AM PravaH User Manual 

System Requirements: 

System requirements, how to install AM PravaH after downloading, detailed step-by-step guide with screenshots. Licensing

Step 1: Unzip the downloaded AM PravaH package. Navigate the "amp-installation" folder.

Step 2: Execute the "install" file using the commands provided below. 

This will install the complete AM PravaH software to your system. 

Step 3: Restart the system to see AM PravaH in the Applications list. Launch

AM PravaH from the taskbar or 'Applications' menu.

AM PravaH features an intuitive GUI. AM pravaH 1.5 comes with three modules named, 

1. Macroscale modeling

2. Microscale modeling

3. Deep learning

Create New: 

1. Enter the project name.

2. Click "CREATE NEW" and save it in the desired directory.

Now users can use any desired module and everything will be saved in the provided project folder. 

Load Results:

1. Click "LOAD RESULTS."

2. Navigate to the project folder.

3. Select the ".pravah" file and click "open" to load previous results.

The "Macroscale Modeling'' module in AM PravaH facilitates the execution of Laser Powder Bed Fusion (LPBF) or Welding simulations, enabling the examination of meltpool dynamics. This module encompasses the entire physics involved in the LPBF or Welding process, covering multiple aspects, including:

1. Four phases (Solid metal, Molten metal, Metal vapor, Inert gas) 

2. Phase Change (Melting, Vaporization)

3. Particle Dynamics

4. Heat & Mass Transfer

5. Laser Dynamics

6. Temperature-Dependent Surface Tension

7. Marangoni Convection

8. Spattering

9. Keyhole Effect

10. Pore Formation

This comprehensive coverage ensures that the results obtained from this module closely align with experimental outcomes.

Running first Macroscale Simulation: 

In the macroscale module, there are three input sections under settings.

1. Alloy and Inert gas 

2. Process Parameters  

3. Computational settings

Step 1: Alloy and Inert gas: 

This section involves selecting the material and defining its thermophysical properties for all three metal phases and the Inert gas.

Alloy Thermophysical properties:

Choose the alloy from a dropdown list, and properties for common alloys are provided. Users can edit properties, which won't alter defaults but will reflect in the current project upon clicking "SAVE."

Shielding Gas Properties:

Default properties for Argon and Air are provided. Users can customize these properties and save changes.

Vaporization State:

Activate or deactivate vaporization to study its effect. Deactivating vaporization reduces simulation time for processes where vaporization has minimal impact on results.

Lee Constant: 

Lee Constants are parameters that govern the rates of melting and solidification. Default values are provided, allowing users to conduct experiments. The Lee constants exhibit an inverse proportionality to the square of the mesh size.

Formulae for Lee Constants:

For a mesh size of approximately 4.45e-6, the default values are as follows:

• Solid to Liquid Lee Constant = 35e5

• Liquid to Solid Lee Constant = -0.7e5

Users can calculate Lee constant values for a new mesh size using the following formulas:

• Solid to Liquid Lee Constant = 35e5 * (4.45e5/ dx)^2

• Liquid to Solid Lee Constant = -0.7e5 * (4.45e5/ dx)^2

Here, 'dx' represents the new mesh size. 

Note: Solid to Liquid Lee constant is positive, Liquid to Solid Lee constant is negative.

Solid absorptivity and Molten absorptivity: 

Absorptivity here is the property of a material to absorb the laser per unit length, unit (1/meter). 

• Input your preferred settings and click the “Save” button to apply the changes.

• To revert to the default properties, use the “Reset” button.

• After resetting, finalize by clicking the “Save” button to restore the properties to their default values.

Step 2: Configuring Process Parameters: 

In this section, you can set various process parameters crucial to the additive manufacturing simulation. The following parameters can be adjusted.

Upload Scan Pattern:

Click the “Upload scan pattern” button to navigate to the project directory, where the system generates a “datasets” folder within your project directory.

Inside the "datasets" folder, default scan patterns include “Pattern.csv” for a single layer and “multi_layer.csv” for a multilayer scan track. 

Select a desired .csv file, click "Open" to upload, and confirm the upload in the terminal. Click on open after selecting the desired .csv file and pattern will be uploaded. In terminal, user can ensure the file is uploaded

To customize the scan track, users can navigate to the project folder and open the .csv file. The initial column denotes the layer number, the second column signifies the simulation time commencing from zero, and the subsequent three columns represent the x, y, and z coordinates of the laser. The sixth column indicates whether the laser is turned on or off at the corresponding time. 

In the default "pattern.csv," the laser is activated from 0 seconds to 0.0002 seconds and promptly deactivated at 0.00021 seconds. It remains inactive until 0.0008 seconds, representing the solidification phase. 

Within the default "multi_layer.csv," a predefined two-layer pattern is available. The numeral '2' in the initial column signifies the second layer. The initiation of  the second layer occurs immediately after the completion of the first layer. Specifically, the start time for the second layer is at 0.000801 seconds, just after the conclusion of the last layer at 0.0008 seconds, and the laser is activated. 

Users have the flexibility to assign any name to these input files, provided they have the .csv extension. This allows users to craft customized scan patterns tailored to their specific requirements.

Powder distribution: 

Users have the option to upload a personalized powder distribution through a .csv file. The system includes a default particle distribution named "particle_distribution.csv" within the datasets.

In the data structure, the initial column represents particle size, and the second column denotes the ratio of that particular particle size within the powder. For instance, in the first row, a value of 0.1 indicates that 10% of the particles have a diameter of 1.4E-05. It is essential to ensure that the sum of all particle ratios consistently equals '1'.

Laser direction: 

The laser direction is the orientation of the laser beam vector, typically directed downward. This downward orientation can be specified by assigning a value of '-1' to the 'Along Z' axis, with other axes set to zero.

Users have the flexibility to define any direction by combining the X, Y, and Z axes. For example, suppose we desire a laser in the X-Z plane inclined at an angle 'θ' degrees to the -Z axis. In this case, the mathematical representation would be Z = 

-rcos(θ), X = rsin(θ), assuming r = 1. These calculations allow the determination of the vector coefficients along the X, Y, and Z directions.

D4sigma: 

In AM-PravaH, a Gaussian laser is employed, and users are required to furnish the "D4sigma" value for it. Alternatively, if the user is unaware of the D4sigma value, they can input the laser spot diameter, which serves the same purpose. The D4sigma parameter characterizes the power distribution of the laser within the laser spot, playing a critical role in influencing the size and shape of the meltpool.

Fresnel coefficient: 

The solver in AM-PravaH employs the Fresnel reflection model to simulate laser reflection. This model comes into play when a laser beam transitions from one medium to another, and the interface acts as a reflective surface, causing a portion of the laser power to be reflected.

Fresnel coefficients are crucial for the liquid-gas interface and liquid-vapor interface, significantly influencing energy absorption and laser penetration into the keyhole. A higher Fresnel coefficient corresponds to increased absorption and reduced reflection, shaping the dynamics of laser-material interactions during the additive manufacturing process.

Substrate height: 

It is the height or thickness of the base on which powder is spread. 

Spreader clearance: 

It is a distance above between the substrate or previous layer to the spreader in meters. Required Powder bed thickness can be given by giving the right spreader clearance.  

Velocity of spreader: 

Spreader velocity affects the powder spreading, input value is in m/s.

• Press the “SAVE” button to save all the values provided in this section and ensure from the terminal that the values are saved.

• Pressing “RESET” will load the default values, press “SAVE” to save the loaded default values.

• Press “BACK” to go to the “MACROSCALE MODELING” main page.

Step 3: Computational Settings: 

“ACCURACY” and “PERFORMANCE” sliders are available to adjust the mesh size. Increasing the ACCURACY slider results in a finer mesh, enhancing precision. Conversely, adjusting the PERFORMANCE slider increases the mesh size, optimizing computational efficiency. Users can fine-tune these settings based on 

their specific computational requirements and balance the trade-off between accuracy and performance.

Data saving frequency:  

The Data Saving Frequency parameter determines the frequency at which result files are saved during simulation. For simulations with extended processing times, saving data at regular intervals can consume significant memory. To alleviate this, users can reduce the Data Saving Frequency, thereby reducing the overall memory usage.

High-performance computing: 

Users have the option to enhance simulation efficiency by running it in parallel. The number of CPU cores to be utilized can be specified. The system's maximum available cores are indicated as "Max available 12," indicating, for example, that the user has 12 CPU cores available, and they may choose to utilize 4 of them for the simulation. This feature optimizes simulation performance, especially for computationally demanding tasks.

• Press the "SAVE" button after configuring the desired computational settings. In the terminal, users will receive feedback displaying the mesh size, Courant number, time step, and a success message indicating that the values have been saved.

Monitoring the terminal provides insight into the simulation's progress. Following the selection of "RUN SOLVER," users will observe the message 'Launching Macroscale Modeling with AM PravaH.' followed by subsequent messages. 

• Press the "SAVE" button after configuring the desired computational settings. In the terminal, users will receive feedback displaying the mesh size, Courant number, time step, and a success message indicating that the values have been saved. 

• If you opt to "RESET," the default computational settings will be loaded. To apply these loaded defaults, press "SAVE."

• To return to the main page of "MACROSCALE MODELING," press "BACK."

Step 4: Running Simulation and Simulation Progress: 

After completing the preceding three steps, users can initiate the simulation by pressing the "RUN SOLVER" button.

Monitoring the terminal provides insight into the simulation's progress. Following the selection of "RUN SOLVER," users will observe the message 'Launching Macroscale Modeling with AM PravaH.' followed by subsequent messages. 

Upon the appearance of the message 'Modeling melt pool dynamics using Multiphase CFD (parallel) ...' in the terminal without any accompanying error messages, it signifies the seamless progression of the Macroscale simulation into the melt pool calculation phase. 

Show progress console: 

This button serves to provide users with a real-time view of the simulation progress. Users have the capability to observe data from the last few time steps. The terminal displays comprehensive information during the simulation, encompassing Storage details, Required time for the simulation, AM Process time, and the percentage of completion.

The total required storage depends on the data saving frequency specified in the computational settings. The Estimated Remaining Time is dynamic, influenced by system performance and time step adjustments made by the solver to maintain control over the Courant number. The solver's automatic enhancement of the time step can lead to improvements in simulation speed, and the estimated clock time required may change accordingly. Typically, the simulation concludes in less time than initially estimated. 

Users can also monitor the residual plot, gaining insights into Courant number and time step improvements. This window remains active for 5 minutes and closes automatically. By pressing the "SHOW PROGRESS CONSOL" button, users can reopen the plots. The green refresh button atop the plot window facilitates the refreshing of plots.

Save and Terminate: 

To terminate the simulation prematurely, the "SAVE AND TERMINATE" button can be utilized. This action not only stops the ongoing simulation but also preserves the results in the project folder. Even after using this button, users retain the ability to visualize and analyze the obtained results.

Step 5: Visualization:

By clicking the "VISUALIZE" button, a Paraview window will be launched, loading the simulation results. Users have the flexibility to leverage all available Paraview tools for effective visualization of the result data.

Step 1: Simulation setup:

Alloy Thermophysical properties: 

Users can choose the alloy of interest from the dropdown menu, where the thermophysical properties of several common alloys are readily available. If necessary, users have the option to edit these properties. It's important to note that these edits won't modify the default properties; however, they will be applied to the current project upon pressing the "SAVE" button. This feature allows users to customize alloy properties for their specific simulation needs.

Upload scan pattern: 

To customize the scan track, users can navigate to the project folder and open the .csv file. The initial column denotes the layer number, the second column signifies the simulation time commencing from zero, and the subsequent three columns represent the x, y, and z coordinates of the laser. The sixth column indicates whether the laser is turned on or off at the corresponding time.

In the default "pattern.csv," the laser is activated from 0 seconds to 0.0002 seconds and promptly deactivated at 0.00021 seconds. It remains inactive until 0.0008 seconds, representing the solidification phase. 

Within the default "multi_layer.csv," a predefined two-layer pattern is available. The numeral '2' in the initial column signifies the second layer. The initiation of the second layer occurs immediately after the completion of the first layer. Specifically, the start time for the second layer is at 0.000801 seconds, just after the conclusion of the last layer at 0.0008 seconds, and the laser is activated.

Users have the flexibility to assign any name to these input files, provided they have the .csv extension. This allows users to craft customized scan patterns tailored to their specific requirements.

Layer thickness: 

Width adjustment parameter:

This parameter controls the width of the melt pool and is inversely proportional to the width of the melt pool.

Depth adjustment parameter: 

The depth adjustment parameter plays a crucial role in determining the depth of the melt pool, and it operates on an inverse relationship; as this parameter increases, the depth of the melt pool decreases.

Metal heat absorption fraction:

In laser-based additive manufacturing processes, a portion of the laser energy is reflected, while the remaining energy is absorbed by the material. This absorption behavior can be quantified using the metal heat absorption fraction. A default value of 0.5 is provided, representing an average absorption fraction for the entire process. It's worth noting that solid and molten metal have different absorptivity values, ranging from 0.4 for liquid to 0.7 for solid. The average value of 0.5 is offered as a default to represent the overall absorption characteristics throughout the process. Users can adjust this parameter to fine-tune the simulation based on specific material properties or process conditions.

Spins: 

In the initial state, a random number of spins are assigned to the material and distributed randomly. Increasing the number of spins enhances randomness, contributing to more realistic outcomes in a probabilistic model. A default value of '125' is provided, and while users can opt for higher values, it's essential to note that beyond a certain point, further increases may not significantly improve results. To optimize simulation time, users should use the minimum required number of spins. It's crucial to keep in mind that the number of spins cannot exceed the total number of lattices in the domain.

Accuracy and Performance:

The Accuracy and Performance slider influences the lattice size. Upon clicking the "SAVE" button, users can view the lattice size in the terminal, expressed in meters. For instance, a lattice size of 4.99e-6 means each lattice is 4.99e-6 meters. A smaller lattice size results in more accurate results, but it comes at the cost of increased simulation time and memory requirements. Users can adjust this parameter to balance the trade-off between accuracy and computational efficiency based on their specific simulation needs.

Data saving frequency:

The Data Saving Frequency parameter determines how often data is saved during the simulation. A higher data saving frequency results in more frequent data saves, consuming more memory. Users have the flexibility to reduce this frequency to conserve memory. On the other hand, larger values can be advantageous for observing the grain evolution process at smaller intervals, providing insights into how grains evolve.

High-performance computing:

Users can enhance simulation efficiency by running it in parallel, specifying the number of CPU cores to be utilized. The system's maximum available cores are indicated as "Max available 12," suggesting, for example, that the user has 12 CPU cores available, and they may choose to utilize 4 of them for the simulation. This parallel computing feature optimizes simulation performance, particularly for computationally intensive tasks.

Step 2: Running Simulation and Simulation Progress: 

After setting the simulation inputs and clicking on the “SAVE” button, users can press the “RUN SOLVER” button to start the simulation.

Users can watch the terminal to get the progress of the simulation. After pressing “RUN SOLVER” users can see the ‘Launching Microscale Modeling with AM PravaH.’ message followed by further messages. 

Show progress console: 

This button is provided to enable the user to see the progress of the simulation in between. 

• Users can see the data of the last few time steps. 

• Detailed information about the running simulation is printed on the terminal which includes Storage information, Required time for the simulation, and percent simulation completed.

• Total required storage depends on the data saving frequency provided in computational settings.

Step 3: Visualization: 

To visualize the simulation results, click on the “VISUALIZE” button, which will open a ParaView window.

Reload the data by right-clicking on 'Microstructure_pravah' and selecting 'Load new files.' This action will load all the available results.

Users have the flexibility to utilize all the available ParaView tools to visualize the result data.

A Deep Learning module is incorporated into AM PravaH to expedite simulations and utilize data from previous simulations. Users can train their own Deep Learning model using relevant target data from both simulations and experiments.

This module consists of two sections: 

1. "TRAIN NEW MODEL" for model training 

2. "USE TRAINED MODEL" for predictions.

Training a New Model: 

After creating a project from the “LAUNCH PROJECT” user can navigate to the “DEEP LEARNING” page.

1. Model Name: Provide a unique name for your model.

2. No of Neurons per Layer: Customize the number of neurons per layer. More neurons create a more complex model. The number of neurons contributes to the model's accuracy; however, the improvement may plateau after reaching an optimum level, as it also depends on the provided data.

3. Upload Input CSV: Upload the input data for the model using the "Upload input CSV" button. Navigate to your project location, go to datasets > NIST, and upload 'train_features.csv' as your training data. 

Ensure that the file is uploaded correctly from the terminal. 

4. Upload Target CSV: Use the "Upload target CSV" button to upload 'train_labels.csv', containing the target data to be predicted by the model after training. Exclude data intended for testing the trained model.

5. Train New AI: Clicking on this button initiates the model training. 

Observe the progress in the terminal, where the message 'Training AI agent' indicates the commencement of model training.

A success message confirms the successful completion of training. 

6. Progress Console: Utilize the "PROGRESS CONSOLE" button to analyze the model training live. This button opens a TensorBoard window in the default browser, providing real-time insights into the training progress.

Use Trained Model: 

Step 1: Load Trained Model: Click on the "Load Trained Model" button to load your model from ai > saved_models > 'Model_name'. Press 'open'. 

Confirm from the terminal that the model is loaded correctly.

Step 2: Load Data for Prediction: Load the data for prediction with the "Load Data for Prediction" button. Sample prediction data is available as "predict_feature.csv" inside datasets > NIST folder. The respective plots will be saved in ai > plots folder. 

Step 3: Predict: 

Click on the "Predict" button to perform predictions for the provided features.