GOALI: Bayesian Hierarchical Network based Computational Framework for Risk Tolerant Process Design

GOALI：基于贝叶斯分层网络的风险容忍流程设计计算框架

• 批准号: 1000330
• 负责人: Rajiv Shivpuri
• 金额: $ 39万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-15 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1000330&HistoricalAwards=false
• 关键词: GOALI; Bayesian; Hierarchical; Network; based

The research objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) award is to create a computational framework for risk tolerant design of processes in the manufacture of safety critical parts such as thin walled bearings for wind mills. These parts are prone to distortion and premature failure which provides an undesirable constraint on the design of windmill systems. This research will develop a method that captures manufacturing process uncertainties and failure diagnostics in the computational design framework using hierarchical Bayesian network approach. It will progress from the representation of process uncertainties and associated failure response in a probabilistic formulation to embedding this in the continuum representation of process design and its associated transformations in geometry, microstructure and damage state. The physical models of the material state will include the relationship of process variance with the failure state and risk. Deliverables include computational tools for process design, algorithms for Bayesian inference of risk, results of application to design of wind mill systems, and results of calibration and validation experiments.If successful, this research will enable designers of next generation products to consider process design and resulting performance uncertainty in their design decisions. They will be able to not only increase the service life and reliability of their current designs but also design products of which are lighter, with higher power density and longer service lives. Example applications include large thin walled windmill and aeroengine bearings, transmission components in power generation and safety critical parts in nuclear industry. In these parts the risk of poor process design has severe societal consequences. Engineering students especially minorities and women will benefit greatly from the introduction of probabilistic-computational process design in graduate and undergraduate curricula. In addition, students will get opportunities to work with the industrial collaborator Timken in project teams as well as in summer internship programs.

该研究的目的是为学术界与工业界的联系（GOALI）奖的赠款机会是创建一个计算框架的风险容忍设计的过程中的安全关键部件，如薄壁轴承风米尔斯的制造。 这些部件易于变形和过早失效，这对风车系统的设计提供了不期望的约束。本研究将开发一种方法，捕捉制造过程中的不确定性和故障诊断的计算设计框架，使用分层贝叶斯网络方法。 它将从过程的不确定性和相关的故障响应的概率公式的表示过程设计及其相关的几何形状，微观结构和损伤状态的转换的连续表示嵌入这一进展。材料状态的物理模型将包括过程方差与故障状态和风险的关系。这些成果包括工艺设计的计算工具、贝叶斯风险推理算法、风力机系统设计的应用结果以及校准和验证实验结果，如果成功，这项研究将使下一代产品的设计人员能够在设计决策中考虑工艺设计和由此产生的性能不确定性。 他们不仅能够提高现有设计的使用寿命和可靠性，还能够设计出更轻、功率密度更高、使用寿命更长的产品。 示例应用包括大型薄壁风车和航空发动机轴承、发电中的传动部件以及核工业中的安全关键部件。在这些地区，工艺设计不当的风险会产生严重的社会后果。 工程专业的学生，特别是少数民族和妇女将大大受益于概率计算过程设计在研究生和本科课程的介绍。此外，学生将有机会与铁姆肯公司的工业合作伙伴一起参加项目团队和暑期实习计划。