it is a robust measure to overcome the effect of lighting of the environment. The periodically occurring features such as feed marks and tool marks present in the gray-level image can be easily observed from the principal component magnitude squared value F 2. The experimental machining variables speed S, feedrate F, depth of cut D, and the response extracted image variables F 1 , F 2 , and G a could be used as input data, and the response surface roughness R a measured by Surfcorder SE-1100 (traditional stylus method) could be used as output data of an ANN ability to construct the relationships between input and output variables. The ANN was trained using the back-propagation algorithm developed in this work due to its superior strength in pattern recognition and reasonable speed. Using the trained ANN, the experimental result had shown that the surface roughness of milled parts predicted by machine vision system over a wide range of machining conditions could be got with a reasonable accuracy compared with those measured by traditional stylus method. Compared with the stylus method, the constructed machine vision system is a useful method for prediction of the surface roughness faster, with a lower price, and lower environment noise in manufacturing process. Experimental results have shown that the proposed machine vision system can be implemented for automated prediction of surface roughness with accuracy of 97.53%. The results are encouraging that machine vision system can be extended to many real-time industrial prediction applications.

show abstract

On line surface roughness measurements using image processing towards an adaptive control

Cited by 42 publications

References 2 publications

Improvement of the quality factor of RF integrated inductors by layout optimization

Improvement of the quality factor of RF integrated inductors by layout optimization

Study On the Relationship between Surface Roughness of AA6061 Alloy End Milling and Image Texture Features of Milled Surface

Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform

Contact Info

Product

Resources

About