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.

GOALI学术联络机会奖的研究目标是为制造安全关键部件(如风车的薄壁轴承)过程中的风险容限设计创建一个计算框架。这些部件容易变形和过早损坏，这给风车系统的设计带来了不良的限制。这项研究将开发一种方法，利用分层贝叶斯网络方法在计算设计框架中捕获制造过程的不确定性和故障诊断。它将从用概率公式表示工艺不确定性和相关的失效响应，到将其嵌入到工艺设计的连续表示中，以及它在几何、微观结构和损伤状态方面的关联变换。材料状态的物理模型将包括过程差异与故障状态和风险的关系。可交付成果包括工艺设计的计算工具、风险的贝叶斯推理算法、应用于风车系统设计的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。他们将不仅能够增加目前设计的使用寿命和可靠性，而且还可以设计出更轻、更高功率密度和更长使用寿命的产品。应用实例包括大型薄壁风车和航空发动机轴承、发电中的传动部件和核工业中的安全关键部件。在这些地区，糟糕的工艺设计风险具有严重的社会后果。工程学学生，特别是少数族裔和妇女，将从研究生和本科生课程中引入概率计算过程设计中受益匪浅。此外，学生们还将有机会在项目团队和暑期实习项目中与行业合作伙伴铁姆肯合作。