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<p>Porosity remains a critical challenge in the manufacturing of high-pressure die cast components, compromising both their structural integrity and mechanical performance. This study proposes an integrated methodology combining numerical simulation, experimental optimization, and ultrasonic testing validation to minimize porosity in a tractor oil filter bracket made of AlSi12 aluminum alloy. A finite element method (FEM) simulation was employed to identify critical regions susceptible to porosity, which were experimentally validated through ultrasonic inspection. An analysis of variance (ANOVA) was conducted to develop a second-order polynomial regression model linking porosity to key high-pressure die casting parameters: melt temperature, mold temperature, and injection pressure. Subsequently, optimization algorithms (COBYLA, SLSQP, BFGS) were implemented to determine the optimal processing conditions, effectively reducing porosity while preserving the mechanical and physical properties of the component. This systematic approach provides promising perspectives for industrial casting processes.</p>

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Optimization of high-pressure die-casting parameters to reduce porosity in AlSi12 alloy parts: an experimental and numerical approach

  • 期刊论文

  • 发表日期：

    2025年11月30日

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  • Traiaia Noura,
  • Lemmoui Abdennacer,
  • Mimoune Derrez,
  • Tlili Samira

摘要

Porosity remains a critical challenge in the manufacturing of high-pressure die cast components, compromising both their structural integrity and mechanical performance. This study proposes an integrated methodology combining numerical simulation, experimental optimization, and ultrasonic testing validation to minimize porosity in a tractor oil filter bracket made of AlSi12 aluminum alloy. A finite element method (FEM) simulation was employed to identify critical regions susceptible to porosity, which were experimentally validated through ultrasonic inspection. An analysis of variance (ANOVA) was conducted to develop a second-order polynomial regression model linking porosity to key high-pressure die casting parameters: melt temperature, mold temperature, and injection pressure. Subsequently, optimization algorithms (COBYLA, SLSQP, BFGS) were implemented to determine the optimal processing conditions, effectively reducing porosity while preserving the mechanical and physical properties of the component. This systematic approach provides promising perspectives for industrial casting processes.