Development of Smart Test Plans by Linking Modeling and Physical Testing

Residual stress testing and measurement can be applied at various stages of a product lifecycle. In the developmental phase the measurements are typically used alongside processing parameters with the goal of understanding processing impact to residual stresses. In this phase process modeling is often applied, which can be a very useful guide for defining location-specific regions with unique attributes, such as residual stress. Application of modeling and identification of regions of interest can be extremely useful to guide the creation of a measurement plan and reducing the experimental iterations. In the manufacturing phase the residual stress measurements can be used as quality control checks. In the next phase of product testing and validation, residual stress measurements could be used to validate that the engineered and process-induced residual stresses are indeed meeting design intent, and incorporated residual stresses within explicit design and structural analyses. When a product is operational use, field support activities such as maintenance, repairs, and failure investigation can use residual stress measurement as an investigative tool. In all of the above stages of a component lifecycle, the measurement plan must support the key objectives and take into account several considerations such as: required measurement reproducibility and/or accuracy, availability of component locations that allow specific location testing, damage allowable to the part or component, measurement reliability requirements, measurement methodology constrains and economics constrains, such as time and cost available for the task. The aim of the current presentation is to address these elements and give an example of measurement test planning linked to and guided by computational modeling and simulation. Physical testing alone may require large numbers of tests to achieve the required validation and uncertainty, while combining physical testing and computational modeling can focus tests and test locations on those critical to the goal.