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Frontiers of Mechanical Engineering

Front. Mech. Eng.    2016, Vol. 11 Issue (3) : 250-257     https://doi.org/10.1007/s11465-016-0401-2
RESEARCH ARTICLE |
Response surface regression analysis on FeCrBSi particle in-flight properties by plasma spray
Runbo MA,Lihong DONG(),Haidou WANG(),Shuying CHEN,Zhiguo XING
National Key Laboratory for Remanufacturing, Academy of Armored Forces Engineering, Beijing 100072, China
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Abstract

This work discusses the interactive effects between every two of argon flow rate, voltage, and spray distance on in-flight particles by plasma spray and constructs models that can be used in predicting and analyzing average velocity and temperature. Results of the response surface methodology show that the interactive effects between voltage and spray distance on particle in-flight properties are significant. For a given argon flow rate, particle velocity and temperature response surface are obviously bending, and a saddle point exists. With an increase in spray distance, the interactive effects between voltage and argon flow rate on particle in-flight properties appear gradually and then weaken. With an increase in voltage, the interactive effects between spray distance and argon flow rate on particle in-flight properties change from appearing to strengthening and then to weakening.

Keywords particle velocity      particle temperature      interactive effects      response surface     
Corresponding Authors: Lihong DONG,Haidou WANG   
Online First Date: 19 August 2016    Issue Date: 31 August 2016
 Cite this article:   
Runbo MA,Lihong DONG,Haidou WANG, et al. Response surface regression analysis on FeCrBSi particle in-flight properties by plasma spray[J]. Front. Mech. Eng., 2016, 11(3): 250-257.
 URL:  
http://journal.hep.com.cn/fme/EN/10.1007/s11465-016-0401-2
http://journal.hep.com.cn/fme/EN/Y2016/V11/I3/250
Fig.1  Morphology of FeCrBSi powder
Parameter Value
Current, A 380
Powder feed rate, g/min 50
Carrier gas flow, L/min 13
Auxiliary gas flow, L/min 100, 110, 120
Voltage, V 150, 160, 165
Spray distance, mm 90, 100, 110, 120, 130
Tab.1  Summary of the operating spray parameters
No. Argon flow rate/(L·min−1) Voltage/V Spray distance/mm Particle average velocity/(m·s−1) Particle average temperature/°C
1 110 150 90 325 2400
2 120 150 90 310 2350
3 110 165 90 330 3000
4 120 165 90 325 2850
5 110 165 100 315 2550
6 120 165 100 315 2380
7 120 165 110 310 2380
8 110 160 110 318 2500
9 100 160 120 330 2700
10 100 160 120 330 2550
11 110 160 120 346 2500
12 100 160 130 335 2480
13 110 160 130 340 2500
14 120 160 130 340 2450
Tab.2  Particle average velocity and temperature under different levels of the three factors
Variance source Sum of squares F test value Significance
Model (2) 1379.52 4.47 0.0351
A 15.85 0.31 0.5959
B 541.92 10.55 0.0141
C 820.32 15.96 0.0052
AB 48.35 0.94 0.3644
AC 62.29 1.21 0.3073
BC 559.17 10.88 0.0131
Tab.3  Variance test of particle average velocity of response surface model
Fig.2  Response surface of interactive effects between voltage and spray distance on the particle average velocity (argon flow rate is at 110 L/min)
Fig.3  Response surface of interactive effects between argon flow rate and voltage on the particle average velocity. (a) Spray distance is at 60 mm; (b) spray distance is at 90mm; (c) spray distance is at 140 mm
Fig.4  Response surface of interactive effects between spray distance and argon flow rate on particle velocity. (a) Voltage is at 150 V; (b) voltage is at 160 V; (c) voltage is at 175 V
Variance source Sum of squares F test value Significance
Model (3) 375400.00 5.38 0.0219
A 11577.10 1.00 0.3516
B 50506.57 4.34 0.0756
C 11615.96 1.00 0.3508
AB 1259.42 0.11 0.7517
AC 3760.81 0.32 0.5873
BC 137500.00 11.83 0.0109
Tab.4  Variance test of particle average temperature of response surface model
Fig.5  Response surface of interactive effects between voltage and spray distance on the particle average temperature (argon flow rate is at 110 L/min)
Fig.6  Response surface of interactive effects between argon flow rate and voltage on the particle average temperature. (a) Spray distance is at 60 mm; (b) spray distance is at 100 mm; (c) spray distance is at 140 mm
Fig.7  Response surface of interactive effects between spray distance and argon flow rate on particle temperature. (a) Voltage is at 100 V; (b) voltage is at 160 V; (c) voltage is at 170 V
Fig.8  Normal distribution probability plot of residual
Fig.9  Residual plot. (a) Residual plot of particle velocity; (b) residual plot of particle temperature
1 Wang H, Xu B, Jiang Y, . Microstructure and mechanical properties of supersonic plasma sprayed coating. Transactions of the China Welding Institution, 2011, 32(9): 1–5 (in Chinese)
2 Cizek J, Khor K A. Role of in-flight temperature and velocity of powder particles on plasma sprayed hydroxyapatite coating characteristics. Surface and Coatings Technology, 2012, 206(8–9): 2181–2191
https://doi.org/10.1016/j.surfcoat.2011.09.058
3 Zhang C, Li C, Liao H, . Effect of in-flight particles velocity on the performance of plasma-sprayed YSZ electrolyte coating for solid oxide fuel cells. Surface and Coatings Technology, 2008, 202(12): 2654–2660
https://doi.org/10.1016/j.surfcoat.2007.09.037
4 Wand S, Li G, Wang H, . Influence of microdefect on rolling contact fatigue performance of thermal spraying coating. Journal of Materials Engineering, 2012, 40(2): 72–76 (in Chinese)
5 Piao Z, Xu B, Wang H, . Investigation of rolling contact fatigue lives of Fe-Cr alloy coatings under different loading conditions. Surface and Coatings Technology, 2010, 204(9–10): 1405–1411
https://doi.org/10.1016/j.surfcoat.2009.09.035
6 Zhang X, Xu B, Xuan F, . Microstructural and porosity variations in the plasma-sprayed Ni-alloy coatings prepared at different spraying powers. Journal of Alloys and Compounds, 2009, 473(1–2): 145–151
https://doi.org/10.1016/j.jallcom.2008.05.107
7 Zhang S, Li C, Li C, . Scandia-stabilized zirconia electrolyte with improved interlamellar bonding by high-velocity plasma spraying for high performance solid oxide fuel cells. Journal of Power Sources, 2013, 232: 123–131
https://doi.org/10.1016/j.jpowsour.2012.12.092
8 Cizek J, Khor K A, Prochazka Z. Influence of spraying conditions on thermal and velocity properties of plasma sprayed hydroxyapatite. Materials Science and Engineering C, 2007, 27(2): 340–344
https://doi.org/10.1016/j.msec.2006.05.002
9 Kang J. Research on Competing Failure Behavior and Life Prediction of Plasma Spraying Coating. Beijing: China University of Geosciences, 2013 (in Chinese)
10 Bai Y, Liu K, Wen Z, . The influence of particles in-flight properties on the microstructure of coatings deposited by the supersonic atmospheric plasma spraying. Ceramics International, 2013, 39(7): 8549–8553
https://doi.org/10.1016/j.ceramint.2013.03.091
11 Liu K, Tang J, Bai Y, . Particles in-flight behavior and its influence on the microstructure and mechanical property of plasma sprayed La2Ce2O7 thermal barrier coatings. Materials Science and Engineering: A, 2015, 625: 177–185
https://doi.org/10.1016/j.msea.2014.11.098
12 .. Anderson-Cook C M, Borror C M, Montgomery D C. Response surface design evaluation and comparison. Journal of Statistical Planning and Inference, 2009, 139(2): 629–641
https://doi.org/10.1016/j.jspi.2008.04.004
13 Xu Y, Li M, Zhao X, . The response curved surface regression analysis technique the application of a new regression analysis technique in materials research. Rare Metal Materials and Engineering, 2001, 30(6): 428–432 (in Chinese)
14 Grubbs F E. Sample criteria for testing outlying observations. Annals of Mathematical Statistics, 1950, 21(1): 27–58
https://doi.org/10.1214/aoms/1177729885
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