Prediction of in-flight particle behaviors in plasma spraying

• Autorzy: Fang J. C. , Zeng H. P. , Xu W. J. , Zhao Z. Y. , Wang L.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

statistic methods; prediction; in-flight particle behaviors; plasma spraying

PL

metody statystyczne; prognozowanie; natryskiwanie plazmowe

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2006
• Tom: Vol. 18, nr 1-2
• Strony: 283--286
• Opis fizyczny: Bibliogr. 15 poz., wykr.

Twórcy

autor

Fang J. C.

• College of Mechanical Engineering and Automation, HQU, Quanzhou 362021 P.R. China

autor

Zeng H. P.

• Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, 116024, P.R. China

autor

Xu W. J.

• Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, 116024, P.R. China

autor

Zhao Z. Y.

• College of Mechanical Engineering and Automation, HQU, Quanzhou 362021 P.R. China

autor

Wang L.

• College of Mechanical Engineering and Automation, HQU, Quanzhou 362021 P.R. China

Bibliografia

• [1] J.C. Fang, W.J. Xu, Plasma spray forming, Journal of Materials Processing Technology 129 (2002) 288-293.
• [2] H. Herman, S. Sampath, Plasma spray forming, Industrial Ceramics (Italy) 18 (1998) 1 29-32.
• [3] A. Geibel, L. Froyen, L. Delaey, Plasma spray forming: an alternate route for manufacturing freestanding components, Journal of Thermal Spray Technology 5 (1996) 4 419-430.
• [4] J.F. Li, H.L. Liao, C.X. Ding, C. Coddet, Optimizing the plasma spray process parameters of yttria stabilized zirconia coatings using a uniform design of experiments, Journal of Materials Processing Technology 160 (2005) 34–42.
• [5] M. Friis, C. Persson, J. Wigren, Influence of particle in-flight characteristics on the microstructure of atmospheric plasma sprayed yttria stabilized ZrO2, Surface and Coatings Technology 141 (2001) 115-127.
• [6] L. Zhao, K. Seemann, A. Fischer, E. Lugscheider, Study on atmospheric plasma spraying of Al2O3 using on-line particle monitoring, Surface and Coatings Technology 168 (2003) 186–190.
• [7] B. Liu, T. Zhang, Y. Bao and D.T. Gawne, Numerical modelling of motion and heating of particles during plasma spraying, Surface Engineering 18 [5] (2002) 350-357.
• [8] S. Guessasma, Z. Salhi, G. Montavon, P. Gougeon, C. Coddet, Modeling of the APS plasma spray process using artificial neural networks: basis, requirements and an example, Computational Materials Science 29 (2004) 315–333.
• [9] S. Guessasma, C. Coddet, Neural computation applied to APS spray process: Porosity analysis, Surface and Coatings Technology 197 (2005) 85–92.
• [10] W.M. Campbell, J.P. Campbell, D.A. Reynolds, E. Singer, P.A. Torres-Carrasquillo, Support vector machines for speaker and language recognition, Computer Speech and Language 20 (2006) 210–229.
• [11] H. Drucker, C.J.C. Burges, L. Kaufman, A. Smola, V. Vapnik, Support vector regression machines, Advances in Neural Information Processing Systems 9 (1997) 155-161,Cambridge, MA, MIT Press.
• [12] P. Mantero, G. Moser, S.B. Serpico, Partially supervised classification of remote sensing images using SVM-based probability density estimation, 2003 IEEE workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 27-28 October 2003, NASA Goddard Visitor Center, Greenbelt MD, USA, 327-336.
• [13] V. Vapnik, The Nature of Statistical Learning Theory. NY: Springer Verlag, 1995.
• [14] V. Vapnik, Statistical learning theory, John Wiley, New-York, 1998.
• [15] J.C. Fang, W.J. Xu and Z.Y. Zhao, In-flight Behaviors of ZrO2 Particle in Plasma Spraying, Surface and Coatings Technology (accepted).