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

Ohio State University, The, Columbus OH

Investigators

Abstract

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.