We present an application of data analytics and supervised machine learning to allow accurate predictions of the macroscopic stiffness and yield strength of a unidirectional composite loaded in the transverse plane. Predictions are obtained from the analysis of an image of the material microstructure, as well as knowledge of the constitutive models for fibres and matrix, without performing physically-based calculations. The computational framework is based on evaluating the 2-point correlation function of the images of 1800 microstructures, followed by dimensionality reduction via principal component analysis. Finite element (FE) simulations are performed on 1800 corresponding statistical volume elements (SVEs) representing cylindrical fibres in a continuous matrix, loaded in the transverse plane. A supervised machine learning (ML) exercise is performed, employing a gradient-boosted tree regression model with 10-fold cross-validation strategy. The model obtained is able to accurately predict the homogenized properties of arbitrary microstructures.

A numerical and experimental study of ballistic impacts at various temperatures on precipitation hardened Inconel 718 nickel-base superalloy plates has been performed. A coupled elastoplastic-damage constitutive model with Lode angle dependent failure criterion has been implemented in LS-DYNA non-linear finite element code to model the mechanical behaviour of such an alloy. The ballistic impact tests have been carried out at three temperatures: room temperature (25 °C), 400 °C and 700 °C. The numerical study showed that the mesh size is crucial to predict correctly the shear bands detected in the tested plates. Moreover, the mesh size convergence has been achieved for element sizes on the same order that the shear bands. The residual velocity as well as the ballistic limit prediction has been considered excellent for high temperature ballistic tests. Nevertheless, the model has been less accurate for the numerical simulations performed at room temperature, being though in reasonable agreement with the experimental data. Additionally, the influence that the Lode angle had on quasi-static failure patterns such as cup-cone and slanted failure has been studied numerically. The study has revealed that the combined action of weakened constitutive equations and Lode angle dependent failure criterion has been necessary to predict the previously-mentioned failure pattern

The new generation jet engines operate at highly demanding working conditions. Such conditions need very precise design which implies an exhaustive study of the engine materials and behaviour in their extreme working conditions. With this purpose, this work intends to describe a numerically-based calibration of the widely-used Johnson–Cook fracture model, as well as its validation through high temperature ballistic impact tests. To do so, a widely-used turbine casing material is studied. This material is the Firth Vickers 535 martensitic stainless steel. Quasi-static tensile tests at various temperatures in a universal testing machine, as well as dynamic tests in a Split Hopkinson Pressure Bar, are carried out at different triaxialities. Using ABAQUS/Standard and LS-DYNA numerical codes, experimental data are matched. This method allows the researcher to obtain critical data of equivalent plastic strain and triaxility, which allows for more precise calibration of the Johnson–Cook fracture model. Such enhancement allows study of the fracture behaviour of the material across its usage temperature range