Modeling of splat particle splashing data during thermal spraying with the Burr distribution

Document Type : Research Article

Authors

1 Department of Mathematics and Statistics, Lahijan Branch, Islamic Azad University, Lahijan, Iran

2 Department of Mechanical Engineering, Payame Noor University (PNU), Tehran, Iran

Abstract

Splashing of splat particles is one of the most important phenomena in industrial processes such as thermal spray coating. The data relative to the degree of splashing of splats sprayed with a normal angle are commonly characterized by the Weibull distribution function. In this present study, an effort has been made to show that the Burr distribution is better than the Weibull distribution for presenting the distribution of the degree of splashing. For this purpose, the Burr Type XII distribution and Weibull distribution are compared using different criteria. Furthermore, because of the great importance of statistical prediction of censored data in reducing costs and improving quality of the coating process, we consider different predictors of this data based on a progressively censored sample. For computing the prediction values we obtain the maximum likelihood estimates using the Expectation-Maximization (EM) algorithm. An important implication of the present study is that the Burr Type XII distribution more appropriately described the degree of splashing data. Therefore, the Burr Type XII can be used as an alternative distribution that adequately describes the splashing data and thereby predicts the censored data.

Highlights

  • Inference about the splat particle splashing data which sprayed with a normal angle.
  • Perform the number of model selection tests to determine the appropriate probability model under complete and progressive censored sample.
  • Study of different methods for predicting the missing splat particle splashing data.

Keywords

References

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Journal of Particle Science and Technology
Volume 3, Issue 1
March 2017
Pages 41-50

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History

  • Receive Date: 14 February 2017
  • Revise Date: 11 July 2017
  • Accept Date: 02 December 2017

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