Publications

Abstract not provided.

We are developing the capability to track material changes through numerous possible steps of the manufacturing process, such as forging, machining, and welding. In this work, experimental and modeling results are presented for a multiple-step process in which an ingot of stainless steel 304L is forged at high temperature, then machined into a thin slice, and finally subjected to an autogenous GTA weld. The predictions of temperature, yield stress, and recrystallized volume fraction are compared to experimental results.

The process of verification and validation can be resource intensive. From the computational model perspective, the resource demand typically arises from long simulation run times on multiple cores coupled with the need to characterize and propagate uncertainties. In addition, predictive computations performed for safety and reliability analyses have similar resource requirements. For this reason, there is a tradeoff between the time required to complete the requisite studies and the fidelity or accuracy of the results that can be obtained. At a high level, our approach is cast within a validation hierarchy that provides a framework in which we perform sensitivity analysis, model calibration, model validation, and prediction. The evidence gathered as part of these activities is mapped into the Predictive Capability Maturity Model to assess credibility of the model used for the reliability predictions. With regard to specific technical aspects of our analysis, we employ surrogate-based methods, primarily based on polynomial chaos expansions and Gaussian processes, for model calibration, sensitivity analysis, and uncertainty quantification in order to reduce the number of simulations that must be done. The goal is to tip the tradeoff balance to improving accuracy without increasing the computational demands.

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In forged, welded, and machined components, residual stresses can form during the fabrication process. These residual stresses can significantly alter the fatigue and fracture properties compared to an equivalent component containing no residual stress. When performing lifetime assessment, the residual stress state must be incorporated into the analysis to most accurately reflect the initial condition of the component. The focus of this work is to present the computational and experimental tools that we are developing to predict and measure the residual stresses in stainless steel for use in pressure vessels. The contour method was used to measure the residual stress in stainless steel forgings. These results are compared to the residual stresses predicted using coupled thermo-mechanical simulations that track the evolution of microstructure, strength and residual stress during processing.

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