About this Abstract |
| Meeting |
2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
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| Symposium
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2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
|
| Presentation Title |
G-Code-Aware Segmentation for Quality Monitoring in Direct Ink Writing |
| Author(s) |
Jesus Diaz, Syed Ziaul Bin Bashar, Satyajayant Misra, Chaitanya Mahajan |
| On-Site Speaker (Planned) |
Jesus Diaz |
| Abstract Scope |
Direct Ink Writing (DIW) is gaining momentum for its capacity to deposit a wide range of materials; however, the inherent rheological behavior of these substances often leads to expansion and shrinkage, compromising structural quality. To mitigate these geometric inaccuracies, we present GG-Net, a U-Net-derived segmentation model designed for real-time quality monitoring. GG-Net processes in-situ imagery at each layer, comparing the printed output directly against the intended toolpath mapping. By effectively distinguishing the top-most layer from underlying structures, the framework enables a precise, layer-by-layer evaluation of the printed part’s fidelity. This automated comparison enables immediate identification of deviations between the physical print and its digital counterpart, thereby minimizing waste and improving the efficiency of the material extrusion process. |
| Proceedings Inclusion? |
Undecided |