The Smooth Bootstrap Approach to the Distribution of a Shape in the Ferritic Stainless Steel AISI 434L Powders

Abstract:

The ferritic-austenitic stainless steel was obtained by sintering the mixture of ferritic stainless steel AISI 434L powders with different amount of additions: Mn, Ni and Si. The structure of obtained sintered samples was investigated by computer image analysis methods. In porous materials the character of the pore structure strongly effects on its mechanical properties. Accurate information about pores shape is important information for technological process and quality control of produced materials as well. The images of the porous microstructure were analyzed using ADCIS Aphelion analytical software. Analyzed pores have complex and irregular shapes and thus authors decided to appoint different shape factors to obtain its proper quantitative description. Data obtained from image analysis process were statistically analyzed. Authors used a special resampling approach known as smooth bootstrapping to smoothing cumulative empirical distributions. The results obtained during resampling procedure have been compared with raw data from verification set and guidelines for the application of the proposed approach have been formulated. The validity of the proposed approach was positively verified and it significantly improved quality of the results. The smoothing and imputing of data allow to avoid numerical artifacts that may arise during the classical statistical calculations on irregular data originated from image analysis obtained from sintered samples. Efficient, reliable and relatively fast method for accessing the distribution of any others quantitative parameters describing microstructure of the materials is very interesting proposal for wide spectrum of application.

• Periodical: Solid State Phenomena (Volume 197)
• Pages: 162-167
• DOI: https://doi.org/10.4028/www.scientific.net/SSP.197.162
• Online since: February 2013
• Authors: Jacek Pietraszek, Aneta Gądek-Moszczak
• Keywords: Image Analysis, Material Science, Probabilistic Distribution, Shape Factors, Smooth Bootstrap, Statistical Analysis, Steel Powders

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