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

arXiv: https://arxiv.org/abs/2601.17587 · HTML: https://mlsanity.com/paper/2601.17587/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 24 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints](https://mlsanity.com/paper/2009.01721/) (2020)
- [Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces](https://mlsanity.com/paper/2111.01186/) (2021)
- [Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2204.05944/) (2022)
- [Mercer Features for Efficient Combinatorial Bayesian Optimization](https://mlsanity.com/paper/2012.07762/) (2020)
- [Bayesian Optimization with Expensive Integrands](https://mlsanity.com/paper/1803.08661/) (2018)
- [Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings](https://mlsanity.com/paper/2303.01774/) (2023)
- [Active Learning under Label Shift](https://mlsanity.com/paper/2007.08479/) (2020)
- [Bayesian Optimization over Hybrid Spaces](https://mlsanity.com/paper/2106.04682/) (2021)
- [Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing](https://mlsanity.com/paper/2107.12809/) (2021)
- [Efficient nonmyopic active search with applications in drug and materials discovery](https://mlsanity.com/paper/1811.08871/) (2018)

## Built on by (most cited first, 1 of 1)

- [Black-Box Optimization From Small Offline Datasets via Meta Learning with Synthetic Tasks](https://mlsanity.com/paper/2604.12325/) (2026)

## Abstract (arXiv)

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.