This article presents a novel methodology for the inspection scheduling of gas turbine welded structures, based on reliability calculations and overhaul findings. The model was based on a probabilistic crack propagation analysis for welds in a plate and considered the uncertainty in material properties, defect inspection capabilities, weld geometry, and loads. It developed a specific stress intensity factor and an improved first-order reliability method. The proposed routine alleviated the computational cost of stochastic crack propagation analysis, with accuracy. It is useful to achieve an effective design for manufacturing, to develop structural health monitoring applications, and to adapt inspection schedules to airplane fleet experience.

This article shows a method for inspection scheduling of structures made by additive manufacturing, derived from reliability function evaluations and overhaul inspection findings. The routine was an adaption of an existing method developed by the authors for welded components; in this latter case, the routine used a stochastic defect-propagation analysis for pores and lack of fusion defects of additive manufacturing process, instead of the weld liquation crack. In addition, the authors modified the specific stress-intensity factor for welded components to consider additive manufacturing-related material property variability, defect distributions, flaw-inspection capabilities, and component geometry. The proposed routine evaluated the failure rate and inspection intervals using the first-order reliability method (FORM + Fracture) to alleviate the computational cost of probabilistic defect-propagation analysis. The proposed method is one of the first applying reliability concepts to additive manufacturing (AM) components. This is an important milestone, since in 10 years, additive manufacturing is to be used for 30% of the components in aeroengines. This paper presents an example comparing the reliability and cost of a jet engine, with components either made by additive manufacturing or welded parts; in the process, the reliability AM-key features are found, and overhaul schedules of an airplane fleet made with AM components are defined. The simplicity and performance demonstrated in the comparison make the proposed method a powerful engineering tool for additive manufacturing assessment in aeronautics.