Reliability improvement of diamond drill bits using design of experiments

ABSTRACT

Design of experiments has received much attention in the context of reliability improvement. Many engineers and researchers are considering how to use designed experiments to improve the reliability of products and processes. This work illustrates a 12-run Plackett-Burman design and its analysis to improve the reliability of a diamond drill bit. A detailed description and execution of the design are provided including how to choose the factors and determine the number of experimental factors. In addition, the results of the real-world experiment are presented and the predicted performance from statistical model fitting is verified by part of a confirmation experiment. The experimental data show that the recommended factor settings substantially improved the reliability of the diamond drill bits.

KEYWORDS:

• diamond drill bit
• DOE
• failure data
• reliability improvement
• Weibull distribution

About the authors

Juntao Fang is a postdoctoral fellow of the College of Management and Economics at Tianjin University, China and is an assistant professor in the School of Management of Tianjin University of Traditional Chinese Medicine, China. She received her Ph.D. degree from Tianjin University, China, in 2011. Her current research interests include design of experiments, response surface methodology, and reliability analysis.

Zhen He is a professor in the College of Management and Economics at Tianjin University, China. He received his B.S., M.S., and Ph.D. degrees from Tianjin University, China, in 1988, 1991, and 2000, respectively. His current research interests include reliability analysis, statistical process controls, design of experiments, response surface methodology, and Six Sigma.

Zhaomin Zhang is a Ph.D. student in the College of Management and Economics at Tianjin University, China. He received his B.S. degree in 2013 from Southwest Jiao Tong University. His research interests include reliability analysis and warranty data analysis.

Shuguang He is a professor in the College of Management and Economics at Tianjin University, China. He received the B.S. degree from Southwest Jiao Tong University, China, and M.S. and Ph.D. degrees from Tianjin University, China, in 1997, 2000, and 2002, respectively. His current research interests include reliability analysis, warranty data analysis, statistical process controls, and quality control using machine learning.

Acknowledgment

The authors are grateful to Geoffrey Vining for his helpful comments and suggestions.

Funding

This research was supported by National Natural Science Foundation of China Grants 71225006, 71171180, 71301117, and 71472132.