Purpose: In plasma spraying, the residual stress is one of the important factors that reduce the strength and shorten the service lifetime of the spray coatings. It is therefore essential to investigate the evolution of the temperature and the distribution of the residual stresses, which are primarily induced by initial temperature difference and thermal expansion coefficient mismatch between the splat and the substrate. Design/methodology/approach: As the plasma spraying process involves the solidification and cooling of extremely tiny molten metal droplets in a very short time, it is very difficult to observe the procedure directly. In this paper, a finite element model involving the temperature and residual stress simulation of a single NiCoCrAlY particle splat in plasma spraying when cooled on the carbon steel substrate is presented. Findings: The numerical analysis results show that the temperature rise is more evident within the interior than on the top surface of the substrate. The maximum residual stresses of about 170 MPa appear at the central part of the splat. Research limitations/implications: Future work should integrate the flattening process with the solidification and cooling of the droplet. Practical implications: It will be helpful to the understanding and control of residual stresses in plasma spraying. Originality/value: This research simulates the evolution temperature and residual stress distribution during the solidification and cooling process on the single splat level in plasma spraying.
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Purpose: In plasma spraying, the coating properties such as porosity, hardness, strength, etc. are directly determined by particle behaviors, i.e. the temperature and velocity. Therefore, it is necessary and meaningful to predict the particle behaviors under a certain combination of process parameters before the spraying process is executed. Design/methodology/approach: In this study, SVM (Support Vector Machines) is applied to the prediction of in-flight particle temperature and velocity in plasma spraying by argon flow rate, hydrogen flow rate and electric current. The influences of the three parameters on particle temperature and velocity are also investigated. Findings: In the leave-one-out cross validation on an orthogonal experiment with 9 sets of parameters, the maximum relative errors of prediction for particle temperature and velocity are 0.68% and 1.42% respectively. The prediction results reveal that the most influential parameter for particle temperature is hydrogen flow rate, and argon flow rate exerts the greatest influences on particle velocity. Research limitations/implications: Future work should focus on the modeling of the whole spraying proces with all the spraying parameters. Practical implications: It will be helpful to the prediction and controll of particle behaviors in plasma spraying. Originality/value: First application of SVM to modeling the in-flight particle behaviors in plasma spraying.
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