Abstract

This paper presents an on-machine surface defect detection system using light scattering and deep learning. A supervised deep learning model is used to mine the information related to defects from light scattering patterns. A convolutional neural network is trained on a large dataset of scattering patterns that are predicted by a rigorous forward scattering model. The model is valid for any surface topography with homogeneous materials and has been verified by comparing with experimental data. Once the neural network is trained, it allows for fast, accurate, and robust defect detection. The system capability is validated on microstructured surfaces produced by ultraprecision diamond machining.

Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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  1. A. Malshe, K. Rajurkar, A. Samant, H. N. Hansen, S. Bapat, and W. Jiang, “Bio-inspired functional surfaces for advanced applications,” CIRP Ann. 62, 607–628 (2013).
    [Crossref]
  2. M. Liu, C. F. Cheung, X. Feng, and C. Wang, “Diamond machining of freeform-patterned surfaces on precision rollers,” Int. J. Adv. Manuf. Technol. 103, 4423–4431 (2019).
    [Crossref]
  3. X. Jiang, “In situ real-time measurement for micro-structured surfaces,” CIRP Ann. 60, 563–566 (2011).
    [Crossref]
  4. C. F. Cheung, M. Liu, R. K. Leach, X. Feng, and C. Zhao, “Hierarchical-information-based characterization of multiscale structured surfaces,” CIRP Ann. 67, 539–542 (2018).
    [Crossref]
  5. R. K. Leach, Optical Measurement of Surface Topography (Springer, 2011).
  6. F. Fang, Z. Zeng, X. Zhang, and L. Jiang, “Measurement of micro-V-groove dihedral using white light interferometry,” Opt. Commun. 359, 297–303 (2016).
    [Crossref]
  7. F. Gao, R. K. Leach, J. Petzing, and J. M. Coupland, “Surface measurement errors using commerciafl scanning white light interferometers,” Meas. Sci. Technol. 19, 015303 (2007).
    [Crossref]
  8. W. Gao, J. Aoki, B.-F. Ju, and S. Kiyono, “Surface profile measurement of a sinusoidal grid using an atomic force microscope on a diamond turning machine,” Precis. Eng. 31, 304–309 (2007).
    [Crossref]
  9. S. Goto, K. Hosobuchi, and W. Gao, “An ultra-precision scanning tunneling microscope Z-scanner for surface profile measurement of large amplitude micro-structures,” Meas. Sci. Technol. 22, 085101 (2011).
    [Crossref]
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    [Crossref]
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    [Crossref]
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  22. M. Thomas, R. Su, N. Nikolaev, J. Coupland, and R. K. Leach, “Modelling of interference microscopy beyond the linear regime,” Opt. Eng. 59, 034110 (2020).
    [Crossref]
  23. T. Sata, M. Li, S. Takata, H. Hiraoka, C. Li, X. Xing, and X. Xiao, “Analysis of surface roughness generation in turning operation and its applications,” CIRP Ann. 34, 473–476 (1985).
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    [Crossref]

2020 (1)

M. Thomas, R. Su, N. Nikolaev, J. Coupland, and R. K. Leach, “Modelling of interference microscopy beyond the linear regime,” Opt. Eng. 59, 034110 (2020).
[Crossref]

2019 (2)

M. Liu, C. F. Cheung, X. Feng, and C. Wang, “Diamond machining of freeform-patterned surfaces on precision rollers,” Int. J. Adv. Manuf. Technol. 103, 4423–4431 (2019).
[Crossref]

J. M. Coupland and N. I. Nikolaev, “A new approach to vector scattering: the 3s boundary source method,” Opt. Express 27, 30380–30395 (2019).
[Crossref]

2018 (2)

C. F. Cheung, M. Liu, R. K. Leach, X. Feng, and C. Zhao, “Hierarchical-information-based characterization of multiscale structured surfaces,” CIRP Ann. 67, 539–542 (2018).
[Crossref]

J. S. M. Madsen, S. A. Jensen, J. Nygård, and P. E. Hansen, “Replacing libraries in scatterometry,” Opt. Express 26, 34622–34632 (2018).
[Crossref]

2017 (1)

J. Liu, M. Osadchy, L. Ashton, M. Foster, C. J. Solomon, and S. J. Gibson, “Deep convolutional neural networks for Raman spectrum recognition: a unified solution,” Analyst 142, 4067–4074 (2017).
[Crossref]

2016 (2)

F. Fang, Z. Zeng, X. Zhang, and L. Jiang, “Measurement of micro-V-groove dihedral using white light interferometry,” Opt. Commun. 359, 297–303 (2016).
[Crossref]

M. H. Madsen and P.-E. Hansen, “Scatterometry—fast and robust measurements of nano-textured surfaces,” Surf. Topogr. Metrol. Prop. 4, 023003 (2016).
[Crossref]

2015 (1)

J. Schmidhuber, “Deep learning in neural networks: an overview,” Neural Netw. 61, 85–117 (2015).
[Crossref]

2013 (1)

A. Malshe, K. Rajurkar, A. Samant, H. N. Hansen, S. Bapat, and W. Jiang, “Bio-inspired functional surfaces for advanced applications,” CIRP Ann. 62, 607–628 (2013).
[Crossref]

2012 (1)

2011 (2)

X. Jiang, “In situ real-time measurement for micro-structured surfaces,” CIRP Ann. 60, 563–566 (2011).
[Crossref]

S. Goto, K. Hosobuchi, and W. Gao, “An ultra-precision scanning tunneling microscope Z-scanner for surface profile measurement of large amplitude micro-structures,” Meas. Sci. Technol. 22, 085101 (2011).
[Crossref]

2010 (1)

I. Simonsen, “Optics of surface disordered systems,” Eur. Phys. J. Spec. Top. 181, 1–103 (2010).
[Crossref]

2009 (1)

2007 (2)

F. Gao, R. K. Leach, J. Petzing, and J. M. Coupland, “Surface measurement errors using commerciafl scanning white light interferometers,” Meas. Sci. Technol. 19, 015303 (2007).
[Crossref]

W. Gao, J. Aoki, B.-F. Ju, and S. Kiyono, “Surface profile measurement of a sinusoidal grid using an atomic force microscope on a diamond turning machine,” Precis. Eng. 31, 304–309 (2007).
[Crossref]

1995 (1)

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20, 273–297 (1995).
[Crossref]

1985 (1)

T. Sata, M. Li, S. Takata, H. Hiraoka, C. Li, X. Xing, and X. Xiao, “Analysis of surface roughness generation in turning operation and its applications,” CIRP Ann. 34, 473–476 (1985).
[Crossref]

Aoki, J.

W. Gao, J. Aoki, B.-F. Ju, and S. Kiyono, “Surface profile measurement of a sinusoidal grid using an atomic force microscope on a diamond turning machine,” Precis. Eng. 31, 304–309 (2007).
[Crossref]

Ashton, L.

J. Liu, M. Osadchy, L. Ashton, M. Foster, C. J. Solomon, and S. J. Gibson, “Deep convolutional neural networks for Raman spectrum recognition: a unified solution,” Analyst 142, 4067–4074 (2017).
[Crossref]

Bapat, S.

A. Malshe, K. Rajurkar, A. Samant, H. N. Hansen, S. Bapat, and W. Jiang, “Bio-inspired functional surfaces for advanced applications,” CIRP Ann. 62, 607–628 (2013).
[Crossref]

Brodmann, B.

T. V. Vorburger, R. Silver, R. Brodmann, B. Brodmann, and J. Seewig, “Light scattering methods,” in Optical Measurement of Surface Topography (Springer, 2011), pp. 287–318.

Brodmann, R.

T. V. Vorburger, R. Silver, R. Brodmann, B. Brodmann, and J. Seewig, “Light scattering methods,” in Optical Measurement of Surface Topography (Springer, 2011), pp. 287–318.

Burger, S.

Cheung, C. F.

M. Liu, C. F. Cheung, X. Feng, and C. Wang, “Diamond machining of freeform-patterned surfaces on precision rollers,” Int. J. Adv. Manuf. Technol. 103, 4423–4431 (2019).
[Crossref]

C. F. Cheung, M. Liu, R. K. Leach, X. Feng, and C. Zhao, “Hierarchical-information-based characterization of multiscale structured surfaces,” CIRP Ann. 67, 539–542 (2018).
[Crossref]

Cortes, C.

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20, 273–297 (1995).
[Crossref]

Coupland, J.

M. Thomas, R. Su, N. Nikolaev, J. Coupland, and R. K. Leach, “Modelling of interference microscopy beyond the linear regime,” Opt. Eng. 59, 034110 (2020).
[Crossref]

Coupland, J. M.

J. M. Coupland and N. I. Nikolaev, “A new approach to vector scattering: the 3s boundary source method,” Opt. Express 27, 30380–30395 (2019).
[Crossref]

F. Gao, R. K. Leach, J. Petzing, and J. M. Coupland, “Surface measurement errors using commerciafl scanning white light interferometers,” Meas. Sci. Technol. 19, 015303 (2007).
[Crossref]

Fang, F.

F. Fang, Z. Zeng, X. Zhang, and L. Jiang, “Measurement of micro-V-groove dihedral using white light interferometry,” Opt. Commun. 359, 297–303 (2016).
[Crossref]

Feng, X.

M. Liu, C. F. Cheung, X. Feng, and C. Wang, “Diamond machining of freeform-patterned surfaces on precision rollers,” Int. J. Adv. Manuf. Technol. 103, 4423–4431 (2019).
[Crossref]

C. F. Cheung, M. Liu, R. K. Leach, X. Feng, and C. Zhao, “Hierarchical-information-based characterization of multiscale structured surfaces,” CIRP Ann. 67, 539–542 (2018).
[Crossref]

Foster, M.

J. Liu, M. Osadchy, L. Ashton, M. Foster, C. J. Solomon, and S. J. Gibson, “Deep convolutional neural networks for Raman spectrum recognition: a unified solution,” Analyst 142, 4067–4074 (2017).
[Crossref]

Gao, F.

F. Gao, R. K. Leach, J. Petzing, and J. M. Coupland, “Surface measurement errors using commerciafl scanning white light interferometers,” Meas. Sci. Technol. 19, 015303 (2007).
[Crossref]

Gao, W.

S. Goto, K. Hosobuchi, and W. Gao, “An ultra-precision scanning tunneling microscope Z-scanner for surface profile measurement of large amplitude micro-structures,” Meas. Sci. Technol. 22, 085101 (2011).
[Crossref]

W. Gao, J. Aoki, B.-F. Ju, and S. Kiyono, “Surface profile measurement of a sinusoidal grid using an atomic force microscope on a diamond turning machine,” Precis. Eng. 31, 304–309 (2007).
[Crossref]

Gibson, S. J.

J. Liu, M. Osadchy, L. Ashton, M. Foster, C. J. Solomon, and S. J. Gibson, “Deep convolutional neural networks for Raman spectrum recognition: a unified solution,” Analyst 142, 4067–4074 (2017).
[Crossref]

Goto, S.

S. Goto, K. Hosobuchi, and W. Gao, “An ultra-precision scanning tunneling microscope Z-scanner for surface profile measurement of large amplitude micro-structures,” Meas. Sci. Technol. 22, 085101 (2011).
[Crossref]

Hagness, S. C.

A. Taflove and S. C. Hagness, Computational Electrodynamics: The Finite-Difference Time-Domain Method (Artech House, 2005).

Hansen, H. N.

A. Malshe, K. Rajurkar, A. Samant, H. N. Hansen, S. Bapat, and W. Jiang, “Bio-inspired functional surfaces for advanced applications,” CIRP Ann. 62, 607–628 (2013).
[Crossref]

Hansen, P. E.

Hansen, P.-E.

M. H. Madsen and P.-E. Hansen, “Scatterometry—fast and robust measurements of nano-textured surfaces,” Surf. Topogr. Metrol. Prop. 4, 023003 (2016).
[Crossref]

Hiraoka, H.

T. Sata, M. Li, S. Takata, H. Hiraoka, C. Li, X. Xing, and X. Xiao, “Analysis of surface roughness generation in turning operation and its applications,” CIRP Ann. 34, 473–476 (1985).
[Crossref]

Hosobuchi, K.

S. Goto, K. Hosobuchi, and W. Gao, “An ultra-precision scanning tunneling microscope Z-scanner for surface profile measurement of large amplitude micro-structures,” Meas. Sci. Technol. 22, 085101 (2011).
[Crossref]

Jensen, S. A.

Jiang, L.

F. Fang, Z. Zeng, X. Zhang, and L. Jiang, “Measurement of micro-V-groove dihedral using white light interferometry,” Opt. Commun. 359, 297–303 (2016).
[Crossref]

Jiang, W.

A. Malshe, K. Rajurkar, A. Samant, H. N. Hansen, S. Bapat, and W. Jiang, “Bio-inspired functional surfaces for advanced applications,” CIRP Ann. 62, 607–628 (2013).
[Crossref]

Jiang, X.

X. Jiang, “In situ real-time measurement for micro-structured surfaces,” CIRP Ann. 60, 563–566 (2011).
[Crossref]

Jones, C. W.

R. K. Leach, C. W. Jones, B. Sherlock, and A. Krysinski, “The high dynamic range surface metrology challenge,” in 28th Annual Meeting of the American Society for Precision Engineering (ASPE) (2013), pp. 149–152.

Ju, B.-F.

W. Gao, J. Aoki, B.-F. Ju, and S. Kiyono, “Surface profile measurement of a sinusoidal grid using an atomic force microscope on a diamond turning machine,” Precis. Eng. 31, 304–309 (2007).
[Crossref]

Kato, A.

Kiyono, S.

W. Gao, J. Aoki, B.-F. Ju, and S. Kiyono, “Surface profile measurement of a sinusoidal grid using an atomic force microscope on a diamond turning machine,” Precis. Eng. 31, 304–309 (2007).
[Crossref]

Krysinski, A.

R. K. Leach, C. W. Jones, B. Sherlock, and A. Krysinski, “The high dynamic range surface metrology challenge,” in 28th Annual Meeting of the American Society for Precision Engineering (ASPE) (2013), pp. 149–152.

Leach, R. K.

M. Thomas, R. Su, N. Nikolaev, J. Coupland, and R. K. Leach, “Modelling of interference microscopy beyond the linear regime,” Opt. Eng. 59, 034110 (2020).
[Crossref]

C. F. Cheung, M. Liu, R. K. Leach, X. Feng, and C. Zhao, “Hierarchical-information-based characterization of multiscale structured surfaces,” CIRP Ann. 67, 539–542 (2018).
[Crossref]

F. Gao, R. K. Leach, J. Petzing, and J. M. Coupland, “Surface measurement errors using commerciafl scanning white light interferometers,” Meas. Sci. Technol. 19, 015303 (2007).
[Crossref]

R. K. Leach, C. W. Jones, B. Sherlock, and A. Krysinski, “The high dynamic range surface metrology challenge,” in 28th Annual Meeting of the American Society for Precision Engineering (ASPE) (2013), pp. 149–152.

R. K. Leach, Optical Measurement of Surface Topography (Springer, 2011).

M. Liu, N. Senin, and R. K. Leach, “Defect detection for structured surfaces via light scattering and machine learning,” in International Symposium on Measurement Technology and Intelligent Instruments (ISMTII), Niigata, Japan, 2019.

Li, C.

T. Sata, M. Li, S. Takata, H. Hiraoka, C. Li, X. Xing, and X. Xiao, “Analysis of surface roughness generation in turning operation and its applications,” CIRP Ann. 34, 473–476 (1985).
[Crossref]

Li, M.

T. Sata, M. Li, S. Takata, H. Hiraoka, C. Li, X. Xing, and X. Xiao, “Analysis of surface roughness generation in turning operation and its applications,” CIRP Ann. 34, 473–476 (1985).
[Crossref]

Liu, J.

J. Liu, M. Osadchy, L. Ashton, M. Foster, C. J. Solomon, and S. J. Gibson, “Deep convolutional neural networks for Raman spectrum recognition: a unified solution,” Analyst 142, 4067–4074 (2017).
[Crossref]

Liu, M.

M. Liu, C. F. Cheung, X. Feng, and C. Wang, “Diamond machining of freeform-patterned surfaces on precision rollers,” Int. J. Adv. Manuf. Technol. 103, 4423–4431 (2019).
[Crossref]

C. F. Cheung, M. Liu, R. K. Leach, X. Feng, and C. Zhao, “Hierarchical-information-based characterization of multiscale structured surfaces,” CIRP Ann. 67, 539–542 (2018).
[Crossref]

M. Liu, N. Senin, and R. K. Leach, “Defect detection for structured surfaces via light scattering and machine learning,” in International Symposium on Measurement Technology and Intelligent Instruments (ISMTII), Niigata, Japan, 2019.

Liu, S.

Madsen, J. S. M.

Madsen, M. H.

M. H. Madsen and P.-E. Hansen, “Scatterometry—fast and robust measurements of nano-textured surfaces,” Surf. Topogr. Metrol. Prop. 4, 023003 (2016).
[Crossref]

Malshe, A.

A. Malshe, K. Rajurkar, A. Samant, H. N. Hansen, S. Bapat, and W. Jiang, “Bio-inspired functional surfaces for advanced applications,” CIRP Ann. 62, 607–628 (2013).
[Crossref]

Nikolaev, N.

M. Thomas, R. Su, N. Nikolaev, J. Coupland, and R. K. Leach, “Modelling of interference microscopy beyond the linear regime,” Opt. Eng. 59, 034110 (2020).
[Crossref]

Nikolaev, N. I.

Nygård, J.

Osadchy, M.

J. Liu, M. Osadchy, L. Ashton, M. Foster, C. J. Solomon, and S. J. Gibson, “Deep convolutional neural networks for Raman spectrum recognition: a unified solution,” Analyst 142, 4067–4074 (2017).
[Crossref]

Petzing, J.

F. Gao, R. K. Leach, J. Petzing, and J. M. Coupland, “Surface measurement errors using commerciafl scanning white light interferometers,” Meas. Sci. Technol. 19, 015303 (2007).
[Crossref]

Rajurkar, K.

A. Malshe, K. Rajurkar, A. Samant, H. N. Hansen, S. Bapat, and W. Jiang, “Bio-inspired functional surfaces for advanced applications,” CIRP Ann. 62, 607–628 (2013).
[Crossref]

Raymond, C. J.

C. J. Raymond, “Scatterometry for semiconductor metrology,” in Handbook of Silicon Semiconductor Metrology (CRC Press, 2001), pp. 389–418.

Samant, A.

A. Malshe, K. Rajurkar, A. Samant, H. N. Hansen, S. Bapat, and W. Jiang, “Bio-inspired functional surfaces for advanced applications,” CIRP Ann. 62, 607–628 (2013).
[Crossref]

Sata, T.

T. Sata, M. Li, S. Takata, H. Hiraoka, C. Li, X. Xing, and X. Xiao, “Analysis of surface roughness generation in turning operation and its applications,” CIRP Ann. 34, 473–476 (1985).
[Crossref]

Schmidhuber, J.

J. Schmidhuber, “Deep learning in neural networks: an overview,” Neural Netw. 61, 85–117 (2015).
[Crossref]

Scholze, F.

Seewig, J.

T. V. Vorburger, R. Silver, R. Brodmann, B. Brodmann, and J. Seewig, “Light scattering methods,” in Optical Measurement of Surface Topography (Springer, 2011), pp. 287–318.

Senin, N.

M. Liu, N. Senin, and R. K. Leach, “Defect detection for structured surfaces via light scattering and machine learning,” in International Symposium on Measurement Technology and Intelligent Instruments (ISMTII), Niigata, Japan, 2019.

Sherlock, B.

R. K. Leach, C. W. Jones, B. Sherlock, and A. Krysinski, “The high dynamic range surface metrology challenge,” in 28th Annual Meeting of the American Society for Precision Engineering (ASPE) (2013), pp. 149–152.

Shi, T.

Silver, R.

T. V. Vorburger, R. Silver, R. Brodmann, B. Brodmann, and J. Seewig, “Light scattering methods,” in Optical Measurement of Surface Topography (Springer, 2011), pp. 287–318.

Simonsen, I.

I. Simonsen, “Optics of surface disordered systems,” Eur. Phys. J. Spec. Top. 181, 1–103 (2010).
[Crossref]

Solomon, C. J.

J. Liu, M. Osadchy, L. Ashton, M. Foster, C. J. Solomon, and S. J. Gibson, “Deep convolutional neural networks for Raman spectrum recognition: a unified solution,” Analyst 142, 4067–4074 (2017).
[Crossref]

Su, R.

M. Thomas, R. Su, N. Nikolaev, J. Coupland, and R. K. Leach, “Modelling of interference microscopy beyond the linear regime,” Opt. Eng. 59, 034110 (2020).
[Crossref]

Taflove, A.

A. Taflove and S. C. Hagness, Computational Electrodynamics: The Finite-Difference Time-Domain Method (Artech House, 2005).

Takata, S.

T. Sata, M. Li, S. Takata, H. Hiraoka, C. Li, X. Xing, and X. Xiao, “Analysis of surface roughness generation in turning operation and its applications,” CIRP Ann. 34, 473–476 (1985).
[Crossref]

Tang, Z.

Thomas, M.

M. Thomas, R. Su, N. Nikolaev, J. Coupland, and R. K. Leach, “Modelling of interference microscopy beyond the linear regime,” Opt. Eng. 59, 034110 (2020).
[Crossref]

Vapnik, V.

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20, 273–297 (1995).
[Crossref]

Vorburger, T. V.

T. V. Vorburger, R. Silver, R. Brodmann, B. Brodmann, and J. Seewig, “Light scattering methods,” in Optical Measurement of Surface Topography (Springer, 2011), pp. 287–318.

Wang, C.

M. Liu, C. F. Cheung, X. Feng, and C. Wang, “Diamond machining of freeform-patterned surfaces on precision rollers,” Int. J. Adv. Manuf. Technol. 103, 4423–4431 (2019).
[Crossref]

Xiao, X.

T. Sata, M. Li, S. Takata, H. Hiraoka, C. Li, X. Xing, and X. Xiao, “Analysis of surface roughness generation in turning operation and its applications,” CIRP Ann. 34, 473–476 (1985).
[Crossref]

Xing, X.

T. Sata, M. Li, S. Takata, H. Hiraoka, C. Li, X. Xing, and X. Xiao, “Analysis of surface roughness generation in turning operation and its applications,” CIRP Ann. 34, 473–476 (1985).
[Crossref]

Zeng, Z.

F. Fang, Z. Zeng, X. Zhang, and L. Jiang, “Measurement of micro-V-groove dihedral using white light interferometry,” Opt. Commun. 359, 297–303 (2016).
[Crossref]

Zhang, C.

Zhang, X.

F. Fang, Z. Zeng, X. Zhang, and L. Jiang, “Measurement of micro-V-groove dihedral using white light interferometry,” Opt. Commun. 359, 297–303 (2016).
[Crossref]

Zhao, C.

C. F. Cheung, M. Liu, R. K. Leach, X. Feng, and C. Zhao, “Hierarchical-information-based characterization of multiscale structured surfaces,” CIRP Ann. 67, 539–542 (2018).
[Crossref]

Analyst (1)

J. Liu, M. Osadchy, L. Ashton, M. Foster, C. J. Solomon, and S. J. Gibson, “Deep convolutional neural networks for Raman spectrum recognition: a unified solution,” Analyst 142, 4067–4074 (2017).
[Crossref]

Appl. Opt. (1)

CIRP Ann. (4)

T. Sata, M. Li, S. Takata, H. Hiraoka, C. Li, X. Xing, and X. Xiao, “Analysis of surface roughness generation in turning operation and its applications,” CIRP Ann. 34, 473–476 (1985).
[Crossref]

A. Malshe, K. Rajurkar, A. Samant, H. N. Hansen, S. Bapat, and W. Jiang, “Bio-inspired functional surfaces for advanced applications,” CIRP Ann. 62, 607–628 (2013).
[Crossref]

X. Jiang, “In situ real-time measurement for micro-structured surfaces,” CIRP Ann. 60, 563–566 (2011).
[Crossref]

C. F. Cheung, M. Liu, R. K. Leach, X. Feng, and C. Zhao, “Hierarchical-information-based characterization of multiscale structured surfaces,” CIRP Ann. 67, 539–542 (2018).
[Crossref]

Eur. Phys. J. Spec. Top. (1)

I. Simonsen, “Optics of surface disordered systems,” Eur. Phys. J. Spec. Top. 181, 1–103 (2010).
[Crossref]

Int. J. Adv. Manuf. Technol. (1)

M. Liu, C. F. Cheung, X. Feng, and C. Wang, “Diamond machining of freeform-patterned surfaces on precision rollers,” Int. J. Adv. Manuf. Technol. 103, 4423–4431 (2019).
[Crossref]

J. Opt. Soc. Am. A (1)

Mach. Learn. (1)

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20, 273–297 (1995).
[Crossref]

Meas. Sci. Technol. (2)

F. Gao, R. K. Leach, J. Petzing, and J. M. Coupland, “Surface measurement errors using commerciafl scanning white light interferometers,” Meas. Sci. Technol. 19, 015303 (2007).
[Crossref]

S. Goto, K. Hosobuchi, and W. Gao, “An ultra-precision scanning tunneling microscope Z-scanner for surface profile measurement of large amplitude micro-structures,” Meas. Sci. Technol. 22, 085101 (2011).
[Crossref]

Neural Netw. (1)

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Figures (11)

Fig. 1.
Fig. 1. Proposed scattering method for surface defect detection.
Fig. 2.
Fig. 2. Designed CNN.
Fig. 3.
Fig. 3. Prototype system: (a) diagram, (b) setup for the on-machine experiment, and (c) enlarged view for the system.
Fig. 4.
Fig. 4. Designed saw-tooth microstructured surface.
Fig. 5.
Fig. 5. Two 90º diamond tools for the experiments: (a) a sharp tool and (b) a worn tool with a tip angle equivalent to 157°.
Fig. 6.
Fig. 6. Machined surfaces using the sharp tool and worn tool: (a) SEM and (b) AFM results for the surface machined using the sharp tool; (c) SEM and (d) AFM results for the surface machined using the worn tool.
Fig. 7.
Fig. 7. Measured scattering signals for the saw-tooth surfaces machined with (a) the sharp tool and (b) the worn tool.
Fig. 8.
Fig. 8. Designed vee-groove microstructured surface.
Fig. 9.
Fig. 9. Two 25.5º diamond tools for the experiments: (a) a sharp tool and (b) a worn tool with a tip angle equivalent to 44º.
Fig. 10.
Fig. 10. SEM images for machined surfaces with (a) the sharp tool and (b) the worn tool.
Fig. 11.
Fig. 11. Measured scattering signals for the vee-groove surfaces machined with (a) the sharp tool and (b) the worn tool.

Tables (8)

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Table 1. Ranges for Surface Parameters and Experiment Settings in the Simulation

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Table 2. Defect Detection Results for the Saw-tooth Microstructured Surfaces

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Table 3. Ranges for Surface Parameters and Experiment Settings in the Simulation

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Table 4. Defect Detection Results for the Vee-Groove Microstructured Surfaces

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Table 5. Defect Detection Results for the Saw-tooth Microstructured Surfaces Using the SVM Method

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Table 6. Defect Detection Results for the Vee-Groove Microstructured Surfaces Using the SVM Method

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Table 7. Defect Detection Results for the Saw-tooth Microstructured Surfaces Using the Library Search Method

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Table 8. Defect Detection Results for the Vee-Groove Microstructured Surfaces Using the Library Search Method