Materials science: The ultrasmoothness of diamond-like carbon surfaces

The ultrasmoothness of diamond-like carbon coatings is explained by an atomistic/continuum multiscale model. At the atomic scale, carbon ion impacts induce downhill currents in the top layer of a growing film. At the continuum scale, these currents cause a rapid smoothing of initially rough substrates by erosion of hills into neighboring hollows. The predicted surface evolution is in excellent agreement with atomic force microscopy measurements. This mechanism is general, as shown by similar simulations for amorphous silicon. It explains the recently reported smoothing of multilayers and amorphous transition metal oxide films and underlines the general importance of impact-induced downhill currents for ion deposition, polishing, and nanopattering.

An example of the use of neural computing techniques in materials science - The modelling of fatigue thresholds in Ni-base superalloys

Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network's complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules. © 1999 Elsevier Science S.A.