Prediction of the Production Rate of Chain Saw Machine using the Multilayer Perceptron (MLP) Neural Network

Javad Mohammadi, Mohammad Ataei, Reza Khalo Kakaei, Reza Mikaeil, Sina Shaffiee Haghshenas


The production rate in rock cutting machines is one of the most influential parameters in designing and planning procedures. Complete understanding of the production rate of cutting machines help experts and owners of this industry to predict the production expenses. Therefore, the present study predicts the production rate of the chain saw machine in dimensional stone quarries. In this research, the method of artificial neural networks was used for modeling and predicting the production rate. In addition, in this modeling, 98 data were collected from the results obtained from field studies on 7 carbonate rock samples as the dataset. Four important parameters, including uniaxial compressive strength (UCS), Los Angeles abrasion (LAA) Test, equivalent quartz content (Qs), and Schmidt Hammer (Sch) were considered as input data and the production rate was considered as the output data. The model was evaluated by the performance indices for artificial neural networks, including the value account for (VAF), root mean square error (RMSE), and coefficient of determination (R2). For simulation, 10 models were created and evaluated. Finally, the best model, i.e. model No. 3, was selected with a 4 × 3 × 1 structure, including 4 input neurons, 3 neurons in the hidden layer and 1 output neuron. The results obtained from the model’s performance indices show that a very appropriate prediction has been done for determining the production rate of the chain saw machine by artificial neural networks.


Chain Saw Machine; Production Rate; Artificial Neural Network; Carbonate Rocks.


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DOI: 10.28991/cej-0309196


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