Abstract: Inexpensive computing and sensing technology is making it possible to measure and store the history of manufacturing equipment and products, infrastructure components, and medical patients. A challenge for computer science is to make effective use of this flood of data. We will develop and demonstrate algorithms, technology, and paradigms for the use of this information in advanced manufacturing. The scope of manufacturing and design will be extended throughout the lifetime of the product. Information from a product such as a piece of factory automation equipment will be used to develop software upgrades for that individual piece of equipment, as well as to refine the designs of new equipment. We will develop machine learning algorithms to detect patterns, optimize performance, customize product behavior, and predict failures. We will also develop large scale simulations to test and verify these learning algorithms, as well as to allow the construction of virtual factories to evaluate manufacturing process designs. We will develop networking services that allow products to communicate with their factories or maintenance organizations, and allocate processing and sharing of data efficiently.
PI: Christopher G. Atkeson (email@example.com)
Co-PIs: Ronald C. Arkin (firstname.lastname@example.org), Richard M. Fujimoto (email@example.com), Ashok K. Goel (firstname.lastname@example.org), Jessica K. Hodgins (email@example.com), Amarnath Mukherjee (firstname.lastname@example.org), Ashwin Ram (email@example.com), and Karsten Schwan (firstname.lastname@example.org).
This piece of factory automation is manufactured by another company for a broad range of applications. However, if the packager can customize itself for the particular characteristics of the object it is packaging, it can be much more cost effective. The packager manufacturer may provide no flexibility or a set of simple controls to allow a factory worker to adjust for product mean weight, for example. A packager that can track the characteristics of the process such as the variance of the weights can make much better decisions as to which object to pack into which package.
The packaging equipment needs to monitor itself to predict maintenance needs. For example, the monitoring system of a bearing in the coolant system could detect a change in the pattern of vibration. This change might match a change discovered in other bearings manufactured in the same batch, and indicate an impending failure. Constant monitoring by dedicated equipment will replace periodic monitoring by human technicians due to cost and more importantly, reliability. This provides an opportunity to minimize the costs of actual human involvement in routine equipment maintenance and replacement, by scheduling the maintenance when and where it is needed.
2) We will develop sources of actual data. We would like to instrument a set of manufacturing processes and several homes to provide real data to develop our algorithms on. This data stream would be made available on the Internet as a general resource.
3) On-line simulation. We need to understand how to couple product design and manufacturing simulations to real-time information on product use. We need to understand how to make such simulations deal with both real-time data and also offer interactive interfaces to enable human users to play `what if' games, to try different solution approaches, etc.
4) Large scale simulations. We need to simulate realistically sized systems. We will develop large scale simulations to provide a general facility for developers to use to test algorithms. These simulations would be available on the Internet as a general resource. In addition, we need to integrate different aspects of manufacturing design, such as combining fluid flow or combustion models simulating dynamic processes in combustion engines with structural models simulating rigid engine components.
5) Intelligent human interfaces. The best way to understand data is to visualize it, and we will need to develop tools that allow humans to explore the data stream generated by these embedded systems. Prototype interfaces will be made available on the Internet as a general resource.
Technology transfer is facilitated by the presence of several on campus centers and programs:
The Manufacturing Research Center (MARC) represents a major commitment to manufacturing related research at both the Institute and State levels. At least 4 Industrial sponsors have donated one million dollars or more to be affiliated with this Center. This unit is housed in its own building and affords numerous opportunities for presentation and interaction with potential industrial collaborators. We have discussed our proposal with the MARC director, and we are exploring the possibility of instrumenting and experimenting with on campus manufacturing processes.
The Materials Handling Research Center consists of approximately 30 member companies and 4 universities. Twice annually this group meets on campus, providing us with the opportunity to disseminate our research results to this community as well as directly receive industrial feedback.
The computer integrated manufacturing (CIMS) program at Georgia Tech is a multi-disciplinary endeavor geared to support manufacturing-related education on campus. Student projects could be readily integrated into the proposed research and awarded credit for their participation.
The CIMS/AT&T intelligent mechatronics laboratory provides excellent resources for hands-on manufacturing education available to the investigators of this proposal. AT&T has donated $225,000 to support this laboratory and is keenly interested in projects such as what we are proposing and how it can impact manufacturing education.
Additionally Georgia Tech has recently won a TRP award in excess of $1 million to further enhance their manufacturing educational laboratory facilities and manufacturing curriculum.
Finally, an Integrated Process and Product Design (IPPD) Lab, heavily interdisciplinary in nature, and being funded by the U.S. Army is being set up to support research in the design of complex intelligent unmanned systems. These resources would likely also be available in some capacity for use within this research.