Machine Learning Enabled Prediction of Stacking Fault Energies in Concentrated Alloys

Calculated stacking-fault energies of elemental metals.

Stacking Fault Energies of Tetrahedrally Coordinated Crystals

The energies of the intrinsic stacking fault in 20 tetrahedrally coordinated crystals, determined by electron microscopy from the widths of extended dislocations, range from a few mJ/m to 300 mJ/m. The reduced stacking fault energy (RSFE: stacking fault energy per bond perpendicular to the fault plane) has been found to have correlations with the effective charge, the charge redistribution inde...

Simple model of stacking-fault energies.

Stacking - fault energies in simple metals : applications to BCC metals

We present a general method for calculating the stacking-fault energy in simple metals, and then we apply this to the ( 1 1 2 ) faults in body-centred cubic (BCC) metals. Our method contains no approximations for a given wavenumber characteristic (or equivalently the pair potential). Our results show that metastable faults do indeed exist in the simple BCC metals (Li, Na. K, Rb, Cs, Ca. Sr, Ba)...

Optimising Machine-Learning-Based Fault Prediction in Foundry Production

Microshrinkages are known as probably the most difficult defects to avoid in high-precision foundry. The presence of this failure renders the casting invalid, with the subsequent cost increment. Modelling the foundry process as an expert knowledge cloud allows properly-trained machine learning algorithms to foresee the value of a certain variable, in this case the probability that a microshrink...