January AMP_Digital

A D V A N C E D M A T E R I A L S & P R O C E S S E S | J A N U A R Y 2 0 1 9 2 1 subjective nature of micrograph analy- sis. Additionally, the standard does not provide guidelines for whether graph- ite on the boundary of the micrograph should be included in the nodularity classification. Nevertheless, the com- puter algorithm can automate the mi- crograph analysis while eliminating all random human error. The system- atic errors present in automated anal- ysis can be corrected for and further reduced by calibrating the algorithm within a specific range. For example, because ductile iron has a characteris- tic nodularity of 80-100%, an automat- ed solution can be further calibrated in this range, and then only applied to ductile irons going forward. As the industry moves toward au- tomation, there is a demand to mod- ernize outdated standards to include guidelines and ground-truth datasets that would allow engineers to work more confidently in the field of mate- rial development and characterization. ~AM&P For more information: John Sosa is CEO of Mipar Software, 5701 N. High St., Suite 204, Worthington, OH, 43085, 614.407.4510, support@mipar.us, www. mipar.us. References 1. M.N. Alam and M. Blackman, High- Angle Kikuchi Patterns, Proc. Royal Soc. of London A: Mathematical, Physical and Engineering Sciences , 221 (1145), p 224-242, 1954. 2. D.J. Dingley and V. Randle, Micro- textureDeterminationby ElectronBack- scatter Diffraction, J. Matls. Sci., 27 (17), p 4545-4566, 1992. 3. F.J. Humphreys, Characterization of Fine-Scale Microstructures by Electron Backscatter Diffraction (EBSD), Scripta Mater., 51 (8), p 771-776, 2004. 4. Standard Test Method for Sieve Analysis of Fine and Coarse Aggregates, ASTM C136/C136M-14, ASTM, 2014. 5. Standard Specification for Woven Wire Test Sieve Cloth and Test Sieves, ASTM E11-17, ASTM, 2017. 6. E. Nsugbe, et al., Monitoring the Particle Size Distribution of a Powder Mixing Process with Acoustic Emissions: A Review, Eng. Technol. Ref., p 1-12, 2016. 7. Standard Guide for Measurement of Particle Size Distribution of Nano- materials in Suspension by Photon Correlation Spectroscopy (PCS), ASTM E2490-09, ASTM, 2015. 8. R. Pecora, Dynamic Light Scattering Measurement of Nanometer Particles in Liquids, J. Nanopart. Res., 2, p 123-131, 2000. 9. D.F. Heaney andC.D. Green, Molding of Components in Metal Injection Mold- ing(MIM),inD.F.Heaney(Ed.), Handbook of Metal Injection Molding, Woodhead Publishing Ltd., Cambridge, UK, 2012. 10. J.M. Benson, et al., The Need for Powder Characterization in the Additive Manufacturing Industry and the Estab- lishment of a National Facility, S. Afr. J. Ind. Eng., 26, p 104-114, 2015. 11. Standard Test Method for Evalua- ting the Microstructure of Graphite in Iron Castings, ASTM A247-17, ASTM, 2017. Fig. 5 — Area fraction algorithm error analysis results showing ambiguity in ASTM standard reference micrographs for estimating graphite nodularity in ductile iron (see Table 4): (a) 20% nodularity; (b) 30% nodularity; and (c) 40% nodularity (blue = non-nodular graphite and green = nodular graphite). (a) (b) (c)