In a Circular Economy, high-value metal products, such as those in the heavy-duty industry, are returned back into the system and are ripe for remanufacturing. These returned products are called “cores” by the industry. Gauging the quality of these cores is critical to providing viable materials for any remanufacturing program.
Today, visual inspection of a core’s damaged components is not reliable enough. The potential for errors is significant. A cost-effective method for recognizing common failure modes for damaged, high-value components, such as a cylinder head, is needed. Accurately recognizing damage and failure modes, such as porosity variations and cracks, requires a much higher order of reasoning than typical machine vision methods provide.
In this project webinar, Iowa State’s Paul Kremer, the project lead, and the University of Dayton’s Gül Kremer will discuss their research, which seeks to address this issue. More specifically, automated visual defect inspection systems that can be trained and integrated using commercial off-the-shelf technologies offer the promise of rapid, reliable detection of defects. Developing system software architectures that use updateable, deep learning models coupled with other established image acquisition and processing techniques is part of the core task in putting together a workable system. However, calibration and validation of each hardware/software component, and of the overall system, through multiple calibration and benchmarking processes with clear definition of ground truth, is of critical importance.
In this webinar, a case study will be presented describing the integration of a software and hardware system for defect detection in cylinder heads undergoing remanufacturing.
Guest Speakers
Paul A. Kremer is a Manager Research in the Department of Civil, Construction and Environmental Engineering at Iowa State University. He is active in national and international standards development and in the design and development of unique, bench-scale and full-scale laboratory facilities across a number of research domains. These include hardware and software systems for automated inspection and damage detection integrating varied technologies.
Gül E. Kremer has degrees in industrial engineering from Yildiz Technical University, an MBA from Istanbul University and a PhD in Engineering Management from Missouri University of Science and Technology. She has been a National Research Council-US AFRL Summer Faculty Fellow in the Human Effectiveness Directorate from 2002 to 2004, and a Fulbright Scholar (2010-2011). She served as a Program Director in the National Science Foundation’s Division of Undergraduate Education between 2013 and 2016. Dr. Kremer’s research interests include applied decision analysis to improve complex products and systems, and engineering education. The results of her research efforts have been presented in various publications including 3 books and more than 300 refereed publications. Several of her papers have been recognized with Best Paper awards. She is a Fellow of the American Society for Mechanical Engineers (ASME), and a senior member of the Institute of Industrial Engineers (IIE). She has served as the Chair of Design Education and Design for Manufacturing and Lifecycle Technical Committees of the Design Engineering Division of ASME.
