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In this research, a groundbreaking statistical design methodology is introduced for the optimization of geo-polymer concrete mixes suitable for 3D printing. The study delves into the influence of five key parameters, including fine aggregate size and type, binder type (slag or metakaolin) and ratio, and alkaline solution amount and ratio (NaOH:Na2SiO3). By conducting a full factorial analysis, the individual and interactive effects of these parameters on crucial quality attributes are thoroughly examined. To strike a balance between workability and mechanical properties, a multi-objective optimization problem is formulated. Leveraging established regression models, the optimal combination of factors that satisfy this complex balance is identified, taking into account the study's inherent constraints.

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Geopolymer Composites for 3D Printing Applications: A Statistical Analysis Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Passant Youssef,
  • Muhammad S. El-Feky,
  • Ahmed M. Ragab,
  • Mohamed I. Serag

摘要

In this research, a groundbreaking statistical design methodology is introduced for the optimization of geo-polymer concrete mixes suitable for 3D printing. The study delves into the influence of five key parameters, including fine aggregate size and type, binder type (slag or metakaolin) and ratio, and alkaline solution amount and ratio (NaOH:Na2SiO3). By conducting a full factorial analysis, the individual and interactive effects of these parameters on crucial quality attributes are thoroughly examined. To strike a balance between workability and mechanical properties, a multi-objective optimization problem is formulated. Leveraging established regression models, the optimal combination of factors that satisfy this complex balance is identified, taking into account the study's inherent constraints.