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Fast Design Prediction for Casting Manufacturing Using Physics AI

User: "MilanRaval"
Altair Employee
Updated by MilanRaval

Introduction

One of the most common problems affecting the functionality of the casting component is Porosity. Although it is not possible to achieve zero porosity in the die-casting process, with well-planned mold design and process control you can minimize it. Each process has its method for removing/reducing porosity. The most popular methods for controlling porosity are X-rays, cutting the part, and computerized tomography. Porosity defects occur due to Trapped Gas, Insufficient feeding during solidification, Improper Venting, Gate size, etc.

Problem Statement

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Inspire Cast + Physics AI Workflow

Step 1: Gathering historical data of casting simulation performed in the past. Access the h3d files of Filling or Solidification for training purposes. The below image shows the variable used to create various design iterations for the high-pressure die-casting process simulation. 

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Step 2: Create a data set for train and testing inside Physics AI. Train Machine Learning algorithm of Physics AI using historical data from Inspire Cast. Define training variables like depth, width, epochs, and a result type that needs to be trained.

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Step 3: Test untrained data against trained data to see the predictive accuracy of the Machine learning algorithm. Validate the ML model against the solver through MAE (Mean absolute error). That explains the predictive accuracy of Physics AI. 

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Step 4: Import a new design after the satisfactory MAE is achieved. New design import should be similar in terms of mesh and units for accurate predictions.  Meshed FEM solver input files can be used to import and predict. Before predicting the new design, make sure to activate the machine learning model that is already trained. Then predict a new design.

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Now, all new designs can be predicted faster and without running traditional casting simulations.

Summary: Physics AI is a great tool for fast predictions based on design changes. However, the result's accuracy depends upon the user's trained data set. If the data is not correctly trained, new design predictions won't show promises on result accuracy. So, it is better to understand the type of data being used for training purposes. Below is an example of a new design prediction against actual casting simulation results.

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Comments

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    User: "Huyen"
    Altair Community Member

    Can you share historical data of the above model for me to practice?

    User: "MilanRaval"
    Altair Employee
    OP

    Hello Minh,

    Here is the link to some tutorial model available for you to practice. Also the link guides you to peform step by step tutorial.

    https://help.altair.com/hwdesktop/hwx/topics/tutorials/hwx/physicsai_model_train_c.htm

    User: "Huyen"
    Altair Community Member

    Hi Milan,

    I would like to practice using PhysicsAI for Casting. Could you share your historical casting data, similar to what you mentioned above.

    Thank you!

    @MilanRaval, kindly provide the casting dataset with the customer.

    User: "MilanRaval"
    Altair Employee
    OP
    Updated by MilanRaval

    Hi Minh,

    Apologies for late response. I missed out on this. Unfortunately due to large size of the model results, I am not able to upload here. I can send you through secure file transfer.