SBIR Phase II: Analytical Modeling and Performance Prediction of Remanufactured Gearbox Components

SBIR 第二阶段：再制造齿轮箱部件的分析建模和性能预测

• 批准号: 1330900
• 负责人: Raja Pulikollu
• 金额: $ 75万
• 依托单位: Sentient Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1330900&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Analytical; Modeling

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.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.

该第二阶段项目的创新是开发基于物理的分析模型，通过预测（1）新部件性能和最佳再制造时间，（2）再制造过程中旧部件的鉴定，以及（3）预测再制造部件性能，分析齿轮箱部件的安全性，寿命，可靠性和成本。当前的行业方法是基于传统方法设计、制造、操作和报废资产，这些方法通常依赖于基于标准的估计、历史数据/领域经验、物理检查、测试、监控和检查。这一过程非常耗费时间和资源。此外，该过程通常不考虑使用再制造过程来扩展/增强产品性能的机会。Sentient技术将解决这些问题并满足行业要求。第二阶段将扩展第一阶段的技术，在一个更全面的设计和分析框架中包括额外的齿轮箱材料、损坏模式和再制造过程，该框架能够预测最佳的再制造时间，并优化翻新操作，以延长部件的使用寿命。该SBIR技术通过虚拟测试减少了物理测试，并将有助于高价值、高要求的旋翼机、汽车和风力涡轮机齿轮箱部件的再制造。因此，它降低了能源、材料资源和与制造相关的成本，并确保保持/提高产品性能。SBIR技术的更广泛/商业影响是在航空航天、能源和运输行业中的高美元资产上，这些资产依赖于高度工程化（因此昂贵）的齿轮箱的可靠功能。我们新的基于先进制造合作伙伴关系的应用程序为美国制造业供应链提供了先行者和可持续的竞争优势，超过了6%的离岸劳动力率优势。这一优势来自于对高附加值资产的再利用，这些资产经过优化以实现最大的使用寿命，同时减少了与传统物理测试和分析方法相关的时间和成本。这是可能的，因为基于物理学的模型中包含了高层次的细节，这些模型（概念上）在微观结构层面解码材料信息，就像人类基因组计划在DNA层面解码遗传信息一样。我们的创新使客户能够快速，经济有效，准确地预测产品？的生命周期（设计、制造、运行、退化、维护、修理/再制造和退役）。我们预见到未来的机会，因为我们在NSF资助下开发的创新将为我们提供更低成本的软件和服务。我们部署该技术的成本比该领域的传统公司低10倍。