This offering provides a physics-based, AI-enhanced digital twin for thermoforming processes. It serves industrial manufacturers by reducing trial-and-error, scrap rates, and energy consumption. The solution predicts thermal and mechanical behavior, enabling manufacturers to optimize process parameters and improve product quality before and during production.
Simulates thermal and mechanical behavior of the thermoforming process using first-principles physics models. Provides accurate 3D representation of sheet temperature distribution and sagging at pre-heater, main-heater, and ballooning stages, enabling reliable forming and defect prediction.
When a new product is introduced, the solution provides optimized process parameter sets that significantly shorten production line setup time. This reduces trial-and-error during changeovers, enables faster ramp-up, and ensures stable quality from the first production runs.
Provides real-time visibility into sensor data and maintains historical records of process conditions and production runs. Supports traceability, root-cause analysis, and continuous improvement across thermoforming operations.
Predicts potential forming defects by analyzing thermal and mechanical behavior together with process data. Enables early detection of defect risks and supports proactive parameter adjustments before quality issues occur.
As part of the EU Horizon ZDZW project, the solution was applied to a thermoforming pilot to reduce defects and material waste using physics-based modeling and process monitoring.
In aerospace manufacturing, the solution optimizes thermoforming of interior and non-structural parts by simulating temperature and forming behavior, reducing trial-and-error and supporting consistent quality during production ramp-up.