Application of a neural network for estimating the crack formation and propagation in sol-gel CeO₂ coatings during processing at temperature

In this study the application of a neural network to estimate the crack propagation and crack size in CeO₂ coatings on a Ni substrate during processing at temperature was evaluated as a function of the Ce content in solutions with increasing processing temperatures from 24 ^oC and 700 ^oC. In this respect, CeO₂ coatings were prepared on Ni tapes from solutions derived from Ce-based precursors using a sol-gel method for YBCO-coated conductors. The crack size of the coating was determined using an in-situ Hot-Stage ESEM depending on the temperature at a certain time in vacuum conditions. It was determined that the crack size of the coating increased with the increasing processing temperature. Measuring the crack sizes of the coatings using Hot-Stage ESEM is an expensive and time-consuming process. In order to eliminate these kinds of problems a neural-network approach was used to estimate the crack sizes of the coatings at different temperatures. The neural network was constructed directly from the experimental results. It was concluded that the estimation of the crack propagation of CeO₂ coatings on a Ni tape substrate are reasonable for the processing temperatures.