Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing
arXiv:2606.10227v1 Announce Type: new Abstract: Metal additive manufacturing enables the fabrication of complex parts, but achieving consistent build quality remains challenging due to interactions induced by repeated layer-wise melting, solidification, and reheating across the 3D build. Advanced sensing provide a great opportunity to collect rich observations of the actual manufacturing process for real-time quality monitoring and control. Yet, existing methods often have limited ability to represent multi-layer interactions and quantify their contributions to quality. In this paper, we devel
The proliferation of advanced sensing and AI capabilities is enabling real-time monitoring and control in complex manufacturing processes like metal additive manufacturing.
Improving quality and consistency in additive manufacturing is crucial for its broader adoption in critical applications, impacting various industrial sectors.
The ability to accurately model and predict 3D neighborhood interactions in additive manufacturing will lead to more reliable and higher-quality printed metal parts.
- · Additive manufacturing industry
- · Aerospace and defense sectors
- · AI/ML companies specializing in industrial applications
- · Materials science research
- · Traditional subtractive manufacturing (competitive pressure)
- · Companies unable to integrate AI/ML into their manufacturing workflows
Enhanced quality control and reduced waste in metal additive manufacturing.
Accelerated development and adoption of complex metal components in various industries.
Potential for new product categories and capabilities previously limited by manufacturing constraints or cost.
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