Materials engineering and damage tolerance assessment have traditionally been performed as disjoint processes involving repeated tests that can ultimately prolong the time required for certification of new materials. Computational advances have been made both in the prediction of material properties and probabilistic damage tolerance analysis, but have been pursued primarily as independent efforts. Integrated computational materials engineering (ICME) has the potential to significantly reduce the time required for development and insertion of new materials in the gas turbine industry. A manufacturing process software tool called DEFORM™ has been linked with a probabilistic damage tolerance analysis (PDTA) software tool called DARWIN® to form a new capability for ICME of gas turbine engine components. DEFORM simulates rotor manufacturing processes including forging, heat treating, and machining to compute residual stress and strain, track anomaly location, and predict microstructure including grain size and orientation. DARWIN integrates finite element stress analysis results, fracture mechanics models, material anomaly data, probability of anomaly detection, and inspection schedules to compute the probability of fracture of a gas turbine engine rotor as a function of operating cycles. Previous papers have focused on probabilistic modeling of residual stresses in DARWIN based on manufacturing process training data from DEFORM. This paper describes recent efforts to extend the probabilistic link between DEFORM and DARWIN to enable modeling of residual strain, average grain size, and ALA (unrecrystalized) grain size as random variables. Gaussian Process modeling is used to estimate the relationship among model responses and material processing parameters. These random variables are applied to microstructure-based fatigue crack nucleation and growth models for use in probabilistic risk assessments. The integrated DARWIN-DEFORM capability is demonstrated for a representative engine disk model which illustrates the influences of manufacturing-induced random variables on component fracture risk. The results provide critical insight regarding the potential benefits of integrating probabilistic computational material processing models with probabilistic damage tolerance-based risk assessment.