Analysis of the effect of a new process control agent technique on the mechanical milling process using a neural network model: Measurement and modeling

In this study, a new process control agent (PCA) technique called as gradual process control agent technique was developed and the new technique was compared with conventional process control agent technique. In addition, a neural network (ANN) approach was presented for the prediction of effect of gradual process control agent technique on the mechanical milling process. The structural evolution and morphology of powders were investigated using SEM and particle size analyzer techniques. The experimental results were used to train feed forward and back propagation learning algorithm with two hidden layers. The four input parameters in the proposed ANN were the milling time, the gradual PCA content, previous PCA content and gradual PCA content. The particle size was the output obtained from the proposed ANN. By comparing the predicted values with the experimental data it is demonstrated that the ANN is a useful, efficient and reliable method to determine the effect of gradual process control agent technique on the mechanical milling process.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► A new process control agent (PCA) technique called as gradual process control agent technique (GPCA) was developed. ► The new technique was compared with conventional process control agent technique. ► A neural network (ANN) approach is presented for the prediction of effect of the GPCA technique. ► The maximum mean absolute percentage error (MAPE) for predicted values does not exceed 4.68%.