Discovery of Feasible 3D Printing Configurations for Metal Alloys via AI-driven Adaptive Experimental Design

2026-01-24

AI Summary

This study addresses the complex relationship between process parameters and part quality in metal alloy additive manufacturing, where traditional trial-and-error approaches are inefficient and costly. The authors propose an AI-driven adaptive experimental design method that integrates domain knowledge, leveraging surrogate modeling and a small-batch active learning strategy to intelligently select a minimal set of candidate parameters for iterative validation. This approach efficiently explores the feasible process window, enabling the first successful fabrication of high-performance Cu-Cr-Nb (GRCop-42) alloy via infrared laser-directed energy deposition. Within three months, multiple defect-free specimens were produced across a wide range of laser powers, significantly accelerating development timelines and reducing reliance on expensive equipment and expert intuition—thereby advancing decentralized manufacturing of critical materials.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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 Deposition process to print GRCop--42, a high-performance copper--chromium--niobium alloy developed by NASA for aerospace applications. Within three months, our approach yielded multiple defect-free outputs across a range of laser powers dramatically reducing time to result and resource expenditure compared to several months of manual experimentation by domain scientists with no success. By enabling high-quality GRCop--42 fabrication on readily available infrared laser platforms for the first time, we democratize access to this critical alloy, paving the way for cost-effective, decentralized production for aerospace applications.

Problem

• additive manufacturing
• metal alloys
• parameter configuration
• 3D printing
• feasible configurations

Innovation

• AI-driven adaptive experimental design
• surrogate modeling
• metal additive manufacturing
• GRCop-42
• Directed Energy Deposition