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.

该学术与工业联络机会 (GOALI) 奖项的研究目标是创建一个计算框架，用于安全关键部件（例如风车薄壁轴承）制造过程的风险承受设计。 这些部件容易变形和过早失效，这对风车系统的设计造成了不希望有的限制。本研究将开发一种使用分层贝叶斯网络方法在计算设计框架中捕获制造过程不确定性和故障诊断的方法。 它将从概率公式中过程不确定性和相关失效响应的表示发展到将其嵌入到过程设计的连续体表示及其几何、微观结构和损伤状态的相关变换中。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。可交付成果包括用于流程设计的计算工具、贝叶斯风险推断算法、风车系统设计的应用结果以及校准和验证实验的结果。如果成功，这项研究将使下一代产品的设计者能够考虑流程设计和从而导致设计决策中的性能不确定性。 他们不仅能够提高当前设计的使用寿命和可靠性，而且能够设计出更轻、功率密度更高和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。在这些部分，不良工艺设计的风险会产生严重的社会后果。 工程专业的学生，​​尤其是少数族裔和女性，将受益于在研究生和本科生课程中引入概率计算过程设计。此外，学生还将有机会在项目团队以及暑期实习计划中与工业合作伙伴铁姆肯公司合作。