Porosity evaluation plot

This example shows how to use PyAdditive-Widgets to visualize the results of a parametric study containing porosity simulations. It uses the display package to visualize the results of the study.

Units are SI (m, kg, s, K) unless otherwise noted.

Perform required imports and create study

Perform the required imports and create a ParametricStudy instance.

from ansys.additive.core import Additive
from ansys.additive.core.parametric_study import ParametricStudy

from ansys.additive.widgets import display

study = ParametricStudy("porosity-study")

Saving parametric study to /home/runner/work/pyadditive-widgets/pyadditive-widgets/examples/porosity-study.ps

Get name of study file

The current state of the parametric study is saved to a file on each update. This code retrieves the name of the file. This file uses a binary format and is not human readable.

print(study.file_name)

/home/runner/work/pyadditive-widgets/pyadditive-widgets/examples/porosity-study.ps

Select material for study

Select a material to use in the study. The material name must be known by the Additive service. You can connect to the Additive service and print a list of available materials prior to selecting one.

additive = Additive()
additive.materials_list()
material = "IN718"

user data path: /home/runner/.local/share/pyadditive

Create porosity evaluation

Generate a set of porosity simulations using the generate_porosity_permutations() method. This method’s parameters allow you to specify the material and machine parameters for the simulations. Not all parameters are required. Optional parameters that are not specified use the default values defined in the MachineConstants class.

# Specify a range of laser powers. Valid values are 50 to 700 W.
laser_powers = [50, 150, 250, 350]
# Specify a range of laser scan speeds. Valid values are 0.35 to 2.5 m/s.
scan_speeds = [0.35, 0.5, 0.7]
# Specify a range of layer thicknesses. Valid values are 10-6 to 100e-6 m.
layer_thicknesses = [40e-6]
# Specify a range of heater temperatures. Valid values 20 - 500 C.
heater_temperatures = [80]
# Specify laser beam diameters. Valid values are 20e-6 to 140e-6 m.
beam_diameters = [80e-6]
# Specify the machine parameters for the simulations.
start_angles = [45]
rotation_angles = [67.5]
hatch_spacings = [100e-6]
stripe_widths = [0.05]

study.generate_porosity_permutations(
    material_name=material,
    laser_powers=laser_powers,
    scan_speeds=scan_speeds,
    size_x=1e-3,
    size_y=1e-3,
    size_z=1e-3,
    layer_thicknesses=layer_thicknesses,
    heater_temperatures=heater_temperatures,
    beam_diameters=beam_diameters,
    start_angles=start_angles,
    rotation_angles=rotation_angles,
    hatch_spacings=hatch_spacings,
    stripe_widths=stripe_widths,
    iteration=1,
)

Show simulations as a table

Use the display package to list the simulations as a table.

display.show_table(study)

show_table

Run porosity simulations

Run the simulations using the run_simulations() method.

study.run_simulations(additive)

2024-06-14 17:49:37 Completed 0 of 12 simulations
2024-06-14 17:52:29 Completed 1 of 12 simulations
2024-06-14 17:54:28 Completed 2 of 12 simulations
2024-06-14 17:55:57 Completed 3 of 12 simulations
2024-06-14 18:01:42 Completed 4 of 12 simulations
2024-06-14 18:04:33 Completed 5 of 12 simulations
2024-06-14 18:06:15 Completed 6 of 12 simulations
2024-06-14 18:31:08 Completed 7 of 12 simulations
2024-06-14 18:39:35 Completed 8 of 12 simulations
2024-06-14 18:42:46 Completed 9 of 12 simulations
2024-06-14 19:17:25 Completed 10 of 12 simulations
2024-06-14 19:37:36 Completed 11 of 12 simulations
2024-06-14 19:45:03 Completed 12 of 12 simulations

Plot porosity results

Visualize porosity simulation results for relative density and build rate as a contour plot using the porosity_contour_plot() function.

display.porosity_contour_plot(study)

porosity_contour_plot

Visualize a heat map plot of porosity results to determine parametric regions with desirable relative density statistics using the porosity_eval_plot() function.

display.porosity_eval_plot(study)

porosity_eval_plot