Prediction of Manufacturing Data Using Several Machine Learning Approache

This thesis is to meet the needs of developing automation process on defective testing in the hard disk drives production process, focusing on machine learning and articial intelligence. The objective is to to predict the defectives and improve the accuracy rate by using the tree based algorithm and neural networks. The powerful models can help manufacturing process improving time and labor efficiency.