Single bead analysis

This example shows how to use PyAdditive to determine melt pool characteristics for a given material and machine parameter combinations.

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

Perform required imports and connect

Perform the required imports and connect to the Additive service.

import matplotlib.pyplot as plt

from ansys.additive.core import (
    Additive,
    AdditiveMachine,
    MeltPoolColumnNames,
    SimulationError,
    SingleBeadInput,
)

additive = Additive()

2024-07-23 22:41:29 Connected to localhost:50052

Get server connection information

Get server connection information using the about() method.

additive.about()

Client 0.18.2, API version: 1.7.2
ServerConnectionStatus(connected=True, channel_str='localhost:50052', metadata={'API version': '1.6.7', 'Version': '24.2.0'})

Select material

Select a material. You can use the materials_list() method to obtain a list of available materials.

print("Available material names: {}".format(additive.materials_list()))

Available material names: ['316L', 'AlSi10Mg', 'IN625', '17-4PH', 'Al357', 'Ti64', 'CoCr', 'IN718']

You can obtain the parameters for a single material by passing a name from the materials list to the material() method.

material = additive.material("IN718")

Specify machine parameters

Specify machine parameters by first creating an AdditiveMachine object and then assigning the desired values. All values are in SI units (m, kg, s, K) unless otherwise noted.

machine = AdditiveMachine()

# Show available parameters
print(machine)

AdditiveMachine
laser_power: 195 W
scan_speed: 1.0 m/s
heater_temperature: 80 °C
layer_thickness: 5e-05 m
beam_diameter: 0.0001 m
starting_layer_angle: 57 °
layer_rotation_angle: 67 °
hatch_spacing: 0.0001 m
slicing_stripe_width: 0.01 m

Set laser power and scan speed

Set the laser power and scan speed.

machine.scan_speed = 1  # m/s
machine.laser_power = 300  # W

Specify inputs for single bead simulation

Create a SingleBeadInput object containing the desired simulation parameters.

input = SingleBeadInput(
    machine=machine, material=material, id="single-bead-example", bead_length=0.0012  # meters
)

Run simulation

Use the simulate() method of the additive object to run the simulation. The returned object is either a SingleBeadSummary object containing the input and a MeltPool or a SimulationError object.

summary = additive.simulate(input)
if isinstance(summary, SimulationError):
    raise Exception(summary.message)

Plot melt pool statistics

Obtain a Pandas DataFrame instance containing the melt pool statistics by using the data_frame() method of the melt_pool attribute of the summary object. The column names for the DataFrame instance are described in the documentation for data_frame(). Use the plot() method to plot the melt pool dimensions as a function of bead length.

df = summary.melt_pool.data_frame().multiply(1e6)  # convert from meters to microns
df.index *= 1e3  # convert bead length from meters to millimeters

df.plot(
    y=[
        MeltPoolColumnNames.LENGTH,
        MeltPoolColumnNames.WIDTH,
        MeltPoolColumnNames.DEPTH,
        MeltPoolColumnNames.REFERENCE_WIDTH,
        MeltPoolColumnNames.REFERENCE_DEPTH,
    ],
    ylabel="Melt Pool Dimensions (µm)",
    xlabel="Bead Length (mm)",
    title="Melt Pool Dimensions vs Bead Length",
)
plt.show()

List melt pool statistics

You can show a table of the melt pool statistics by typing the name of the data frame object and pressing enter. For brevity, the following code uses the head() method so that only the first few rows are shown. Note, if running this example as a Python script, no output is shown.

df.head()

	length	width	depth	reference_width	reference_depth
bead_length
0.00	91.414399	93.027800	27.820900	0.000000	0.000000
0.02	125.782996	120.333999	55.808499	26.588401	5.808540
0.04	148.120999	130.749002	75.404100	64.150102	25.404099
0.06	168.338999	139.558002	89.802198	81.839800	39.802201
0.08	187.985003	145.352006	97.820804	91.859899	47.820799

Save melt pool statistics

Save the melt pool statistics to a CSV file using the to_csv() method.

df.to_csv("melt_pool.csv")