Improving Quality of Products in Hard Drive Manufacturing by Decision Tree Technique
Hard drives manufacturing is a complex process. The quality of products is determined by a large set of parameters. However, there are defects in products that need to be removed. We study a systematic approach to find suitable parameters which can reduce the number of defected products. Our approach exploits decision tree learning and a set of algorithms to adjust decision parameters obtained from the learned decision tree. Moreover, because we cannot test the result in the real environment, we propose a trustable testing method which can predict the improvement obtained from the parameter adjustment system. The results from the experiments show that the quality of products in the dataset can be improved as much as 12%. Which is significant in hard drive manufacturing.
Keywords: Decision Tree, Data Mining, Hard drive Manufacturing, Improve Quality
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ABOUT THE AUTHORS
Anotai Siltepavet
She received the bachelor’s degree in mathematics science from Mahidol University, Bangkok, Thailand, in 2006. She is currently an asia engineering at the department of PPE, Seagate Technology (Thailand) Ltd. while pursuing her master degree at the Department of Computer Engineering, Chulalongkorn University. Her research area is machine learning.
Sukree Sinthupinyo
He is an Assistant Professor at the Department of Computer Engineering, Chulalongkorn University. He had been work as a lecturer at the Department of Computer Science, Faculty of Science and Technology, Thammasat University. His interests include Artificial Intelligence, Machine Learning, and Neural Networks.
Prabhas Chongstitvatana
He graduated from Kasetsart University with bachelor’s degree in electronic engineering. He earned his doctoral from Edinburgh University. He is a professor in department of Computer Engineering Chulalongkorn since 2006. His researches are in robotics, computation and computer architecture. He is an active member of several professional societies.
Anotai Siltepavet
She received the bachelor’s degree in mathematics science from Mahidol University, Bangkok, Thailand, in 2006. She is currently an asia engineering at the department of PPE, Seagate Technology (Thailand) Ltd. while pursuing her master degree at the Department of Computer Engineering, Chulalongkorn University. Her research area is machine learning.
Sukree Sinthupinyo
He is an Assistant Professor at the Department of Computer Engineering, Chulalongkorn University. He had been work as a lecturer at the Department of Computer Science, Faculty of Science and Technology, Thammasat University. His interests include Artificial Intelligence, Machine Learning, and Neural Networks.
Prabhas Chongstitvatana
He graduated from Kasetsart University with bachelor’s degree in electronic engineering. He earned his doctoral from Edinburgh University. He is a professor in department of Computer Engineering Chulalongkorn since 2006. His researches are in robotics, computation and computer architecture. He is an active member of several professional societies.
