| NSF Org: |
ECCS Division of Electrical, Communications and Cyber Systems |
| Recipient: |
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| Initial Amendment Date: | September 4, 2015 |
| Latest Amendment Date: | September 4, 2015 |
| Award Number: | 1543872 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Radhakisan Baheti
ECCS Division of Electrical, Communications and Cyber Systems ENG Directorate for Engineering |
| Start Date: | September 1, 2015 |
| End Date: | August 31, 2018 (Estimated) |
| Total Intended Award Amount: | $300,000.00 |
| Total Awarded Amount to Date: | $300,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
2200 W MAIN ST DURHAM NC US 27705-4640 (919)684-3030 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
130 Hudson Hall Durham NC US 27708-9029 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
NANOSCALE: INTRDISCPL RESRCH T, ENG IDR-Eng Interdisciplin Res |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
This EArly-concept Grant for Exploratory Research (EAGER) award supports fundamental research on the design of an agile manufacturing exchange system (MES) in which suppliers of raw materials, assemblers, transportation companies, etc., will participate through standardized protocols to fulfill complex manufacturing orders. This design will provide the foundation for a smart software mediation layer (i.e., a "broker") that will enable a MES to be self-learning and adaptive to dynamic/diverse service requests and resource availability, as well as support a large network of service providers and users within a complex information ecosystem. The economic competitiveness of the U.S. dependss on new and innovative methods for intelligent mass customization systems for the manufacturing sector, which will enable the processing of small-sized and diverse orders that demand almost instant fulfillment. The MES will enable this transformation by supporting on-demand integration of resources, graceful recovery from failures, and dynamic adaptation without any disruption in operations.
In order to meet these goals, research will be focused on adaptation to emerging system behaviors by dynamically evolving optimization strategies in real-time. Users and providers will be connected in a dynamic manufacturing network that will accommodate multiple product flows, uncertainty in links between providers and themselves, and fault tolerance to provide service despite failed network components. This level of adaptation, seamless efficiency, and uninterrupted service will constitute a significant step forward towards a smart MES. The research goals will be accomplished through the design of a distributed real-time optimization and knowledge discovery framework that will address workflow optimization, resource allocation, and data-driven performance prediction in a dynamic manufacturing network of users, brokers, and providers. The specific research tasks include online admission control policies, dynamic production planning, analysis and prediction of service-level performance for forecasting, distributed methods for dynamic resource allocation under uncertainty, and visual analytics techniques to support human decision makers and situational awareness.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The goal of this seed-grant project was to study how small businesses and others can be enabled to manufacture small batches of complex devices at low cost. Our approach was to develop an agile manufacturing exchange (ME) in which suppliers of raw materials, assemblers, transportation companies, banks, etc., participate through standardized protocols to fulfill manufacturing orders.
The accomplishments of this grant were relatively modest compared to initial expectations and plans. Progress was made on the study of anomaly detection as a means to predict failures and take pro-active action. The anomaly detection component consists of three subcomponents: comparative analyzer, threshold analyzer, and correlation analyzer. The comparative analyzer uses standard statistical characteristics to filter a large amount of features that are not likely to contain anomalies. The threshold analyzer does more fine-grained filtering to lock on suspect features whose variations across temporal domain show significant abnormalities. Both time-series analysis and machine-learning algorithms were utilized in this subcomponents. The correlation analyzer will not only removes redundant anomalies, but also builds relationships among different service providers as well as between features, therefore helping to identify root causes of failures or malicious behavior.
A decentralized primal-dual method for online distributed optimization was developed to handle global constraints. The PI participated in the NSF cybermanufacturing workshop in Berkeley, CA in 2015 and efforts are underway to prepare a follow-up grant proposal.
Last Modified: 11/30/2018
Modified by: Krishnendu Chakrabarty
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