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Macro Porosity Formation: A Study in High Pressure Die Casting

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Abstract

Porosity formation in high pressure die casting (HPDC) impacts mechanical properties and casting quality. Much is published regarding micro porosity and its impact on mechanical properties, but there is limited research on the actual formation of macro porosity. In production applications, macro porosity plays a critically important role in casting quality and acceptance by the customer. This paper argues that the most useful definition of macro porosity is the limits of visual detectability. With this definition, it will be shown macro porosity presents stochastically within a controlled HPDC process. This means macro porosity has a random probability distribution or pattern that should be analyzed statistically and cannot be predicted precisely. The general region where macro porosity forms is predictable with simulation, but its actual size and distribution of the voids are random. These results challenge the industry accepted practices for inspections and process controls. This also underscores the importance of up-front design for manufacturability to avoid macro porosity-related quality issues.

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References

  1. J. Brevick, Die Casting Porosity Guidebook. North American Die Casting Association, 2008.

  2. W.G. Walkington, Die Casting Defects: Causes and Solutions (North American Die Casting Association, Rosemont, IL, 1997).

    Google Scholar 

  3. D. Twarog, “State of the Industry 2012.” North American Die Casting Association, 2012, [Online]. https://www.diecasting.org/archive/dce/212online2.pdf.

  4. J. Folk, “U.S. Aluminum Casting Industry - 2019,” Die Casting Engineer, vol. July 2019, 2019.

  5. S. Midson, “Report on the 2014 Die Casting Benchmarking Survey Part 2 of 3: Operations,” in Report on the 2014 Die Casting Benchmarking Survey, North American Die Casting Association, 2014.

  6. S. Viswanathan et al., Eds., “Shrinkage Porosity and Gas Porosity,” In: Casting, ASM International, 2008, pp. 370–374. https://doi.org/10.31399/asm.hb.v15.a0005222

  7. P.D. Lee, A. Chirazi, D. See, Modeling microporosity in aluminum–silicon alloys: a review. J. Light Met. 1(1), 15–30 (2001). https://doi.org/10.1016/S1471-5317(00)00003-1

    Article  Google Scholar 

  8. J. Campbell, Castings, 2nd edn. (Butterworth-Heinemann, Oxford, 2003).

    Google Scholar 

  9. E. Fiorese, F. Bonollo, G. Timelli, L. Arnberg, E. Gariboldi, New classification of defects and imperfections for aluminum alloy castings. Int. J. Met. 9(1), 55–66 (2015). https://doi.org/10.1007/BF03355602

    Article  Google Scholar 

  10. H.H. Doehler, Die Casting (McGraw-Hill Book Company, New York, 1951).

    Google Scholar 

  11. NADCA Product Specification Standards for Die Casting, 10th Edition. Arlington Heights, IL: North American Die Casting Association, 2018.

  12. R. Atwood, “A Combined Cellular Automata and Diffusion Model for the Prediction of Porosity Formation During Solidification,” University of London, 2001.

  13. Product Design for Die Casting E-606, Sixth Edition., vol. E-606. North American Die Casting Association, 2009.

  14. I. Brill, B. Kappes, and S. Midson, An Initial Evaluation of CT Scanning for Measuring and Characterizing Porosity in Aluminum Die Castings, Indianapolis, IN, 2018, vol. T18-083. http://www.diecasting.org/archive/transactions/T18-083.pdf

  15. M. Weidt, R.A. Hardin, C. Garb, J. Rosc, R. Brunner, C. Beckermann, Prediction of porosity characteristics of aluminium castings based on X-ray CT measurements. Int. J. Cast Met. Res. (2018). https://doi.org/10.1080/13640461.2018.1467105

    Article  Google Scholar 

  16. C. Gu, Y. Lu, A.A. Luo, Three-dimensional visualization and quantification of microporosity in aluminum castings by X-ray micro-computed tomography. J. Mater. Sci. Technol. 65, 99–107 (2021). https://doi.org/10.1016/j.jmst.2020.03.088

    Article  Google Scholar 

  17. H. Cao, M. Hao, C. Shen, P. Liang, The influence of different vacuum degree on the porosity and mechanical properties of aluminum die casting. Vacuum 146, 278–281 (2017). https://doi.org/10.1016/j.vacuum.2017.09.048

    Article  CAS  Google Scholar 

  18. X.P. Niu, B.H. Hu, I. Pinwill, H. Li, Vacuum assisted high pressure die casting of aluminium alloys. J. Mater. Process. Technol. 105(1–2), 119–127 (2000). https://doi.org/10.1016/S0924-0136(00)00545-8

    Article  Google Scholar 

  19. Y. Zhang, E. Lordan, K. Dou, S. Wang, Z. Fan, Influence of porosity characteristics on the variability in mechanical properties of high pressure die casting (HPDC) AlSi7MgMn alloys. J. Manuf. Process. 56, 500–509 (2020). https://doi.org/10.1016/j.jmapro.2020.04.071

    Article  Google Scholar 

  20. J. A. Dantzig and M. Rappaz, Solidification, 1 st. EPFL Press, 2009

  21. J. Huang, J.G. Conley, Modeling of microporosity evolution during solidification processes, in Review of progress in quantitative nondestructive evaluation. ed. by D.O. Thompson, D.E. Chimenti, (Springer, US, 1998), pp. 1839–1846

    Chapter  Google Scholar 

  22. T. Liang, C. Mobley, N. Tsumagari, “The Effects of Shot Delay Time on the Microstructures and Mechanical Properties of a Die Cast Aluminum Alloy”, Presented at the Die Casting Toward The Future (Rosemont, IL, 2002). https://www.diecasting.org/archive/transactions/T02-053.pdf

    Google Scholar 

  23. B. Zhang, S.L. Cockcroft, D.M. Maijer, J.D. Zhu, A.B. Phillion, Casting defects in low-pressure die-cast aluminum alloy wheels. JOM 57(11), 36–43 (2005). https://doi.org/10.1007/s11837-005-0025-1

    Article  CAS  Google Scholar 

  24. K.D. Carlson, C. Beckermann, Prediction of shrinkage pore volume fraction using a dimensionless Niyama criterion. Metall. Mater. Trans. A 40(1), 163–175 (2009). https://doi.org/10.1007/s11661-008-9715-y

    Article  CAS  Google Scholar 

  25. G.K. Sigworth, Shrinkage, feeding and riser design. AFS Trans. 14(002), 25–36 (2014)

    Google Scholar 

  26. M. Shabani, A. Mazahery, Prediction of mechanical properties of cast A356 alloy as a function of microstructure and cooling rate. Arch. Metall. Mater. (2011). https://doi.org/10.2478/v10172-011-0073-1

    Article  Google Scholar 

  27. M. Easton, C. Davidson, D. St John, Effect of alloy composition on the dendrite arm spacing of multicomponent aluminum alloys. Metall. Mater. Trans. A 41(6), 1528–1538 (2010). https://doi.org/10.1007/s11661-010-0183-9

    Article  CAS  Google Scholar 

  28. J. Cho, C. Kim, The relationship between dendrite arm spacing and cooling rate of Al-Si casting alloys in high pressure die casting. Int. Metalcasting 8(1), 49–55 (2014). https://doi.org/10.1007/BF03355571

    Article  Google Scholar 

  29. “SRE MAX,” Bosello High Technology , a ZEISS company. https://bosello.eu/products/sre-max/. Accessed 29 Dec 2020

  30. “Phoenix Vtomex C | 3D CT Scanner (Mini Focus),” Waygate Technologies. https://www.bakerhughesds.com/industrial-x-ray-ct-scanners/phoenix-vtomex-c-ct. Accessed 29 Dec 2020

  31. “Xradia 610 & 620 Versa.” https://www.zeiss.com/microscopy/us/products/x-ray-microscopy/zeiss-xradia-610-and-620-versa.html. Accessed 29 Dec 2020

  32. T.J. Schorn, Improving the Effectiveness of Visual Inspection (American Foundry Society, Schaumburg, IL USA, 2018).

    Google Scholar 

  33. J.F. Koretz, G.H. Handelman, How the human eye focuses. Sci. Am. 259(1), 92–99 (1988). https://doi.org/10.1038/scientificamerican0788-92

    Article  CAS  Google Scholar 

  34. J. Schindelin et al., Fiji: an open-source platform for biological-image analysis. Nat. Methods 9(7), 676–682 (2012). https://doi.org/10.1038/nmeth.2019

    Article  CAS  Google Scholar 

  35. S. Preibisch, S. Saalfeld, J. Schindelin, P. Tomancak, Software for bead-based registration of selective plane illumination microscopy data. Nat. Methods 7(6), 418–419 (2010). https://doi.org/10.1038/nmeth0610-418

    Article  CAS  Google Scholar 

  36. ASTM E505–15, Standard reference radiographs for inspection of aluminum and magnesium die castings E505–15. ASTM International (2015). https://doi.org/10.1520/E0505-15

    Article  Google Scholar 

  37. S.S. Shapiro, M.B. Wilk, An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965)

    Article  Google Scholar 

  38. F. Wilcoxon, Individual comparisons by ranking methods. Biom. Bull. 1(6), 80–83 (1945)

    Article  Google Scholar 

  39. MAGMAsoft. Kackerstrasse 11, 52072 Aachen, Germany: MAGMA Gmbh, 2019

  40. M.R. Brand, An examination of certain Bayesian methods used in reliability analysis. Reliab. Eng. 1(2), 115–125 (1980). https://doi.org/10.1016/0143-8174(80)90003-7

    Article  Google Scholar 

  41. V.D. Tsoukalas, Optimization of porosity formation in AlSi9Cu3 pressure die castings using genetic algorithm analysis. Mater. Des. 29(10), 2027–2033 (2008). https://doi.org/10.1016/j.matdes.2008.04.016

    Article  CAS  Google Scholar 

  42. S.G. Lee, A.M. Gokhale, Formation of gas induced shrinkage porosity in Mg-alloy high-pressure die-castings. Scr. Mater. 55(4), 387–390 (2006). https://doi.org/10.1016/j.scriptamat.2006.04.040

    Article  CAS  Google Scholar 

  43. Q.-C. Hsu, A.T. Do, Minimum porosity formation in pressure die casting by taguchi method. Math. Probl. Eng. 2013, 1–9 (2013). https://doi.org/10.1155/2013/920865

    Article  Google Scholar 

  44. J. Zheng, Q. Wang, P. Zhao, C. Wu, Optimization of high-pressure die-casting process parameters using artificial neural network. Int. J. Adv. Manuf. Technol. 44(7–8), 667–674 (2009). https://doi.org/10.1007/s00170-008-1886-6

    Article  Google Scholar 

  45. F. Bonollo, N. Gramegna, G. Timelli, High-pressure die-casting: contradictions and challenges. JOM 67(5), 901–908 (2015). https://doi.org/10.1007/s11837-015-1333-8

    Article  Google Scholar 

  46. D. Blondheim Jr., “Unsupervised Machine Learning and Statistical Anomaly Detection Applied to Thermal Images”, Presented at the 2018 NADCA Congress and Exposition (Indianapolis, IN, 2018). http://www.diecasting.org/archive/transactions/T18-071.pdf

    Google Scholar 

  47. C.H. Cáceres, B.I. Selling, Casting defects and the tensile properties of an AlSiMg alloy. Mater. Sci. Eng. A 220(1–2), 109–116 (1996). https://doi.org/10.1016/S0921-5093(96)10433-0

    Article  Google Scholar 

  48. R. Lumley, N. Deeva, M. Gershenzon, An evaluation of quality parameters for high pressure die castings. Int. J. Met. 5(3), 37–56 (2011). https://doi.org/10.1007/BF03355517

    Article  CAS  Google Scholar 

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Funding

Research was sponsored by Mercury Marine – Mercury Castings, a division of Brunswick, Inc.

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Authors and Affiliations

  1. Mercury Marine – Mercury Castings, A Division of Brunswick, Inc., Fond du Lac, WI, USA

    David Blondheim Jr. & Alex Monroe

  2. Colorado State University, Fort Collins, CO, USA

    David Blondheim Jr.

  3. Michigan Technological University, Houghton, MI, USA

    Alex Monroe

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  1. David Blondheim Jr.
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Correspondence to David Blondheim Jr..

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Blondheim, D., Monroe, A. Macro Porosity Formation: A Study in High Pressure Die Casting. Inter Metalcast 16, 330–341 (2022). https://doi.org/10.1007/s40962-021-00602-x

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