Agustin Flores Cuautle - Multilayer Perceptron Artificial Neural

Journal article

K. R. Varadaraj, S. Vijay Kumar, D. Chethan, S. C. Ramesh Kumar, S. Basavaraju, B. M. Kunar, Jose de Jesus Agustin Flores Cuautle
Journal of Mines, Metals and Fuels, 2023

Semantic Scholar DOI

Cite

APA Click to copy
Varadaraj, K. R., Kumar, S. V., Chethan, D., Kumar, S. C. R., Basavaraju, S., Kunar, B. M., & de Jesus Agustin Flores Cuautle, J. (2023). Multilayer Perceptron Artificial Neural Network (Mlpann) Model to Predict Temperature During Rotary Drilling. Journal of Mines, Metals and Fuels.

Chicago/Turabian Click to copy
Varadaraj, K. R., S. Vijay Kumar, D. Chethan, S. C. Ramesh Kumar, S. Basavaraju, B. M. Kunar, and Jose de Jesus Agustin Flores Cuautle. “Multilayer Perceptron Artificial Neural Network (Mlpann) Model to Predict Temperature During Rotary Drilling.” Journal of Mines, Metals and Fuels (2023).

MLA Click to copy
Varadaraj, K. R., et al. “Multilayer Perceptron Artificial Neural Network (Mlpann) Model to Predict Temperature During Rotary Drilling.” Journal of Mines, Metals and Fuels, 2023.

BibTeX Click to copy

@article{k2023a,
  title = {Multilayer Perceptron Artificial Neural Network (Mlpann) Model to Predict Temperature During Rotary Drilling},
  year = {2023},
  journal = {Journal of Mines, Metals and Fuels},
  author = {Varadaraj, K. R. and Kumar, S. Vijay and Chethan, D. and Kumar, S. C. Ramesh and Basavaraju, S. and Kunar, B. M. and de Jesus Agustin Flores Cuautle, Jose}
}

Abstract

In this paper, a multilayer perceptron neural network has been used to represent temperature measurement during rotary drilling of five types of rock samples. To forecast the temperature at various thermocouple depths, the experimentally collected data was standardized. Indicators of model performance was also obtained in order to assess the correctness of the model. One hidden layer and one output layer were employed with MLPANN, which has ten input parameters (bit diameter (DD), Spindle Speed (SS), Penetration Rate (PR), thrust, and torque) and rock properties. Levenberg Marquardt learning algorithm with transfer function of logsig is the most optimal neuron number of 10-16-1 was successfully forecasting the temperature with a correlation of 0.9936 and 0.9941 for training and testing algorithm during drilling after analysis based on the trial-and- error approach to identify the optimum algorithm. Ten input parameters, a logsig sigmoid transfer function, and the trainlm algorithm in this study provide good prediction ability with tolerable accuracy.