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<p>Despite advancements in additive manufacturing, real-time monitoring of high-temperature fluctuations during fused deposition modelling (FDM) remains a critical challenge. These thermal variations can compromise print quality and structural consistency, highlighting the need for innovative solutions. Existing methods suffer from limited sensitivity and low-temperature tolerance, rendering them ineffective for monitoring high-temperature fluctuations. To address this, we propose a machine-learning assisted corn-shaped metamaterial-based (meta-) buffer rod (MBR) for online high-temperature monitoring. Inspired by corn’s structure, the proposed MBR is ingeniously fabricated with dual channels, thermal and ultrasonic, where the thermal channel is optimally designed using artificial neural network (ANN)-based surrogate modelling of the triply periodic minimal surface (TPMS) and the ultrasonic channel is engineered with 7% induced porosity leveraging the diffuse-wave field mechanism. Compared to conventional designs, the MBR demonstrates a remarkable ability to accurately record surface temperatures up to 190 °C while achieving a 60.9% reduction in mass when integrated into the FDM setup. The findings indicate the efficacy of the proposed MBR as a reliable tool for real-time high-temperature monitoring during the FDM process and for assessing the quality of printed components. This innovation paves the way for enhanced online monitoring of the high-temperature fluctuations, ultimately elevating product uniformity and quality.</p>

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A corn shaped ultrasonic meta-buffer rod design for online temperature monitoring in additive manufacturing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月25日

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  • Qi Zhu,
  • Haicheng Li,
  • Haiyan Zhang,
  • Qingqing Zhang,
  • Prateek Mittal,
  • Sriram Subramanian

摘要

Despite advancements in additive manufacturing, real-time monitoring of high-temperature fluctuations during fused deposition modelling (FDM) remains a critical challenge. These thermal variations can compromise print quality and structural consistency, highlighting the need for innovative solutions. Existing methods suffer from limited sensitivity and low-temperature tolerance, rendering them ineffective for monitoring high-temperature fluctuations. To address this, we propose a machine-learning assisted corn-shaped metamaterial-based (meta-) buffer rod (MBR) for online high-temperature monitoring. Inspired by corn’s structure, the proposed MBR is ingeniously fabricated with dual channels, thermal and ultrasonic, where the thermal channel is optimally designed using artificial neural network (ANN)-based surrogate modelling of the triply periodic minimal surface (TPMS) and the ultrasonic channel is engineered with 7% induced porosity leveraging the diffuse-wave field mechanism. Compared to conventional designs, the MBR demonstrates a remarkable ability to accurately record surface temperatures up to 190 °C while achieving a 60.9% reduction in mass when integrated into the FDM setup. The findings indicate the efficacy of the proposed MBR as a reliable tool for real-time high-temperature monitoring during the FDM process and for assessing the quality of printed components. This innovation paves the way for enhanced online monitoring of the high-temperature fluctuations, ultimately elevating product uniformity and quality.