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Surface roughness and tool wear monitoring in turning processes through vibration analysis using PSD and GRMS

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Abstract

This study presents a novel approach to monitor and predict surface roughness and tool wear in the turning process, which is crucial for anticipating tool failures, reducing replacement costs, and optimizing production efficiency. The study analyzes vibration signals collected during the turning process of a stainless-steel workpiece with a carbide insert until the tool wear threshold (VB = 300 µm) is reached. Firstly, the vibration signature associated with the machine and the noise were isolated using the Fourier transform (FFT). Then, the optimal frequency band is selected to extract maximum valuable information using the estimated power spectral density (PSD) through the Welch method. The correlation between the vibration signals and surface roughness is then analyzed by calculating the average root mean square (RMS) acceleration of all the obtained PSD curves. Finally, a mathematical prediction model is extracted using a simple linear regression equation between GRMS and surface roughness. The results show a good agreement between the predicted data and the experimental values. The coefficients MSE, RMSE, and MAE have low values of 0.025, 0.1581, and 0.1174, respectively, confirming the accuracy of the proposed model.

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Data availability

The data used in this article can be obtained by requesting it from the corresponding author.

Code availability

All codes used in this article that are not included in the appendices can be obtained by requesting them from the corresponding author.

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Funding

This study was supported by the Mechanical Laboratory of the University of Constantine 1 in cooperation with the Mechanics Research Center in Constantine and funded by the Algerian Ministry of Higher Education and Scientific Research.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Laboratory of Mechanics, University of Constantine 1, PO Box 325, Ain El Bey Road, 25017, Constantine, Algeria

    Roumaissa Bouchama & Abdelhakim Cherfia

  2. Mechanics Research Center, PO Box 73B, 25021, Constantine, Algeria

    Mohamed Lamine Bouhalais

Authors
  1. Roumaissa Bouchama
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  2. Mohamed Lamine Bouhalais
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  3. Abdelhakim Cherfia
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Correspondence to Roumaissa Bouchama.

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Appendices

Appendix 1 MATLAB code to plot the welch

1.1 Power spectral density estimate

% Spectrum data

sig = si gnal1

h = spectrum.welch;

time = 1.28;

t = linspace(0, time, 65536);

dt = t(2)—t(1);

Fs = 1 / dt;

sx = psd(h, sig, 'Fs', Fs);

figure;

plot(sx)

grid on;

Appendix 2 MATLAB code to plot the vibration

2.1 Signals in a 3-D plot

% Signal Data

signal1 = sig1;

signal2 = sig2;

signal3 = sig3;

% Creating time vectors

time = linspace(0, 1.3, numel(signal1));

pass1 = 1*ones(size(signal1));

pass2 = 2*ones(size(signal2));

pass3 = 3*ones(size(signal3));

% Plotting the signals in 3D

figure;

plot3(time, pass1, signal1, 'LineWidth', 1.5);

hold on;

plot3(time, pass2, signal2, 'LineWidth', 1.5);

plot3(time, pass3, signal3, 'LineWidth', 1.5);

hold off;

grid on;

% Adjusting the view angle

View (-33, 22); % Azimuth = -33 degrees, Elevation = 22 degrees

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Bouchama, R., Bouhalais, M.L. & Cherfia, A. Surface roughness and tool wear monitoring in turning processes through vibration analysis using PSD and GRMS. Int J Adv Manuf Technol 130, 3537–3552 (2024). https://doi.org/10.1007/s00170-023-12742-x

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  • DOI: https://doi.org/10.1007/s00170-023-12742-x

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