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With the advancement of Additive Manufacturing (AM) technology, Big Area Additive Manufacturing (BAAM) has been widely applied in automotive, construction, and other engineering fields. Like other Materials Extrusion Additive Manufacturing (MEAM) technologies, the quality of bonding between extruded filaments is crucial to the mechanical properties of the parts. Fibers are added to polymer materials to improve strength and stiffness, introducing challenges like the effects of fiber distribution and orientation on bonding quality. This study presents a bonding strength prediction model for glass fiber-reinforced acrylonitrile-styrene-acrylate (ASA) materials, addressing the entire bonding and BAAM process characteristics. Infrared temperature measurement and quasi-static tensile tests systematically identified temperature-time curves and fracture strength data at various printing cycle. Based on polymer healing theory and incorporating BAAM process characteristics, a suitable bonding model for BAAM has been established. Multiple fitting methods established key parameters of the model, enabling precise predictions of bonding strength under varying printing cycle conditions and analysis of parameter impacts at different bonding stages. Research findings demonstrate that: 1) After printed, the temperature of extruded filaments drops quickly to ambient levels, while new filament printing periodically elevates temperatures. Shorter the printing cycles significantly increase temperature and decelerate cooling. 2) Longer printing cycles reduce bonding temperatures, correspondingly diminishing bonding strength. 3) The BAAM bonding model precisely forecasts bonding strength for various printing cycle, with the model achieving a coefficient of determination (R2) of 0.9822. This study not only validates the effectiveness of the BAAM Bond bonding model but also provides theoretical support for optimizing the BAAM process. This contributes to improving the mechanical performance and application reliability of printed parts, promoting the broader application of additive manufacturing technology in construction and other fields.

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Study and Prediction of Bonding Strength in Big-Area Additive Manufacturing Parts Based on Glass Fiber-Reinforced ASA Material

  • 会议论文

  • 发表日期：

    2025年

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  • Changxin Liu,
  • Shen Yan,
  • Xianzhong Zhao

摘要

With the advancement of Additive Manufacturing (AM) technology, Big Area Additive Manufacturing (BAAM) has been widely applied in automotive, construction, and other engineering fields. Like other Materials Extrusion Additive Manufacturing (MEAM) technologies, the quality of bonding between extruded filaments is crucial to the mechanical properties of the parts. Fibers are added to polymer materials to improve strength and stiffness, introducing challenges like the effects of fiber distribution and orientation on bonding quality. This study presents a bonding strength prediction model for glass fiber-reinforced acrylonitrile-styrene-acrylate (ASA) materials, addressing the entire bonding and BAAM process characteristics. Infrared temperature measurement and quasi-static tensile tests systematically identified temperature-time curves and fracture strength data at various printing cycle. Based on polymer healing theory and incorporating BAAM process characteristics, a suitable bonding model for BAAM has been established. Multiple fitting methods established key parameters of the model, enabling precise predictions of bonding strength under varying printing cycle conditions and analysis of parameter impacts at different bonding stages. Research findings demonstrate that: 1) After printed, the temperature of extruded filaments drops quickly to ambient levels, while new filament printing periodically elevates temperatures. Shorter the printing cycles significantly increase temperature and decelerate cooling. 2) Longer printing cycles reduce bonding temperatures, correspondingly diminishing bonding strength. 3) The BAAM bonding model precisely forecasts bonding strength for various printing cycle, with the model achieving a coefficient of determination (R2) of 0.9822. This study not only validates the effectiveness of the BAAM Bond bonding model but also provides theoretical support for optimizing the BAAM process. This contributes to improving the mechanical performance and application reliability of printed parts, promoting the broader application of additive manufacturing technology in construction and other fields.