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.

翻译：金属合金增材制造工艺的参数配置因输入参数（如激光功率、扫描速度）与打印输出质量间复杂的关联关系而成为极具挑战性的难题。通过标准试错法寻找可行参数配置的效率极低，因为每个配置的验证在资源（物理及人力成本）方面代价高昂，且配置空间极为庞大。本文结合AI驱动自适应实验设计的通用原理与领域知识，以解决发现可行配置这一挑战性问题。其核心思想是利用历史实验数据构建代理模型，从而在每次迭代中智能选择少量输入配置进行验证。为证明该方法的有效性，我们将其部署于定向能量沉积工艺，用于打印GRCop-42——一种由NASA为航空航天应用开发的高性能铜-铬-铌合金。在三个月内，我们的方法在多种激光功率条件下获得了多个无缺陷输出，与领域科学家耗时数月却未获成功的手动实验相比，显著缩短了成果产出时间并降低了资源消耗。通过首次在易获取的红外激光平台上实现高质量GRCop-42制造，我们推动了这一关键合金的普及应用，为航空航天领域的低成本、分布式生产铺平了道路。