Comparing Machine Learning and Statistical Process Control for Predicting Manufacturing Performance

Abstract

Quality has become one of the most important factors in the success of manufacturing companies. In this paper, the use of machine learning algorithms in quality control is compared to the use of statistical process monitoring, a classical quality management technique. The test dataset has a large number of features, which requires the use of principal component analysis and clustering to isolate the data into potential process groups. A Random Forest, Support Vector Machine and Naive Bayes algorithms were used to predict when the manufacturing process is out of control. The Random Forest algorithm performed significantly better than both the Naive Bayes and SVM algorithms in all 3 clusters of the dataset. The results were benchmarked against Hotelling’s $$T^2$$ control charts which were trained using 80% of each cluster dataset and tested on the remaining 20%. In comparison with Hotelling’s $$T^2$$ multivariate statistical process monitoring charts, the Random Forest algorithm still emerges as the better quality control method.