Analysing of Multivariate Processes with Machine Learning Algorithms
Deniz DEMİRCİOĞLU DİREN Semra BORAN Seda Hatice GÖKLER
AbstractIt is often not easy to obtain results from complex processes multi variables. Additional techniques and methods are needed to guide. In this study, after the detecting the out of control and under control samples with Hotelling T2 control chart in a multivariate manufacturing process then machine learning algorithms was used to predict the quality of future samples. Four machine learning algorithms were trained and tested by shifts of different magnitude from the process average. The performances of the algorithms were compared according to the accuracy and error rates of the predictions and the most appropriate one was chosen as Multilayer Perceptron.