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<p>The industrial application of gas tungsten arc welding (GTAW)-based wire arc additive manufacturing (WAAM) for Ti-6Al-4V remains limited by oxidation sensitivity, bead variability, and lack of in situ monitoring. This study reports the development of an intelligent WAAM system integrating an Arduino-driven IoT module for real-time process data acquisition and a custom trailing shielding design for oxidation control. The shielding system maintained oxygen levels below 50 ppm, enabling defect-less deposition. A hybrid modeling framework combining regression analysis with artificial neural networks (ANN) achieved high predictive accuracy for bead geometry (<i>R</i><sup>2</sup> &gt; 0.95), demonstrating effective parameter control. Microstructural analysis revealed homogeneous <i>α</i> + <i>β</i> Widmanstätten morphologies with refined prior-<i>β</i> grains, while hardness mapping showed a increase in the heat-affected zones and fusion zones relative to the base metal and further strengthening in the fusion zone, consistent with α martensite formation. Despite localized hardening, tensile and flexural tests confirmed superior ductility of the WAAM samples compared to the as-received alloy. These findings establish that intelligent shielding and data-driven modeling can overcome oxidation, geometric inconsistency, and microstructural instability, advancing GTAW-WAAM as a viable route for high-integrity Ti-6Al-4V components in aerospace and biomedical sectors.</p> Graphical Abstract <p></p>

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Wire Arc Additive Manufacturing via Internet of Things Integrated Trailing Shielding and Artificial Neural Network-Based Bead Geometry Prediction

  • 期刊论文

  • 发表日期：

    2025年12月24日

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  • Sai Kiran Kaleru,
  • Ravi Shanker Vidyarthy,
  • Suresh Kumar Reddy Narala,
  • Kishore Babu Nagumothu

摘要

The industrial application of gas tungsten arc welding (GTAW)-based wire arc additive manufacturing (WAAM) for Ti-6Al-4V remains limited by oxidation sensitivity, bead variability, and lack of in situ monitoring. This study reports the development of an intelligent WAAM system integrating an Arduino-driven IoT module for real-time process data acquisition and a custom trailing shielding design for oxidation control. The shielding system maintained oxygen levels below 50 ppm, enabling defect-less deposition. A hybrid modeling framework combining regression analysis with artificial neural networks (ANN) achieved high predictive accuracy for bead geometry (R2 > 0.95), demonstrating effective parameter control. Microstructural analysis revealed homogeneous α + β Widmanstätten morphologies with refined prior-β grains, while hardness mapping showed a increase in the heat-affected zones and fusion zones relative to the base metal and further strengthening in the fusion zone, consistent with α martensite formation. Despite localized hardening, tensile and flexural tests confirmed superior ductility of the WAAM samples compared to the as-received alloy. These findings establish that intelligent shielding and data-driven modeling can overcome oxidation, geometric inconsistency, and microstructural instability, advancing GTAW-WAAM as a viable route for high-integrity Ti-6Al-4V components in aerospace and biomedical sectors.

Graphical Abstract