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An expert system for vibration-based surface roughness prediction using firefly algorithm and LSTM network

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Abstract

Surface roughness is a critical indicator of machining quality and is affected by factors such as tool quality, workpiece properties, and machining conditions. Many studies have focused on predicting the surface roughness of the turning process using cutting parameters. Yet, surface roughness is also impacted by tool qualities, workpiece properties, and machining conditions. Extracting statistical features from vibration signals during machining can aid in predicting surface roughness and facilitating prompt remedial action to boost productivity. However, not all features may contain crucial information about surface conditions. However, all the extracted features may not contain important information about the surface conditions. Hence, a novel hybrid vibration-based prediction methodology was proposed using the firefly algorithm-based feature selection to predict surface roughness. The statistical features were extracted from the vibration signals measured during the machining operation. A novel Firefly algorithm was employed to select features with useful information about the surface condition. The selected features were used to train a long short-term memory (LSTM) network to predict surface roughness. The performance of the proposed methodology was compared to other feature selection and prediction techniques, and it was found to be effective. The results indicated that the features selected using the Firefly algorithm yielded better prediction results with a minimum root mean square error (RMSE) of 0.248 compared to those selected using the genetic algorithm. The LSTM network’s effectiveness with the feature selected using the Firefly method was also compared to that of other conventional regression algorithms and found to be superior.

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Abbreviations

FA:

Firefly algorithm

LSTM:

Long short-term memory

RMSE:

Root mean square error

RNN:

Recurrent neural network

ANN:

Artificial neural network

SFS:

Sequential feature selection

GA:

Genetic algorithm

SRM:

Square root mean

\({\text{MSE}}\) :

Mean square error

\(f\) :

Fitness value

I 0 :

Initial light intensity

γ :

The light absorption coefficient

α :

Noise parameter

δ :

Reduction coefficient

W :

Weight matrix

b :

Bias term

\(C_{t}\) :

Long-term memory information

\(\overline{C}_{t}\) :

Present memory information

\(h_{t - 1}\) :

Output of the LSTM cell

\(\sigma\) :

Sigmoid activation function

\({\text{ReLU}}\) :

Relu activation function

RMS:

Root mean square

\(N_{s}\) :

Number of features selected

\(N_{t}\) :

Total number of features extracted

r :

Correlation coefficient

\(\beta_{0}\) :

Attractiveness

N :

Number of iterations

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Engineering College, Kovilpatti, Tamil Nadu, 628 503, India

    Athisayam Andrews, Kondal Manisekar, F. Michael Thomas Rex, G. Sivakumar & M. Narayanan

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  1. Athisayam Andrews
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  2. Kondal Manisekar
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Andrews, A., Manisekar, K., Michael Thomas Rex, F. et al. An expert system for vibration-based surface roughness prediction using firefly algorithm and LSTM network. J Braz. Soc. Mech. Sci. Eng. 45, 414 (2023). https://doi.org/10.1007/s40430-023-04341-4

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