# Agentic Additive Manufacturing Alloy Evaluation

arXiv: https://arxiv.org/abs/2510.02567 · HTML: https://mlsanity.com/paper/2510.02567/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 100 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 14 of 14)

- [Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions](https://mlsanity.com/paper/2503.23278/) (2025)
- [A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)](https://mlsanity.com/paper/2505.02279/) (2025)
- [AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence](https://mlsanity.com/paper/2407.10022/) (2024)
- [AMGPT: a Large Language Model for Contextual Querying in Additive Manufacturing](https://mlsanity.com/paper/2406.00031/) (2024)
- [MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning](https://mlsanity.com/paper/2201.11662/) (2022)
- [LLM-3D Print: Large Language Models To Monitor and Control 3D Printing](https://mlsanity.com/paper/2408.14307/) (2024)
- [Large Language Model Agent for Modular Task Execution in Drug Discovery](https://mlsanity.com/paper/2507.02925/) (2025)
- [Automating MD simulations for Proteins using Large language Models: NAMD-Agent](https://mlsanity.com/paper/2507.07887/) (2025)
- [LLM-Craft: Robotic Crafting of Elasto-Plastic Objects with Large Language Models](https://mlsanity.com/paper/2406.08648/) (2024)
- [AdditiveLLM: Large Language Models Predict Defects in Additive Manufacturing](https://mlsanity.com/paper/2501.17784/) (2025)
- [ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning](https://mlsanity.com/paper/2404.16882/) (2024)
- [LLM Trainer: Automated Robotic Data Generation via Demonstration Augmentation using LLMs](https://mlsanity.com/paper/2509.20070/) (2025)
- [LLM-Drone: Aerial Additive Manufacturing with Drones Planned Using Large Language Models](https://mlsanity.com/paper/2503.17566/) (2025)
- [MechProNet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing](https://mlsanity.com/paper/2209.12605/) (2022)

## Built on by (most cited first, 4 of 4)

- [Polymer-Agent: Large Language Model Agent for Polymer Design](https://mlsanity.com/paper/2601.16376/) (2026)
- [In-situ process monitoring for defect detection in wire-arc additive manufacturing: an agentic AI approach](https://mlsanity.com/paper/2604.09889/) (2026)
- [Material Database Agent: A Multimodal Agentic Framework for Scientific Literature Mining](https://mlsanity.com/paper/2605.04278/) (2026)
- [Kinematics-Grounded Agentic AI for Robotic Additive Manufacturing Process Planning](https://mlsanity.com/paper/2609.19347/) (2026)

## Abstract (arXiv)

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.