用智能代理自动评估增材制造合金，提升研发效率。
Agentic Additive Manufacturing Alloy Evaluation
- 基于大模型的多智能体系统，自动调用工具完成热物性计算与缺陷分析。
- 可分析SS316L和IN718等常见合金的成形窗口及改性方案。
- 支持动态调整任务路径，适合材料研发人员快速验证新合金设计。
智能代理系统通过整合研究工具，增强研究人员解决复杂问题的能力。在增材制造领域，合金选型与评估涉及材料科学、热力学模拟与实验分析等多个专业方向，极具挑战性。本文利用大语言模型（LLM）驱动的多智能体系统，通过模型上下文协议（MCP）调用工具，实现热物性参数图计算、未熔合过程图生成等自动化操作。该系统能对常见合金如SS316L和IN718及其成分变体进行成形窗口分析，并根据工具调用结果动态调整任务路径，实现自主决策。本工作展示了基于LLM的多智能体系统在加速已知与新型增材制造合金评估中的潜力。
原文摘要 · Abstract (English)
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.
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