An extreme learning machine algorithm to predict the in-flight particle characteristics of an atmospheric plasma spray process

A robust single hidden layer feed forward neural network (SLFN) is used in this study to model the in-flight particle characteristics of the atmospheric plasma spray (APS) process with regard to the input processing parameters. The in-flight particle characteristics influence the structure and properties of the APS coating and, thus, are considered important parameters to comprehend the manufacturing process. The training times of traditional back propagation algorithms, mostly used to model such processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. Performance comparisons of the networks trained with ELM algorithm and standard error back propagation algorithms are presented. It is found that networks trained with ELM have good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The trends represent robustness of the trained networks and enhance reliability of the application of the artificial neural network in modelling APS processes

Process Parameter Impact on Suspension-HVOF-Sprayed Cr2O3 Coatings

Chromium oxide (Cr2O3) is commonly used as an atmospheric plasma-sprayed (APS) coating from powder feedstock in applications requiring resistance to sliding wear and corrosion, as well as amenability to texturing, e.g., in anilox rolls. Recently, high-velocity oxy-fuel spray methods involving suspension feedstock have been considered an extremely promising alternative to produce denser and more homogeneous chromium oxide coatings with lower as-sprayed surface roughness, higher hardness and potentially superior wear performance compared to conventional APS-sprayed coatings. In this study, the impact of process parameters namely auxiliary air cleaning nozzles and a transverse air curtain on suspension high-velocity oxy-fuel-sprayed Cr2O3 suspensions is presented. The produced coatings are characterized for their microstructure, mechanical properties and wear resistance by cavitation erosion. The results reveal the importance of optimized air nozzles and air curtain to achieve a vastly improved coating structure and performance.publishedVersionPeer reviewe