With the increasing use of CubeSats in space exploration, the demand for reliable high-temperature shape memory alloys (HTSMA) continues to grow. A wide range of HTSMAs has been investigated over the past decade but finding suitable alloys by means of trial-and-error experiments is cumbersome and time-consuming. The present work uses a data-driven approach to identify NiTiHf alloys suitable for actuator applications in space. Seven machine learning (ML) models were evaluated, and the best fit model was selected to identify new alloy compositions with targeted transformation temperature (Ms), thermal hysteresis, and work output. Of the studied models, the K-nearest neighbouring ML model offers more reliable and accurate prediction in developing NiTiHf alloys with balanced functional properties and aids our existing understanding on compositional dependence of transformation temperature, thermal hysteresis and work output. For instance, the transformation temperature of NiTiHf alloys is more sensitive to Ni variation with increasing Hf content. A maximum Ms reduction rate of 6.12 °C per 0.01 at.% Ni is attained at 30 at.% Hf, and with a Ni content between 50 and 51 at.%. Graphical abstract

An experimental and numerical study on low-velocity impact responses on [Ti/0/90] _s hybrid titanium composite laminates (HTCLs) is presented. Different energy levels from 10 to 40 J are investigated using a drop-weight instrument and post-impact inspection. An explicit finite element implementation provides a detailed analysis of impact response in composite and titanium layers, respectively. It accounts for interfacial debonding, progressive failure in composite plies and elastic–plastic deformation in titanium. The main failure modes are experimentally and numerically found to be debonding between titanium and composite, matrix cracking and interlaminar delamination. The principal energy-absorbing mechanism is plastic dissipation of the two titanium sheets. The low cost numerical model is able to effectively predict the overall impact response and major failure modes with good accuracy.

Despite gradual progress over the past decades, the simulation of progressive damage in composite laminates remains a challenging task, in part due to inherent uncertainties of material properties. This paper combines three computational methods - finite element analysis (FEA), machine learning and Markov Chain Monte Carlo - to estimate the probability density of FEA input parameters while accounting for the variation of mechanical properties. First, 15,000 FEA simulations of open-hole tension tests are carried out with randomly varying input parameters by applying continuum damage mechanics material models. This synthetically-generated data is then used to train and validate a neural network consisting of five hidden layers and 32 nodes per layer to develop a highly efficient surrogate model. With this surrogate model and the incorporation of statistical test data from experiments, the application of Markov Chain Monte Carlo algorithms enables Bayesian parameter estimation to learn the probability density of input parameters for the simulation of progressive damage evolution in fibre reinforced composites. This methodology is validated against various open-hole tension test geometries enabling the determination of virtual design allowables.