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

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 theproduction expenses. Therefore, the present study predicts the production rate of the chain saw machine in dimensionalstone quarries. In this research, the method of artificial neural networks was used for modeling and predicting theproduction rate. In addition, in this modeling, 98 data were collected from the results obtained from field studies on 7carbonate rock samples as the dataset. Four important parameters, including uniaxial compressive strength (UCS), LosAngeles abrasion (LAA) Test, equivalent quartz content (Qs), and Schmidt Hammer (Sch) were considered as input dataand the production rate was considered as the output data. The model was evaluated by the performance indices for artificialneural 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 a4 × 3 × 1 structure, including 4 input neurons, 3 neurons in the hidden layer and 1 output neuron. The results obtained fromthe model’s performance indices show that a very appropriate prediction has been done for determining the production rateof the chain saw machine by artificial neural networks.

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

Chain Saw Machine , Production Rate , Artificial Neural Network , Carbonate Rocks