Applications of reinforced composite materials are getting more and more popular in response to the lightweight and low-energy demands in the manufacturing. In order to predict and control the breakage or failure of an injection-molded plastic part, an accurate simulation on the fiber’s orientations inside is crucial for the following structural analyses. But the fiber model’s parameters would need some fine-tuning before being applied on the real part’s simulation workflow. As a result, it was natural for Moldex3D, a cutting-edge and widely used molding simulation in the industry, and Xnovo Technology, a pioneer in advanced X-ray imaging solutions, to offer together an advanced calibration for fiber parameters based on scan experiments, empowering users to conduct reliable and accurate simulations.
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Advanced X-ray imaging solutions
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“Simulation of fiber orientation is not a new technology and we created a great model for it, but with the input of real-life data it would be even more enhanced”, said Ethan Chiu, R&D Director at CoreTech System, the headquarters of Moldex3D. “The top design and manufacturers are all implementing the digital twin concept so the precision requirement has also upgraded. Every little detail counts.”
“We are proud to support Moldex3D users with our advanced X-ray imaging solutions”, said Erik Lauridsen, CEO at Xnovo Technology. “By building on our proven expertise in 3D imaging and analysis, we are able to provide 3D fiber orientation characterization with unparalleled time-to-result and superior quality.”
This collaboration enables users to conduct simulations of 3D fiber orientation using calibrated fiber parameters, which is particularly valuable for defining the anisotropic mechanical strength and controlling molding shrinkage in fiber-reinforced components. By integrating Xnovo’s 3D fiber orientation characterization capabilities with Moldex3D’s simulations, users can achieve improved accuracy and reliability in their simulation analyses. And with the help of Moldex3D Studio API, computing iterations between the digital and physical data are automated on a large scale.
