research-article

Hybrid Monitoring of Surface Roughness and Straightness in CNC Turning of Aluminium using Neural Networks Approach

Author:

Somkiat TangjitsitcharoenAuthors Info & Claims

AICCC '19: Proceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference

Pages 203 - 208

https://doi.org/10.1145/3375959.3375964

Published: 16 February 2020 Publication History

Abstract

The relations of the surface roughness, the straightness and the cutting conditions are investigated to realize an intelligent CNC machine by monitoring the in-process cutting forces during CNC turning of aluminium 6063 with the use of coated carbide tools. The cutting force is proposed to predict the straightness and surface roughness. The Fast Fourier Transform (FFT) is used to prove the relations of them by checking the frequencies of them. The cutting force ratio is proposed and normalized to predict the in-process surface roughness and straightness regardless of the cutting conditions. The surface roughness and the straightness are calculated simultaneously by employing the two-layer feed-forward neural networks with sigmoid hidden and linear output neurons. The neural networks is trained by using the Levenberg-Marquardt back propagation algorithm. It is understood that the surface roughness and the straightness can be estimated well by utilizing the proposed method under various cutting conditions.

References

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Cited By

Tangjitsitcharoen SSuksomcheewin N(2024)Intelligent Monitoring of Surface Roughness and Straightness with Roundness on CNC Turning Utilizing Wavelet Transform via Neural NetworksProceedings of the 2024 9th International Conference on Multimedia Systems and Signal Processing (ICMSSP)10.1145/3690063.3690066(9-17)Online publication date: 24-May-2024
https://dl.acm.org/doi/10.1145/3690063.3690066

Index Terms

Hybrid Monitoring of Surface Roughness and Straightness in CNC Turning of Aluminium using Neural Networks Approach
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
      1. Computational control theory

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AICCC '19: Proceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference

December 2019

216 pages

ISBN:9781450372633

DOI:10.1145/3375959

Copyright © 2019 ACM.

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Kobe University: Kobe University

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Published: 16 February 2020

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AICCC 2019: 2019 2nd Artificial Intelligence and Cloud Computing Conference

December 21 - 23, 2019

Kobe, Japan

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Cited By

Tangjitsitcharoen SSuksomcheewin N(2024)Intelligent Monitoring of Surface Roughness and Straightness with Roundness on CNC Turning Utilizing Wavelet Transform via Neural NetworksProceedings of the 2024 9th International Conference on Multimedia Systems and Signal Processing (ICMSSP)10.1145/3690063.3690066(9-17)Online publication date: 24-May-2024
https://dl.acm.org/doi/10.1145/3690063.3690066

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