The performance prediction of roadheader using Artificial neural network

Roadheaders are one of those machines that have a unique ability and flexibility in mechanical excavating of soft to moderate rock formations, therefore they are widely used in underground mining and tunneling. Evaluating and predicting the performance of hydraulic is considered a very important factor in their successful application. The main goal of this research is to provide a model for predicting the amount of ICR based on the characteristics of the excavated rock formations. Artificial neural network method is used. The database was then analyzed through ANN to yield an optimum predictive model for ICR (m3/h). Results showed that there is a close relation between actual (measured) data and predicted data with coefficient of determination (R2) of 0.85 and root mean square error (RMSE) =0.07.

The performance prediction of roadheader using Artificial neural network Keywords:

Roadheaders , excavating , Artificial neural network , coefficient of determination