Applied Engineering

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

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.

DOI 10.11648/j.ae.20210501.17
Published in Applied Engineering (Volume 5, Issue 1, June 2021)
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), 2024. 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
  • Laboratory of Mechanics, Postgraduate School for Pure and Applied Sciences, University of Douala, Douala, Cameroon

  • Laboratory of Mechanics, Postgraduate School for Pure and Applied Sciences, University of Douala, Douala, Cameroon; Laboratory of Applied Engineering and Biotechnology, Higher Technical Teacher Training College Ebolowa, University of Yaounde I, Ebolowa, Cameroon

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

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

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