Anagrafe della ricerca

Damage tolerance in design of metallic structures of aircraft is among the most significant accomplishments in the aerospace technology. This fundamental achievement was due mostly to the work of eminent scientists and engineers whose work has led to the development of multidisciplinary tools and knowledge to control failure of structures (i.e., fatigue behaviour, progressive crack growth, non-destructive testing, residual stress evaluation, etc.). With the advent of advanced composite structures, we are today in a comparable paradigm. Aerospace composite structures are nowadays realized by means of highly automated layup processes and then consolidated through autoclave. Albeit improved over the last twenty years, the Automated Tape Laying (ATL) and the Automated Fibre Placement (AFP) technologies are affected by meso- to micro-scale defects which ultimately result into mechanical deficiencies of the structural components. Furthermore, available computational models and failure criteria are unable to characterize the propagation of those unwanted manufactured-induced defects within the composite materials. As a result, most of the aeronautical composites are designed according to the "no-crack growth" principle, which inevitably leads to unjustified heavy structures. The project “muLti-scale continuOus and coUpled non-local moDels (LOUD) for failure analysis and damage detection in 3D printed composites” ambitiously aims at setting new methodologies and design tools to enable the characterization and the health monitoring of damage tolerant advanced aerospace structures. The focal point of LOUD will be the development of local-non-local models coupling advanced finite element methods based on classical elasticity and peridynamics, which is a continuous version of molecular dynamics. Peridynamics is effective for progressive failure analysis but also computationally demanding. Thus, peridynamic regions will be used only in those zones of the finite element model where crack propagation is happening. These models will be thus employed in conjunction with machine learning algorithm, surrogate modelling and random fields to carry out defect uncertainty analysis and damage detection. Particular emphasis will be given to advanced variable stiffness composites along with traditional ones. Also, we will investigate the use of the methods developed for the analysis of structures and materials obtained with innovative 3D printing processes, including Fused Filament Fabrication (FFF), Continuous Fiber 3D Printing (CF3D) and Composite Fiber Coextrusion (CFC), which are expected to change the manufacturing paradigms in the next decades.