Agentic systems enable the intelligent use of research tooling, augmenting a researcher's ability to investigate and propose novel solutions to existing problems. Within Additive Manufacturing (AM), alloy selection and evaluation remains a complex challenge, often requiring expertise in the various domains of materials science, thermodynamic simulations, and experimental analysis. Large Language Model (LLM) enabled agents can facilitate this endeavor by utilizing their extensive knowledge base to dispatch tool calls via Model Context Protocol (MCP) to perform actions such as thermophysical property diagram calculations and lack of fusion process map generation. In addition, the multi-agent system can effectively reason through complex user prompts and provide analysis on the lack of fusion process window of common alloys such as SS316L and IN718 along with proposed composition variants of known alloys. These agents can dynamically adjust their task trajectory to the outcomes of tool call results, effectively enabling autonomous decision-making in practical environments. This work aims to showcase the benefits of adopting a LLM enabled multi-agent system to automate and accelerate the task of evaluating proposed additive manufacturing alloys, both novel and known.

翻译：智能体系统能够智能运用研究工具，增强研究人员探索现有问题并提出创新解决方案的能力。在增材制造领域，合金选择与评估始终是一项复杂挑战，通常需要跨材料科学、热力学模拟及实验分析等多领域的专业知识。基于大语言模型的智能体可通过调用其广泛知识库，利用模型上下文协议调度工具调用，以执行热物理性质相图计算、未熔合工艺窗口图生成等操作，从而有效推进该研究进程。此外，多智能体系统能够对复杂的用户指令进行逻辑推演，针对SS316L、IN718等常见合金的未熔合工艺窗口提供分析报告，并提出已知合金的成分变体方案。这些智能体可根据工具调用结果动态调整任务执行路径，从而在实际环境中实现自主决策。本研究旨在展示采用大语言模型驱动的多智能体系统，在自动化与加速新型及已知增材制造合金评估任务方面的显著优势。