Robert X. Gao - US grants

9624353 Gao This CAREER award will investigate the research issues associated with "smart" bearings that embed and integrate transducers and microelectronics for data processing for diagnostics and predictive maintenance. The educational plan will produce globally oriented engineers that understand competitive pressures and have experienced cross-disciplinary teaming. The research plan starts with quantifying bearing failure signatures, considers microelectronics and miniaturization of sensors and data processing into a prototype smart bearing, and bench scale and cooperative industrial experiments for optimization reliability. The educational program has components of percolate outreach through the University of Massachusetts-Amherst's K-12 Outreach Committee, and undergraduate course in mechatronic system design, and a graduate course on random data analysis and measurement. The impact of this innovative research plan is that it is a key component of predictive maintenance, with implications for productivity, availability and most importantly, safety, of manufacturing machines and transportation systems. The educational plan will enable the students to think critically and communicate better in a team environment, making the nations human resources more competitive in a global environment.

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Gao
The objective of this project is to develop a new design course sequence in the broad area of Assistive Technology for undergraduate students in the Mechanical and Industrial Engineering (MIE) Department, University of Massachusetts Amherst. A two-semester design course entitled "Senior Design Projects to Aid the Disabled" is to be developed and integrated within the established undergraduate curriculum of the Department.

Through close collaborations with the Lemelson Assistive Technology Development Center (LATDC) at Hampshire College and Adaptive Design Services (ADS) under the Massachusetts Department of Mental Retardation (DMR), the new design course sequence is to apply rigorous analytical and computer simulation approaches to specific design problems originated by disabled clients. The output of each design project will be a prototype of a functional mechanical and/or electromechanical device that satisfies the specific need of an individual client. The new course will strengthen the existing undergraduate curriculum by introducing mechanical and industrial engineering students to a new area of great social importance. It further enhances the Department's effort in promoting its newly identified research thrust area in assistive technology and biomedical engineering.

The objective of this research is to enable a new generation of remote sensors for injection molding process monitoring and control. This research is motivated by current limitations in observing part quality during manufacture that allow the occurrence of defective products and cost inefficiencies. The new sensors will be self-energized by extracting energy from the intensification and decay of polymer melt pressure to generate and transmit signal pulses that correspond to changes in the molding process. This approach promises to reduce the energy consumption by several orders of magnitude. Issues to be investigated include sensor design, simulation, heat conduction, structural integrity, signal encoding, and optimal transmission. The research represents a radical step forward in sensing technology, and has the potential to change industrial molding practice by enabling better algorithms for molding control. The potential increase on productivity is estimated to be $200 million/year.

This research project, being a close collaboration between the faculty researchers and their industrial partner at Dynisco Instruments, may provide validated process sensing technology for many other applications beyond injection molding. The self-energizing and wireless signal transmission concept promises to greatly reduce the information bottleneck out of manufacturing processes. Finally, the research has a great potential to fundamentally impact the manufacturing and design curriculum and ultimately, the educational infrastructure the researchers' home institution.

This Grant Opportunity for Academic Liaison with Industry (GOALI) provides funding for the development of a unified time-scale-frequency domain signal processing technique that is germane to the health assessment and time-to-failure prediction of rolling bearings. The project is aimed at bridging an existing gap between the state-of-the-art of signal processing research in the academia and current bearing condition monitoring practice on the factory floor, and will focus on establishing the theoretical foundation for an effective and efficient algorithm to estimate a defective bearing's damage status and remaining service life. The research will consists of numerical simulations in multiple domains, custom-designed experimental validation, and systematic analysis of historical data provided by the industrial partner of this project. The developed technique will be implemented in the form of a bearing assessment firmware, residing on a Digital Signal Processor platform, and installed in a handheld bearing data analyzer, suited for direct industry translation to bearing manufacturers and end-users.

This research, if successful, will lead to improved capability of concurrent machine fault feature extraction in multiple domains. Such a technique will be essential to reducing costly and unexpected machine downtime, and improving product quality. In a broader context, the research will enhance existing curricula in manufacturing and mechanical engineering, create new, research-based educational materials, promote collaborations between a major public university and the bearing industry, and improve the overall education infrastructure at the PI's institution. Ultimately, by being able to more reliably predict the health status of a machine system and improve operation safety and productivity, the research has the potential to impact a wide range of manufacturing processes other than bearings.

This grant provides funding to establish a unified framework for the management of large-scale, embedded sensor networks for the health monitoring of manufacturing enterprises and complex systems operating in stochastic environments. The project will focus on fundamental research issues in the areas of distributed inferencing and decision-making for adaptive sensing and sensor communication. Specifically, it will address the following issues: 1) development of a theoretical basis for managing large number of networked sensors that collectively form an "intelligent" sensing grid, capable of making distributed inferences with guarantees on consistency of data interpretation, and 2) deployment of architectural configuration and parametric design of networkable, miniaturized sensors to physically realize the proposed "intelligent" sensing grid using a novel "agent-on-a-chip" (AOC) firmware.

If successful, the results of this research will lead to improvements in two areas. First, the research will lead to a new theory and quantitative guidelines for the architectural design and deployment of networked sensors that are characterized by the ability to perform event-driven, dynamic inferencing and distributed decision-making at the local sensor level. Secondly, it will demonstrate, the feasibility of a new sensor network architecture that will physically embody, bench test, and validate a large number of sensors prototyped based on the state-of-the-art miniaturization technologies. Such hardware and software will be applicable in a wide range of application areas, including manufacturing, transportation, health care, medical devices, environmental control, and homeland security. The broader impact of this research will be in bridging the gap between fundamental research on decision theory, Bayesian networks, and embedded sensing chip design in the academia and real-world applications in a manufacturing environment. On the educational front, the proposed activity will contribute to multidisciplinary training of graduate and undergraduate students in the area of management of large-scale sensor networks.

The objective of this Small Sensor Team (SST)/Collaborative Research project is to significantly improve the observability in polymer processing operations by providing feedback of pressure, temperature, flow rate, and other process states at multiple locations within the mold cavity, for real-time process and quality control. This objective will be achieved through simultaneous in-mold pressure-temperature sensing, multi-domain signal processing, and injection molding process analysis.

The benefit of this research is that it will introduce a new generation of miniaturized sensors that can be placed at practically anywhere within the mold cavity that are previously not possible, and in much less time than previously required to modify a mold for placing a conventional sensor. The broader impact of this research is three-fold: 1) it represents a radical step forward from the current wired sensors that have been used for decades, and has the potential to significantly improve molding productivity wherein each percentage point improvement corresponds to cost savings over $40 million per year, 2) the new sensing technique will not only be applicable to injection molding, but also to other high-energy manufacturing processes, and 3) the research will contribute substantially to multidisciplinary education and training of graduate and undergraduate students and enhance manufacturing curriculum development at the PIs' institutions.

This grant provides funding to organize the 2009 CMMI Grantees and Research Conference in Honolulu, Hawaii, on June 22nd -25th, 2009. The goal of the conference is to provide a forum for the Division of Civil, Mechanical and Manufacturing Innovation grantees to showcase their research outcomes and network with researchers from the Far East and Pacific-Rim regions to foster closer international cooperation in research and education. The conference will consist of keynote presentations, plenary sessions, workshops, poster sessions, breakout sessions, social activities and informal interactions, which will result in seeding new partnerships and generating cross-disciplinary research ideas.

The conference theme draws attention to the ever-changing landscape at the frontier of science and technology research across the globe. It will enable researchers and educators from across the Pacific Rim to discuss their activities and form collaborations, and will likely result in an advanced pace of research across the fields supported by the Division of Civil, Mechanical and Manufacturing Innovation.