Comparison of optical and stylus methods for measurement of surface texture

Vorburger, Theodore V.; Rhee, Hyug-Gyo; Renegar, Thomas B.; Song, J.-F.; Zheng, Alan

doi:10.1007/s00170-007-0953-8

Cited by 175 publications

(136 citation statements)

References 15 publications

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“…In stylus profilometry, the stylus profiler touches mechanically the surface, and the vertical motion is then transferred into electrical signal which represents the surface topography (Vorburger et al 2007). Since there is a mechanical contact between surface and stylus, so it can cause damage to the surface of the sample (Lindroos et al 2010;Vorburger et al 2007). …”

Section: Surface Roughness Measurementsmentioning

confidence: 99%

Surface roughness analysis of SiO2 for PECVD, PVD and IBD on different substrates

et al. 2015

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This study compares surface roughness of SiO 2 thin layers which are deposited by three different processes (plasma-enhanced chemical vapor deposition, physical vapor deposition and ion beam deposition) on three different substrates (glass, Si and polyethylene naphthalate). Plasma-enhanced chemical vapor deposition (PECVD) processes using a wide range of deposition temperatures from 80 to 300°C have been applied and compared. It was observed that the nature of the substrate does not influence the surface roughness of the grown layers very much. It is also perceived that the value of the surface roughness keeps on increasing as the deposition temperature of the PECVD process increases. This is due to the increase in the surface diffusion length with the rise in substrate temperature. The layers which have been deposited on Si wafer by ion beam deposition (IBD) process are found to be smoother as compared to the other two techniques. The layers which have been deposited on the glass substrates using PECVD reveal the highest surface roughness values in comparison with the other substrate materials and techniques. Different existing models describing the dynamics of clusters on surfaces are compared and discussed.

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Section: Surface Roughness Measurementsmentioning

confidence: 99%

Surface roughness analysis of SiO2 for PECVD, PVD and IBD on different substrates

et al. 2015

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“…A general difficulty of interferometric measurement of rough surfaces is the speckle effect, which leads to phase ambiguity. As a consequence, interferometric sensors which evaluate the phase of an occurring interference signal often fail on rough surfaces as it was reported previously by Vorburger et al [14]. Therefore, this contribution is intended to study the applicability of our interferometric point sensor to surface roughness measurement.…”

Section: Introductionmentioning

confidence: 72%

“…In ref. [14] lineprofiling tactile stylus methods are compared with different optical areal methods. Phaseshifting interferometry, white-light interferometry and confocal microscopes were used to determine the roughness average value of multiple frequently used roughness standards.…”

Section: Determination Of Surface Roughnessmentioning

confidence: 99%

“…The standards [5,6] also propose a traversing length for which the sensor acquires samples and an evaluation length of where the evaluation takes place after filters were applied. In our experiment we focus on two commonly used parameters to describe surface roughness, the roughness average value also referred in [14] and the average maximum height of the profile . The roughness average is defined as [5,8]:…”

Section: Determination Of Surface Roughnessmentioning

confidence: 99%

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High-resolution fiber-coupled interferometric point sensor for micro- and nano-metrology

Schake¹,

Schulz²,

Lehmann³

2015

Tm - Technisches Messen

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The determination of surface roughness is a common challenge in industrial quality assurance. Because tactile techniques like the stylus method or atomic force microscopy run the risk of damaging the measurement object there is a high demand for contact free optical measurements. In this contribution we demonstrate the feasibility of a high resolution fibercoupled interferometric point sensor with periodical path length modulation to determine the surface profile of rough surfaces. Measurements on two specimens characterized by different roughness parameters are presented and corrections for common measurement errors, due to phase ambiguity are discussed.

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“…The traditional stylus method is the most widely used technique in industry. A precision diamond stylus is drawn through the surface being detected and the perpendicular motion is amplified electronically [1][2][3]. The accuracy of stylus method depends on the radii of diamond tips.…”

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

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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.

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Comparison of optical and stylus methods for measurement of surface texture

Cited by 175 publications

References 15 publications

Surface roughness analysis of SiO2 for PECVD, PVD and IBD on different substrates

Surface roughness analysis of SiO2 for PECVD, PVD and IBD on different substrates

High-resolution fiber-coupled interferometric point sensor for micro- and nano-metrology

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

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