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This article presents an investigation of the use of machine learning methodologies
for the prediction of surface roughness in milling operations, using sensor data as
the primary source of information. The sensors, which included current transformers, a
microphone, and displacement sensors, captured comprehensive machining signals at a
frequency of 10 kHz. The signals were subjected to preprocessing using the Savitzky–Golay
filter, with the objective of isolating relevant moments of active material machining and
reducing noise. Two machine learning models, namely Elastic Net and neural networks,
were employed for the prediction purposes. The Elastic Net model demonstrated effective
handling of multicollinearity and reduction in the data dimensionality, while the neural
networks, utilizing the ReLU activation function, exhibited the capacity to capture complex,
nonlinear patterns. The models were evaluated using the coefficient of determination (R²),
which yielded values of 0.94 for Elastic Net and 0.95 for neural networks, indicating a high
degree of predictive accuracy. These findings illustrate the potential of machine learning to
optimize manufacturing processes by facilitating precise predictions of surface roughness.
Elastic Net demonstrated its utility in reducing dimensionality and selecting features, while
neural networks proved effective in modeling complex data. Consequently, these methods
exemplify the efficacy of integrating data-driven approaches with robust predictive models
to improve the quality and efficiency of surface.
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