Remote Monitoring of Surface Roughness on Turned and Milled Carbon Steels with High-Speed Steel and Tungsten Carbide Tools

Ludovic Ngongang; Thomas Kanaa; Ebenezer Njeugna; Atangana Ateba

doi:doi:10.11648/j.ae.20210501.17

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Remote Monitoring of Surface Roughness on Turned and Milled Carbon Steels with High-Speed Steel and Tungsten Carbide Tools

Ludovic Ngongang, Thomas Kanaa, Ebenezer Njeugna, Atangana Ateba

Published in Applied Engineering (Volume 5, Issue 1)

Received: 23 April 2021 Accepted: 10 May 2021 Published: 21 May 2021

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Abstract

Machinists and Metrologists are most often called to take decisions for least machining to achieve specific surface qualities of mechanical organs used in aeronautics and other severe environments. The highest challenge they face is in decision-making about the surface finish related to the cutting conditions, the tool life, and the machined material. This paper proposes a method of remote measurement and prediction of the surface roughness on turned and milled carbon steels with high-speed steel and tungsten carbide tools, based on the image acquisition protocol, material content and tool life. The remote measurement of the surface roughness involved a point-to-point viewing angle to capture the image surfaces to appreciate the ideal angle of optimal optical measurement. The assessment of the optical roughness involved the line profiling calculation method on the locally corrected pixels’ values before the areal integration. The optical roughness values were regressed on the reference values and the precision of the method was assessed. The angles of 60°, 75°, and 120° show the effectiveness of the measurement method with precision attaining 83%. For the roughness prediction in the milling and turning operations with high-speed steel and tungsten carbide tools, the fuzzy logic and artificial neural networks techniques are compared considering the cutting conditions as fixed, the carbon percentage and the tool life, all as inputs. With an overall measurement precision above 90% and very low mean square errors, the qualifiedness of the predictive methods is underlined.

Published in	Applied Engineering (Volume 5, Issue 1)
DOI	10.11648/j.ae.20210501.17
Page(s)	22-34
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Surface Roughness, Remote Monitoring, Prediction, Fuzzy Logic, Artificial Neural Networks, Machining

References

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APA Style

Ludovic Ngongang, Thomas Kanaa, Ebenezer Njeugna, Atangana Ateba. (2021). Remote Monitoring of Surface Roughness on Turned and Milled Carbon Steels with High-Speed Steel and Tungsten Carbide Tools. Applied Engineering, 5(1), 22-34. https://doi.org/10.11648/j.ae.20210501.17

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ACS Style

Ludovic Ngongang; Thomas Kanaa; Ebenezer Njeugna; Atangana Ateba. Remote Monitoring of Surface Roughness on Turned and Milled Carbon Steels with High-Speed Steel and Tungsten Carbide Tools. Appl. Eng. 2021, 5(1), 22-34. doi: 10.11648/j.ae.20210501.17

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AMA Style

Ludovic Ngongang, Thomas Kanaa, Ebenezer Njeugna, Atangana Ateba. Remote Monitoring of Surface Roughness on Turned and Milled Carbon Steels with High-Speed Steel and Tungsten Carbide Tools. Appl Eng. 2021;5(1):22-34. doi: 10.11648/j.ae.20210501.17

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@article{10.11648/j.ae.20210501.17,
  author = {Ludovic Ngongang and Thomas Kanaa and Ebenezer Njeugna and Atangana Ateba},
  title = {Remote Monitoring of Surface Roughness on Turned and Milled Carbon Steels with High-Speed Steel and Tungsten Carbide Tools},
  journal = {Applied Engineering},
  volume = {5},
  number = {1},
  pages = {22-34},
  doi = {10.11648/j.ae.20210501.17},
  url = {https://doi.org/10.11648/j.ae.20210501.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ae.20210501.17},
  abstract = {Machinists and Metrologists are most often called to take decisions for least machining to achieve specific surface qualities of mechanical organs used in aeronautics and other severe environments. The highest challenge they face is in decision-making about the surface finish related to the cutting conditions, the tool life, and the machined material. This paper proposes a method of remote measurement and prediction of the surface roughness on turned and milled carbon steels with high-speed steel and tungsten carbide tools, based on the image acquisition protocol, material content and tool life. The remote measurement of the surface roughness involved a point-to-point viewing angle to capture the image surfaces to appreciate the ideal angle of optimal optical measurement. The assessment of the optical roughness involved the line profiling calculation method on the locally corrected pixels’ values before the areal integration. The optical roughness values were regressed on the reference values and the precision of the method was assessed. The angles of 60°, 75°, and 120° show the effectiveness of the measurement method with precision attaining 83%. For the roughness prediction in the milling and turning operations with high-speed steel and tungsten carbide tools, the fuzzy logic and artificial neural networks techniques are compared considering the cutting conditions as fixed, the carbon percentage and the tool life, all as inputs. With an overall measurement precision above 90% and very low mean square errors, the qualifiedness of the predictive methods is underlined.},
 year = {2021}
}

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TY  - JOUR
T1  - Remote Monitoring of Surface Roughness on Turned and Milled Carbon Steels with High-Speed Steel and Tungsten Carbide Tools
AU  - Ludovic Ngongang
AU  - Thomas Kanaa
AU  - Ebenezer Njeugna
AU  - Atangana Ateba
Y1  - 2021/05/21
PY  - 2021
N1  - https://doi.org/10.11648/j.ae.20210501.17
DO  - 10.11648/j.ae.20210501.17
T2  - Applied Engineering
JF  - Applied Engineering
JO  - Applied Engineering
SP  - 22
EP  - 34
PB  - Science Publishing Group
SN  - 2994-7456
UR  - https://doi.org/10.11648/j.ae.20210501.17
AB  - Machinists and Metrologists are most often called to take decisions for least machining to achieve specific surface qualities of mechanical organs used in aeronautics and other severe environments. The highest challenge they face is in decision-making about the surface finish related to the cutting conditions, the tool life, and the machined material. This paper proposes a method of remote measurement and prediction of the surface roughness on turned and milled carbon steels with high-speed steel and tungsten carbide tools, based on the image acquisition protocol, material content and tool life. The remote measurement of the surface roughness involved a point-to-point viewing angle to capture the image surfaces to appreciate the ideal angle of optimal optical measurement. The assessment of the optical roughness involved the line profiling calculation method on the locally corrected pixels’ values before the areal integration. The optical roughness values were regressed on the reference values and the precision of the method was assessed. The angles of 60°, 75°, and 120° show the effectiveness of the measurement method with precision attaining 83%. For the roughness prediction in the milling and turning operations with high-speed steel and tungsten carbide tools, the fuzzy logic and artificial neural networks techniques are compared considering the cutting conditions as fixed, the carbon percentage and the tool life, all as inputs. With an overall measurement precision above 90% and very low mean square errors, the qualifiedness of the predictive methods is underlined.
VL  - 5
IS  - 1
ER  -

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Author Information

Ludovic Ngongang

Laboratory of Mechanics, Postgraduate School for Pure and Applied Sciences, University of Douala, Douala, Cameroon
Thomas Kanaa

Laboratory of Mechanics, Postgraduate School for Pure and Applied Sciences, University of Douala, Douala, Cameroon
Ebenezer Njeugna

Laboratory of Mechanics, Postgraduate School for Pure and Applied Sciences, University of Douala, Douala, Cameroon
Atangana Ateba

Laboratory of Mechanics, Postgraduate School for Pure and Applied Sciences, University of Douala, Douala, Cameroon

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APA Style

Ludovic Ngongang, Thomas Kanaa, Ebenezer Njeugna, Atangana Ateba. (2021). Remote Monitoring of Surface Roughness on Turned and Milled Carbon Steels with High-Speed Steel and Tungsten Carbide Tools. Applied Engineering, 5(1), 22-34. https://doi.org/10.11648/j.ae.20210501.17

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ACS Style

Ludovic Ngongang; Thomas Kanaa; Ebenezer Njeugna; Atangana Ateba. Remote Monitoring of Surface Roughness on Turned and Milled Carbon Steels with High-Speed Steel and Tungsten Carbide Tools. Appl. Eng. 2021, 5(1), 22-34. doi: 10.11648/j.ae.20210501.17

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AMA Style

Ludovic Ngongang, Thomas Kanaa, Ebenezer Njeugna, Atangana Ateba. Remote Monitoring of Surface Roughness on Turned and Milled Carbon Steels with High-Speed Steel and Tungsten Carbide Tools. Appl Eng. 2021;5(1):22-34. doi: 10.11648/j.ae.20210501.17

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@article{10.11648/j.ae.20210501.17,
  author = {Ludovic Ngongang and Thomas Kanaa and Ebenezer Njeugna and Atangana Ateba},
  title = {Remote Monitoring of Surface Roughness on Turned and Milled Carbon Steels with High-Speed Steel and Tungsten Carbide Tools},
  journal = {Applied Engineering},
  volume = {5},
  number = {1},
  pages = {22-34},
  doi = {10.11648/j.ae.20210501.17},
  url = {https://doi.org/10.11648/j.ae.20210501.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ae.20210501.17},
  abstract = {Machinists and Metrologists are most often called to take decisions for least machining to achieve specific surface qualities of mechanical organs used in aeronautics and other severe environments. The highest challenge they face is in decision-making about the surface finish related to the cutting conditions, the tool life, and the machined material. This paper proposes a method of remote measurement and prediction of the surface roughness on turned and milled carbon steels with high-speed steel and tungsten carbide tools, based on the image acquisition protocol, material content and tool life. The remote measurement of the surface roughness involved a point-to-point viewing angle to capture the image surfaces to appreciate the ideal angle of optimal optical measurement. The assessment of the optical roughness involved the line profiling calculation method on the locally corrected pixels’ values before the areal integration. The optical roughness values were regressed on the reference values and the precision of the method was assessed. The angles of 60°, 75°, and 120° show the effectiveness of the measurement method with precision attaining 83%. For the roughness prediction in the milling and turning operations with high-speed steel and tungsten carbide tools, the fuzzy logic and artificial neural networks techniques are compared considering the cutting conditions as fixed, the carbon percentage and the tool life, all as inputs. With an overall measurement precision above 90% and very low mean square errors, the qualifiedness of the predictive methods is underlined.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Remote Monitoring of Surface Roughness on Turned and Milled Carbon Steels with High-Speed Steel and Tungsten Carbide Tools
AU  - Ludovic Ngongang
AU  - Thomas Kanaa
AU  - Ebenezer Njeugna
AU  - Atangana Ateba
Y1  - 2021/05/21
PY  - 2021
N1  - https://doi.org/10.11648/j.ae.20210501.17
DO  - 10.11648/j.ae.20210501.17
T2  - Applied Engineering
JF  - Applied Engineering
JO  - Applied Engineering
SP  - 22
EP  - 34
PB  - Science Publishing Group
SN  - 2994-7456
UR  - https://doi.org/10.11648/j.ae.20210501.17
AB  - Machinists and Metrologists are most often called to take decisions for least machining to achieve specific surface qualities of mechanical organs used in aeronautics and other severe environments. The highest challenge they face is in decision-making about the surface finish related to the cutting conditions, the tool life, and the machined material. This paper proposes a method of remote measurement and prediction of the surface roughness on turned and milled carbon steels with high-speed steel and tungsten carbide tools, based on the image acquisition protocol, material content and tool life. The remote measurement of the surface roughness involved a point-to-point viewing angle to capture the image surfaces to appreciate the ideal angle of optimal optical measurement. The assessment of the optical roughness involved the line profiling calculation method on the locally corrected pixels’ values before the areal integration. The optical roughness values were regressed on the reference values and the precision of the method was assessed. The angles of 60°, 75°, and 120° show the effectiveness of the measurement method with precision attaining 83%. For the roughness prediction in the milling and turning operations with high-speed steel and tungsten carbide tools, the fuzzy logic and artificial neural networks techniques are compared considering the cutting conditions as fixed, the carbon percentage and the tool life, all as inputs. With an overall measurement precision above 90% and very low mean square errors, the qualifiedness of the predictive methods is underlined.
VL  - 5
IS  - 1
ER  -

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