We were honored to have Brent Vorst from Kinetic Vision present at the SIMULIA Americas Users Conference, May 1-2, 2024 in Novi, Michigan.
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Abstract
Kinetic Vision will present a study that demonstrates the effectiveness of a streamlined product development process that integrates computer-aided design (CAD), digital thickness prediction, design of experiments (DOE), 3D printing, and manufacturing line testing. By leveraging these tools, we were able to rapidly design, optimize, and manufacture a new bottle design.
First, a parametric CAD model of the bottle was created in 3DX, allowing for precise control over the dimensions and shape of the product. Next, a digital thickness prediction method was used to generate accurate thickness models for all CAD designs. To identify the most promising designs, a DOE was executed using Process Composer covering a design space of geometry and material properties. The top three bottle designs were selected for further evaluation.
Physical prototypes of each top design were 3D printed with realistic thickness distributions. The 3D printing technology used creates bottles that physically behave similarly to the mass-produced EBM bottles. The 3D printed bottles were physically tested on the manufacturing line to ensure their compatibility with the production process
The integration of CAD, digital thickness prediction, DOE, 3D printing, and manufacturing line testing proved to be an efficient and effective approach to product development. This digital-first method reduced the time to market by roughly 50% and the prototyping cost by 90%.
Presenter Bio
Brent Vorst is a Senior Account Manager at Kinetic Vision, bridging the gap between cutting-edge research and real-world impact. He has a degree in Aerospace Engineering from the University of Cincinnati and started in the Modeling and Simulation group at Kinetic Vision in 2011. Brent has worked on hundreds of models during his tenure at Kinetic Vision. His expertise includes structural and dynamic simulations, thermal modeling, numerical methods and surrogate modeling, process automation, physical testing, and material characterization.
