Drilling performance analysis of drill column machine using proposed neural networks

In spite of advanced material cutting technology, there are still some problems due to unpredicted vibrations on horizontal and vertical directions on column drilling machines. This paper presents an investigation for drilling condition of drill column machines performance using proposed neural networks. The investigation is divided into two parts. First, the drill column machine is employed to analyze vibrations with steel and aluminum materials for increased drilling speeds. During the working of the system, some measuring points are indicated to analyses of drilling conditions. Finally, two types of proposed neural networks predictors are used to predict vibration variation for both cases of steel and aluminum materials of drilling systems. The experimental and simulation result is improved that radial basis neural network has superior performance to adapt experimental applications for drill column machines.