Discovery of Feasible 3D Printing Configurations for Metal Alloys via AI-driven Adaptive Experimental Design
Azza Fadhel, Nathaniel W. Zuckschwerdt, Aryan Deshwal, Susmita Bose, Amit Bandyopadhyay, Jana Doppa

TL;DR
This paper presents an AI-driven adaptive experimental design method to efficiently discover feasible 3D printing configurations for metal alloys, significantly reducing time and resources needed compared to manual trial-and-error approaches.
Contribution
It introduces a surrogate model-based adaptive experimental framework tailored for metal additive manufacturing, enabling rapid discovery of defect-free printing parameters.
Findings
Achieved defect-free GRCop--42 prints within three months.
Reduced experimental resources and time compared to manual methods.
Enabled high-quality alloy fabrication on accessible laser platforms.
Abstract
Configuring the parameters of additive manufacturing processes for metal alloys is a challenging problem due to complex relationships between input parameters (e.g., laser power, scan speed) and quality of printed outputs. The standard trial-and-error approach to find feasible parameter configurations is highly inefficient because validating each configuration is expensive in terms of resources (physical and human labor) and the configuration space is very large. This paper combines the general principles of AI-driven adaptive experimental design with domain knowledge to address the challenging problem of discovering feasible configurations. The key idea is to build a surrogate model from past experiments to intelligently select a small batch of input configurations for validation in each iteration. To demonstrate the effectiveness of this methodology, we deploy it for Directed Energy…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsAdditive Manufacturing Materials and Processes · Machine Learning in Materials Science · Additive Manufacturing and 3D Printing Technologies
