High-performance failure-critical parts such as aeroengine disks are manufactured in a sequence of processing steps such as solidification, deformation, heat treatment and finishing. The fatigue and failure performance of these parts are often governed by the material and processing state. Uncertainties in the material structure, defects and anomalies play a major role in the uncertainties in performance. This paper includes a hybrid computational framework for integrated materials and process design with achieved through model decomposition based on Bayesian probabilistic formulation. Efficacy of this approach is demonstrated by applying it to the selection of forging parameters for maximizing life of titanium disk with hard alpha anomaly.
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