NSF Award
1330900
The Innovation of this Phase II project is developing physics-based analytical models to analyze gearbox components for safety, longevity, reliability and cost by predicting (1) New component performance, and optimal time-to-remanufacture, (2) Qualification of used components for remanufacturing process, and (3) Predicting the remanufactured component performance. Current industry approach is to design, manufacture, operate, and retire assets based on traditional methods, which typically rely on standards-based estimates, historical data/domain experience, physical examination, testing, monitoring, and inspection. This process is extremely time and resource intensive. Further, this process often does not consider the opportunity to use remanufacturing processes to extend/enhance product performance. Sentient technology will address these issues and fulfill the industry requirements. Phase II will expand the Phase I technology to include additional gearbox materials, damage modes and remanufacturing processes in a more comprehensive design and analysis framework capable of predicting optimal time-to-remanufacture and optimizing refurbishing operations to extend the useful life of components. This SBIR technology reduces physical testing using virtual testing, and will assist in remanufacturing of high value, high demand rotorcraft, automotive and wind turbine gearbox components. Hence, it decreases the energy, material resources, and costs associated with manufacturing, and ensures that the product performance is maintained/improved.<br/><br/>The broader/commercial impact of the SBIR technology is within aerospace, energy, and transportation industries on high dollar assets that rely on the reliable function of highly engineered (and thus expensive) gearboxes. Our new Advanced Manufacturing partnership based application provides the US manufacturing supply chain a first mover and a sustainable competitive advantage greater than the 6% offshore labor rate advantage. This advantage comes through reuse of high value-added assets optimized for maximum lifetime use, coupled with decreased time and costs associated with traditional physical testing and analysis methods. This is possible based on the high-level of detail included in physics-based models, which (conceptually) decode material information at the microstructure level just as the Human Genome Project decodes genetic information at the DNA level. Our innovation enables the customer to rapidly, cost effectively, and accurately predict a product?s lifecycle (design, manufacture, operation, degradation, maintenance, repair/remanufacture, and retirement) at the material, component, and assembly/system scales. We foresee a future opportunity due to the fact that our innovation developed under this NSF grant will give us a competitive advantage of lower costs to provide the software and service. Our cost to deploy the technology is 10X lower than traditional companies in this space.
