eagar-tsai 0.3.1

Eagar–Tsai moving heat source model for melt pool dimension estimation

eagar-tsai

eagar-tsai is a Python library implementing the Eagar–Tsai moving heat source model to estimate melt pool dimensions (length, width, depth) for a scanning laser over a semi-infinite solid. Temperature fields are computed via a 1D integral; melt pool dimensions are extracted from the liquidus isotherm. Built-in plotting covers temperature field heatmaps and power-velocity printability maps.

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Installation

Pre-built binary wheels are published to PyPI for Python 3.11, 3.12, and 3.13 on Linux (x86-64, i686), macOS (x86-64 and Apple Silicon), and Windows (AMD64). If a matching wheel exists for your platform, no C compiler is needed.

# Recommended — using uv
uv add eagar-tsai

# Alternative — using pip
pip install eagar-tsai

[!NOTE] If no pre-built wheel matches your platform (for example, a non-standard Linux architecture or a Python version outside the supported range), the package falls back to building from source. In that case a C compiler is required: GCC or Clang on Linux/macOS; MSVC Build Tools or MinGW-w64 on Windows.

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Quick Start

For a single parameter set, use compute_single_point directly:

from eagar_tsai import BeamParameters, MaterialProperties, SimulationDomain, compute_single_point

beam = BeamParameters(
        beam_diameter=100e-6,
        power=200.0,
        velocity=0.5,
        absorptivity=0.35
)

material = MaterialProperties(
        liquidus_temperature=1700.0,
        thermal_conductivity=30.0,
        density=7800.0,
        specific_heat=700.0
)

domain = SimulationDomain(
        x_length_um=1200.0,
        y_length_um=1200.0,
        z_depth_um=1000.0,
        spatial_resolution_um=1.0,
)

result = compute_single_point(beam, material, domain)

print(f"Length: {result.length_um:.1f} µm")
print(f"Width:  {result.width_um:.1f} µm")
print(f"Depth:  {result.depth_um:.1f} µm")

To run multiple parameter sets in parallel, use compute_melt_pool with a DataFrame. workers, chunk_size, and output_dir are optional:

import pandas as pd
from eagar_tsai import compute_melt_pool

df = pd.DataFrame({
    "velocity_m_s":              [0.5],
    "power_w":                   [200.0],
    "beam_diameter_m":           [100e-6],
    "absorptivity":              [0.35],
    "liquidus_temperature_k":    [1700.0],
    "thermal_conductivity_w_mk": [30.0],
    "density_kg_m3":             [7800.0],
    "specific_heat_j_kgk":       [700.0],
})

result = compute_melt_pool(
    data=df,
    workers=4,                # parallel worker processes (default: None, serial)
    chunk_size=50,            # rows per worker chunk (default: 50)
    output_dir="CalcFiles/",  # write per-chunk CSVs (default: None)
)
print(result[["melt_length_um", "melt_width_um", "melt_depth_um"]])

Required Input Columns

Column	Unit	Description
velocity_m_s	m/s	Scan velocity
power_w	W	Laser power
beam_diameter_m	m	Beam diameter (2σ)
absorptivity	—	Absorptivity (0, 1]
liquidus_temperature_k	K	Liquidus temperature
thermal_conductivity_w_mk	W/(m·K)	Thermal conductivity at liquidus
density_kg_m3	kg/m³	Density
specific_heat_j_kgk	J/(kg·K)	Specific heat at liquidus

Output Columns Added to the DataFrame

Column	Unit
melt_length	m
melt_width	m
melt_depth	m
melt_length_um	µm
melt_width_um	µm
melt_depth_um	µm
peak_temperature	K
min_temperature	K

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Temperature Field Visualization

compute_single_point returns a MeltPoolResult that always includes the full TemperatureField and a built-in plot method:

from eagar_tsai import BeamParameters, MaterialProperties, SimulationDomain, compute_single_point

beam = BeamParameters(
        beam_diameter=100e-6,
        power=200.0,
        velocity=0.5,
        absorptivity=0.35
)

material  = MaterialProperties(
        liquidus_temperature=1700.0,
        thermal_conductivity=30.0,
        density=7800.0,
        specific_heat=700.0
)

domain = SimulationDomain(
        x_length_um=320.0,
        y_length_um=110.0,
        z_depth_um=60.0,
        spatial_resolution_um=5.0,
)

result = compute_single_point(
        beam=beam,
        material=material,
        domain=domain
)

print(result.length_um)                        # melt pool length in µm
print(result.temperature_field.T_xy.shape)     # (ny, nx) — surface plane in Kelvin
print(result.temperature_field.T_xz.shape)     # (nz, nx) — depth cross-section in Kelvin

fig = result.plot(output="temperature_field.png") # equivalently: result.temperature_field.plot(output="temperature_field.png")

The standalone convenience function skips constructing the MeltPoolResult object explicitly:

from eagar_tsai.plot import plot_temperature_field

fig = plot_temperature_field(beam, material, domain, output="temperature_field.png")

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Printability Maps

compute_printability_map sweeps laser power and scan speed over a regular grid, runs the Eagar–Tsai model at every point, and classifies each point into one of four defect regimes (keyhole porosity, lack of fusion, balling, or defect-free) using the five criteria from Sheikh et al. (2023). Each grid point is dispatched as an independent parallel task, so workers stay fully utilized even when isolated points require iterative domain expansion.

from eagar_tsai import (
    MaterialProperties,
    PrintabilityParameters,
    SimulationDomain,
    compute_printability_map,
)
from eagar_tsai.plot import plot_printability_map

material = MaterialProperties(
    liquidus_temperature=1700.0,
    thermal_conductivity=30.0,
    density=7800.0,
    specific_heat=700.0,
)

process = PrintabilityParameters(
    beam_diameter_m=80e-6,
    absorptivity=0.35,
    layer_thickness_m=40e-6,
    hatch_spacing_m=90e-6,
)

domain = SimulationDomain(
    x_length_um=1200.0,
    y_length_um=1200.0,
    z_depth_um=1000.0,
    spatial_resolution_um=5.0,  # coarser grid, ~25x faster than 1 µm
)

# Raw data: one row per grid point with defect classification
df = compute_printability_map(
    process,
    material,
    power_range=(50.0, 400.0),
    velocity_range=(0.1, 3.0),
    n_power=50,
    n_velocity=50,
    domain=domain,
    workers=-1,  # use all CPU cores
)

print(df["defect"].value_counts())

# Or render directly as a color-coded figure
fig = plot_printability_map(
    process,
    material,
    power_range=(50.0, 400.0),
    velocity_range=(0.1, 3.0),
    n_power=50,
    n_velocity=50,
    domain=domain,
    workers=-1,
    output="printability_map.png",
)

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References

• T. W. Eagar and N.-S. Tsai, "Temperature Fields Produced by Traveling Distributed Heat Sources," Welding Journal (Research Supplement), December 1983, pp. 346-s–354-s.
• C integrand reformulation: Sasha Rubenchik, LLNL, 2015.

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License

This project is licensed under the GNU GPLv3 License. See the LICENSE file for details.

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Citation

We are currently preparing a preprint for publication. If you use eagar-tsai in your research, please cite the following:

Sarıtürk, D., & Vela, B. (2026). eagar-tsai. Zenodo. https://doi.org/10.5281/zenodo.19837225

BibTeX:

@software{sariturk_2026_19837225,
  author    = {Sarıtürk, Doğuhan and Vela, Brent},
  title     = {eagar-tsai},
  year      = 2026,
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.19837225},
  url       = {https://doi.org/10.5281/zenodo.19837225},
}