Processing Cycle Prediction Using Support Vector Regression In Intelligent Manufacturing

The processing cycle in an intelligent manufacturing machine (IMM) is difficult to predict accurately owing to uncertainties caused by unexpected maintenance errors and damage. Thus, a new method for accurate prediction is required. We propose a new prediction method using an algorithm based on support vector regression (SVR) in this study. The new method uses big data and determines its logical relationship with a processing cycle to obtain an accurate prediction of the cycle. The accuracy of the SVR method (>95%) is better than that of the traditional method (79.3-89.6%). The result proves that the method predicts the processing cycle accurately and provides essential information for developing algorithms for designing processing cycles in an IMM.