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Accelerate New Product Development for Additive Manufacturing

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The Pearl engine is built on a decade of research, working with industry-leading companies. Your build parameters and resulting microstructure are linked to failure.

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A Machine Learning-based Framework to Predict Local Strain Distribution & the Evolution of Plastic Anisotropy & Fracture in Additively Manufactured Alloys

    Presented at the New Mexico Machine Learning Symposium, Machine Learning for Material Applications, April 21, 2021

    Pearl can accelerate your workflow

    • , pearlsimai, VIAS3D

      Step 1

      We train Pearl to connect build parameters to microstructure for your printed part.

    • , pearlsimai, VIAS3D

      Step 2

      We train Pearl to connect microstructure to failure of your printed part.

    • , pearlsimai, VIAS3D

      Step 3

      Iterate among hundreds of build parameter variations in minutes to optimize your additive manufacturing process.

    Save time. Save Money. Build high-quality parts.

    PEARL HAS DEMONSTRATED SUCCESSES WITH SOME OF THE WORLD'S LARGEST AUTOMOTIVE AND MATERIALS COMPANIES, SAVING MILLIONS OF DOLLARS ANNUALLY.

    Where'd we get our name?

    We named our product after our inspiration: Pearl Sullivan, a Professor in the Mechanical and Mechatronics Engineering department at the University of Waterloo. She was the eighth Dean of Engineering at Waterloo and the first woman to hold that post. She both supported and inspired our work in artificial intelligence.

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