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.
Uncertainties associated with loading, materials and manufacturing introduce considerable risk in the design of failure critical parts such windmill bearings and aeroengine disks. In this project, the design process was decomposed into a multilevel Bayesian probabilistic framework that links process design to the part design and performance. A causal design framework was developed that can link with the current design practices and life estimation models. The Bayesian formulation of this framework enables the use of legacy data on the performance and manufacture of current systems to be extended to the design of future systems where the service data is largely unavailable. The approach developed in this project, enables the incorporation of material and manufacturing risks via the probabilistic failure theories. It enables the design of systems that are risk tolerant, environmentally benign and reliable. This design approach is an alternative to the "safe life" approaches currently used in industry that lead to overdesigned systems that cannot handle material and manufacturing defects. Examples of these poorly designed systems include power plants and structures that cannot withstand environmental risk, transportation systems that are prone to failure, defense systems that are brittle, and software systems that can be easily compromised. While there is considerable previous work in the probabilistic design of complex engineered systems, with approached developed to incorporate uncertainties in the deterministic paradigm, the role of material and manufacturing in the design risk has been largely ignored. This often leads to catastrophic failures and product recalls. This GOALI project enabled the OSU research team to work closely with researchers from the industrial partner (Timken Company) in the development of an integrated approach to risk tolerant system design. Students working in this project had the unique opportunity to interact with industrial designers, and to learn the tradeoffs necessary in the design of industrial systems.
