A new representation with probability distribution for nanometric surface roughness in ultra-precision machining

S. J. Zhang; Suet To; S. J. Wang; G. Q. Zhang

doi:10.1016/j.precisioneng.2016.02.009

A new representation with probability distribution for nanometric surface roughness in ultra-precision machining

S. J. Zhang, Suet To, S. J. Wang, G. Q. Zhang

Research output: Journal article publication › Journal article › Academic research › peer-review

7 Citations (Scopus)

Abstract

Ultra-precision machining (UPM) commonly produces nanometric surface roughness (NSR), which is governed by high-frequency components with tool marks sensitive to noise. Its spacing features (SF) majorly affect optical quality by diffraction and interference. However, the ISO SR standard cannot effectively represent SF. In this study, a new representation for SF was developed by evaluating surface derivative, as extra SR parameters. Probability distribution with the 95-99 rule was adopted to reduce noise effects. The results were found that the extra SR parameters well represents SF and are sensitive to spatial frequency. Probability distribution is an efficient means of reducing noise effects. Significantly, the proposed method is simple and efficient to represent SF of NSR in UPM.

Original language	English
Pages (from-to)	445-449
Number of pages	5
Journal	Precision Engineering
Volume	45
DOIs	https://doi.org/10.1016/j.precisioneng.2016.02.009
Publication status	Published - 1 Jul 2016

Keywords

Probability distribution
Surface roughness
Ultra-precision machining

ASJC Scopus subject areas

General Engineering

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10.1016/j.precisioneng.2016.02.009

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title = "A new representation with probability distribution for nanometric surface roughness in ultra-precision machining",

abstract = "Ultra-precision machining (UPM) commonly produces nanometric surface roughness (NSR), which is governed by high-frequency components with tool marks sensitive to noise. Its spacing features (SF) majorly affect optical quality by diffraction and interference. However, the ISO SR standard cannot effectively represent SF. In this study, a new representation for SF was developed by evaluating surface derivative, as extra SR parameters. Probability distribution with the 95-99 rule was adopted to reduce noise effects. The results were found that the extra SR parameters well represents SF and are sensitive to spatial frequency. Probability distribution is an efficient means of reducing noise effects. Significantly, the proposed method is simple and efficient to represent SF of NSR in UPM.",

keywords = "Probability distribution, Surface roughness, Ultra-precision machining",

author = "Zhang, {S. J.} and Suet To and Wang, {S. J.} and Zhang, {G. Q.}",

year = "2016",

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AU - Zhang, S. J.

AU - To, Suet

AU - Wang, S. J.

AU - Zhang, G. Q.

PY - 2016/7/1

Y1 - 2016/7/1

N2 - Ultra-precision machining (UPM) commonly produces nanometric surface roughness (NSR), which is governed by high-frequency components with tool marks sensitive to noise. Its spacing features (SF) majorly affect optical quality by diffraction and interference. However, the ISO SR standard cannot effectively represent SF. In this study, a new representation for SF was developed by evaluating surface derivative, as extra SR parameters. Probability distribution with the 95-99 rule was adopted to reduce noise effects. The results were found that the extra SR parameters well represents SF and are sensitive to spatial frequency. Probability distribution is an efficient means of reducing noise effects. Significantly, the proposed method is simple and efficient to represent SF of NSR in UPM.

AB - Ultra-precision machining (UPM) commonly produces nanometric surface roughness (NSR), which is governed by high-frequency components with tool marks sensitive to noise. Its spacing features (SF) majorly affect optical quality by diffraction and interference. However, the ISO SR standard cannot effectively represent SF. In this study, a new representation for SF was developed by evaluating surface derivative, as extra SR parameters. Probability distribution with the 95-99 rule was adopted to reduce noise effects. The results were found that the extra SR parameters well represents SF and are sensitive to spatial frequency. Probability distribution is an efficient means of reducing noise effects. Significantly, the proposed method is simple and efficient to represent SF of NSR in UPM.

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DO - 10.1016/j.precisioneng.2016.02.009

M3 - Journal article

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JO - Precision Engineering

JF - Precision Engineering

ER -

A new representation with probability distribution for nanometric surface roughness in ultra-precision machining

Abstract

Keywords

ASJC Scopus subject areas

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