Researchers atCarnegie Mellon University(CMU) have developed a multi-agent, Large Language Model (LLM)-orchestrated workflow designed to automate early-stage alloy evaluation for laser powder bed fusion (LPBF). Reported inAdditive Manufacturing Letters, the system integratesThermo-Calcproperty calculations with an analytical melt pool model to generate lack of fusion (LoF) process maps for screening known and proposed alloy compositions.
Automating a multi-tool workflow for alloy screening
Selecting alloys for additive manufacturing typically requires coordination across thermodynamic databases, simulation tools, and defect modeling. In the CMU study, an LLM acts as an “agent” that dispatches tool calls to external software and interprets the results to evaluate alloy candidates and LPBF parameter windows.
The workflow combines three tool servers exposed through Model Context Protocol (MCP): a Thermo-Calc layer for CALPHAD-based property prediction, an additive-manufacturing package for process-map generation, and a workspace tool for state management. The LLM orchestrates these tools by converting natural-language prompts into structured inputs and summarizing outputs.
From thermophysical properties to lack of fusion maps
For each alloy, the system generates a composition file containing elemental mass fractions. Known alloys are retrieved from a lookup table, while hypothetical compositions are parsed from user prompts.
Thermo-Calc is used to compute density, thermal conductivity, specific heat capacity, and phase transition temperatures. A database-selection routine maps compositions to appropriate Thermo-Calc databases, with fallback options for multi-principal element alloys.
Absorptivity is estimated using a Drude-based approximation derived from electrical resistivity at 1070 nm. The authors note this serves as a baseline and may be less accurate for materials with strong power-dependent absorptivity effects.
The additive-manufacturing module then estimates melt pool dimensions using Rosenthal’s analytical heat-source model and applies a lack-of-fusion overlap criterion based on hatch spacing, layer height, and melt pool geometry. The resulting process map classifies beam power and scan velocity combinations as within or outside the LoF regime.
The current implementation assumes conduction-mode melt pool behavior and does not directly model keyholing or balling, which would typically require computational fluid dynamics (CFD) approaches.
Source: 3D Printing Industry
