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A comparative study of basic and ensemble artificial intelligence models for surface roughness prediction during the AA7075 milling process

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Abstract

This study reports on how ML algorithms are employed to investigate and predict surface roughness. Experiments were executed with a CNC milling machine, using AA7075 as part material and a “TiCN” coated tool. Feed rates per tooth, cutting speeds, cut depth, and cutting fluid were studied in response to roughness average (Ra) values. In the present study, Ra was measured with contact stylus tracing. Forty-two experiments were executed: thirty-three were used in all models training and nine in tests, and an additional experiment was carried out with diverse cutting parameters to validate the preferred models. This is the first study where thirteen ML algorithms, of which seven are basic and six are ensemble models, have been studied in the context of surface roughness. The study results showed that the voting regression model was the best model according to performance metrics (R2= 0.97, RAE = 0.17, RMSE = 0.0325, MAE = 0.13, and RSE = 0.09) and deviation 5.66%. Manufacturing companies can employ the voting regression model to predict surface roughness to enhance manufacturing efficiency, by harmonizing cutting conditions values against surface roughness.

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Data are available within the article or its supplementary materials.

Abbreviations

AA:

Aluminum alloy

Ra:

Roughness average

AI:

Artificial intelligence

ANN:

Artificial neural networks

ML:

Machine learning

-NN:

-nearest neighbor

DTR:

Decision tree regressor

GPR:

Gaussian process regressor

PLSR:

Partial least squares regression

RR:

Kernel ridge regression

VR:

Voting regressor

BR:

Bagging regressor

ABR:

AdaBoost regressor

GBM:

Gradient boosting model

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  1. ISET Nabeul, DGET, 8050, Nabeul, Tunisia

    Abd El Hedi Gabsi, Chokri Ben Aissa & Safa Mathlouthi

  2. Laboratory of Mechanics, Materials and Processes, National High School of Engineering of Tunis, University of Tunis, Tunis, Tunisia

    Chokri Ben Aissa

  3. Applied Mechanics and Engineering Laboratory LMAI, National School of Engineering of Tunis ENIT, University of Tunis Manar, Tunis, Tunisia

    Safa Mathlouthi

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Gabsi, A.E.H., Ben Aissa, C. & Mathlouthi, S. A comparative study of basic and ensemble artificial intelligence models for surface roughness prediction during the AA7075 milling process. Int J Adv Manuf Technol 126, 1–15 (2023). https://doi.org/10.1007/s00170-023-11026-8

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