Macro Porosity Formation: A Study in High Pressure Die Casting

David Blondheim Jr. & Alex Monroe


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.

고압 다이캐스팅(HPDC)의 다공성 형성은 기계적 특성과 주조 품질에 영향을 미칩니다. 미세 기공과 기계적 특성에 미치는 영향에 대해 많이 발표되었지만 매크로 기공의 실제 형성에 대한 연구는 제한적입니다. 생산 응용 분야에서 매크로 다공성은 주조 품질과 고객의 수용에 매우 중요한 역할을 합니다. 이 논문은 매크로 다공성의 가장 유용한 정의가 시각적 탐지 가능성의 한계라고 주장합니다. 이 정의를 통해 매크로 다공성이 제어된 HPDC 프로세스 내에서 확률적으로 나타납니다. 이것은 매크로 다공성이 통계적으로 분석되어야 하고 정확하게 예측할 수 없는 무작위 확률 분포 또는 패턴을 가지고 있음을 의미합니다. 매크로 다공성 형성이 시뮬레이션으로 예측 가능한 일반적인 영역, 그러나 공극의 실제 크기와 분포는 무작위입니다. 이러한 결과는 검사 및 공정 제어에 대해 업계에서 인정하는 관행에 도전합니다. 이것은 또한 매크로 다공성 관련 품질 문제를 피하기 위해 제조 가능성에 대한 선행 설계의 중요성을 강조합니다.


  • high pressure die casting
  • porosity
  • casting defects
  • defect classification
  • macro porosity
  • micro porosity


  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].
  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.
  7. P.D. Lee, A. Chirazi, D. See, Modeling microporosity in aluminum–silicon alloys: a review. J. Light Met. 1(1), 15–30 (2001). 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). 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.
  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). 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). 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). Article 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). 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). 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. 1839–1846Chapter 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). 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). Article 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). Article 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). 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). Article 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). Google Scholar 
  29. “SRE MAX,” Bosello High Technology , a ZEISS company Accessed 29 Dec 2020
  30. “Phoenix Vtomex C | 3D CT Scanner (Mini Focus),” Waygate Technologies Accessed 29 Dec 2020
  31. “Xradia 610 & 620 Versa.” 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). Article Google Scholar 
  34. J. Schindelin et al., Fiji: an open-source platform for biological-image analysis. Nat. Methods 9(7), 676–682 (2012). Article 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). Article Google Scholar 
  36. ASTM E505–15, Standard reference radiographs for inspection of aluminum and magnesium die castings E505–15. ASTM International (2015). 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). 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). Article 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). Article 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). 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). Google Scholar 
  45. F. Bonollo, N. Gramegna, G. Timelli, High-pressure die-casting: contradictions and challenges. JOM 67(5), 901–908 (2015). 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). 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). 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).

Related posts