SIMCON’s new Cadmould AI Solver delivers injection molding simulation results in seconds. By removing lengthy computation times, the tool enables engineers to dynamically explore thousands of design and process variations in a single day.
SIMCON announced the launch of the Cadmould AI Solver, the world’s first Large Engineering Model for injection moulding. Co-developed with Emmi AI, the new transformer-based architecture delivers simulation results up to 1000 times faster than traditional numerical solvers.
Historically, lengthy computation times have acted as a bottleneck, limiting the number of design variations engineers can practically explore during the development process.
The Cadmould AI Solver shatters this barrier by providing engineers with instant feedback on filling pattern, pressure, and temperature in seconds. What once required hours per simulation now takes seconds, enabling engineers to explore orders of magnitude more design alternatives during early development.
Crucially, the new AI technology is designed to complement, not replace, traditional numerical solvers. In this new paradigm, engineers use the Cadmould AI Solver in the early stages to rapidly explore the solution space and narrow down the optimal process window.
Once the ideal design is identified, the classical numerical solver within Cadmould Flex is used to perform the ultimate, highest-precision validation required for final manufacturing sign-off.
“For decades, the industry has accepted that high-fidelity simulation requires hours of computation,” said Bastiaan Oud, CEO at SIMCON.
“By introducing the Cadmould AI Solver, we are giving engineers a high-speed compass to use during the iterative design phase, while Cadmould Flex remains the definitive map for final validation. Together, they create an end-to-end workflow that is both incredibly fast and reliable.”
Built on a transformer-based neural network architecture and trained on hundreds of terabytes of data, the AI Solver demonstrates true geometric generalization. It accurately predicts complex physical behaviours and filling patterns for new, unseen part topologies without requiring model retraining.
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