How physics informed AI copilot are unlocking Additive Manufacturing at scale

O. Fergani, K. Eissing
1000 Kelvin LLC,
United States

Keywords: physics, AI, copilot, additive manufacturing


The advent of Additive Manufacturing (AM) has revolutionized the production landscape, offering unprecedented design flexibility and material efficiency. However, its widespread adoption at scale has been hindered by challenges in cost, scalability, and yield optimization. This presentation introduces a novel product, AMAIZE a physics-informed AI copilots to address these critical barriers. The developed AI models serve a dual purpose: firstly, they automate the software workflow in AM processes, significantly reducing manual intervention and associated time costs. Secondly, they generate an optimal thermal management Computer-Aided Manufacturing (CAM) scan strategy. This strategy is pivotal in maintaining material integrity and minimizing defects during the manufacturing process. By integrating principles of physics directly into the AI algorithms, the models offer enhanced predictability and control over the AM process. The implementation of these AI copilots demonstrates a substantial improvement in production efficiency and yield, effectively unlocking the potential for large-scale deployment of Additive Manufacturing. This breakthrough represents a significant stride in overcoming the predominant challenges in AM, setting a new standard for the industry.