<p>In the domain of casting process optimization, addressing shrinkage porosity and air entrainment defects in vertical centrifugal casting is critical because these defects significantly impact the mechanical properties, strength, and quality of cast components. Existing approaches, such as predictive modeling, process simulation, casting design optimization, and process optimization, face limitations, including inadequate defect quantification, neglect of geometric effects, and insufficient integration with advanced statistical methods. This study presents a novel methodology combining predictive modeling with finite element analysis and advanced statistical tools like response surface methodology, Taguchi methods, and regression analysis. The casting process is evaluated at five levels for three parameters: aspect ratio, pouring temperature, and mold revolution, with simulation results identifying the location and quantity of defects. These results are utilized to develop a predictive model for quantifying both shrinkage porosity and air entrainment defects. Optimal parameters are determined, including an aspect ratio of 1, with mold rotation speeds of 150 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12008_2025_2257_Article_IEq1.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="30" /> </InlineMediaObject> <EquationSource Format="TEX">$\text{rpm}$</EquationSource> <EquationSource Format="MATHML"><math> <mtext>rpm</mtext> </math></EquationSource> </InlineEquation> and casting temperatures of 800 °C for air entrainment, and 75–100 <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12008_2025_2257_Article_IEq1.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="30" /> </InlineMediaObject> <EquationSource Format="TEX">$\text{rpm}$</EquationSource> <EquationSource Format="MATHML"><math> <mtext>rpm</mtext> </math></EquationSource> </InlineEquation> and 775–800 °C for shrinkage porosity. The proposed methodology enables quantitative defect prediction, enhances process reliability, and optimizes casting parameters effectively, addressing the gaps in current approaches. Future work will focus on incorporating additional variables, extending the methodology to other alloys, and further enhancing its industrial scalability.</p>

FEA-assisted minimization of shrinkage porosity and air entrainment defects in A356 vertical centrifugal castings using a statistical predictive model

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发表日期：

2025年3月4日

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Kamar Mazloum,
Ameen Al Njjar,
Amit Sata

摘要

In the domain of casting process optimization, addressing shrinkage porosity and air entrainment defects in vertical centrifugal casting is critical because these defects significantly impact the mechanical properties, strength, and quality of cast components. Existing approaches, such as predictive modeling, process simulation, casting design optimization, and process optimization, face limitations, including inadequate defect quantification, neglect of geometric effects, and insufficient integration with advanced statistical methods. This study presents a novel methodology combining predictive modeling with finite element analysis and advanced statistical tools like response surface methodology, Taguchi methods, and regression analysis. The casting process is evaluated at five levels for three parameters: aspect ratio, pouring temperature, and mold revolution, with simulation results identifying the location and quantity of defects. These results are utilized to develop a predictive model for quantifying both shrinkage porosity and air entrainment defects. Optimal parameters are determined, including an aspect ratio of 1, with mold rotation speeds of 150 $\text{rpm}$ $rpm$ and casting temperatures of 800 °C for air entrainment, and 75–100 $\text{rpm}$ $rpm$ and 775–800 °C for shrinkage porosity. The proposed methodology enables quantitative defect prediction, enhances process reliability, and optimizes casting parameters effectively, addressing the gaps in current approaches. Future work will focus on incorporating additional variables, extending the methodology to other alloys, and further enhancing its industrial scalability.