Predictive Maintenance
Automatic Feature Extraction for Monitoring of Multiple Operation Forging Processes March, 2009 Update
Ran Jin, FIERF Graduate Fellowship Grant, Industrial and Operations Engineering Department, University of Michigan.
Qingyu Yang, Qiang Li and Judy Jin, Department of Industrial and Operations Engineering, The University of Michigan, Ann Arbor, MI, In this research, a new method is developed to draw conclusions on individual die loads, their fluctuation and significance on process variation. 2009-10 FIERF Graduate Fellowship
Yong Lei*, Yu Zhou*, Jionghua Jin*, Jianjun Shi*, Tzyy-Shuh Chang+, * Industrial & Operations Engineering Department, University of Michigan, + OG Technologies Inc. The objective of this research is to develop an effective monitoring and diagnosis system through automatic sequential control charts generation for different processes and adaptive learning from production data for continuous improvement. 2007 FIERF Forging Achievement Award Paper.
Magnet School Professor Jianjun Shi, The University of Michigan. This project melds real-time dimensional control, monitoring of equipment and the ability to predict future equipment functionality and product quality. Partners include National Science Foundation, Michigan 21st Century Jobs Fund, American Axle and Manufacturing, FormTech, MACSTEEL, OG Technologies and Stealth Medical Technologies.
Judy Jin, The University of Michigan, and Andreas Sterzing, Jurgen Steger, Tim Lehnert, IWU, Fraunhofer
In this research project funded by FIERF, the research team conducted experimental studies to investigate how three typical variables, e.g. workpiece geometry, part temperature and hydraulic ram off-center error, affect tonnage signals.