Predict material behavior. Control production risk. Scale reliable performance.
Electronic and e-mobility components must combine conductivity, insulation, thermal management, mechanical stability, and compact geometry in a single structure.
Lithography-based additive manufacturing enables high-resolution, complex components, but multi-material printing introduces major process challenges.
Glass-ceramic and copper respond differently to printing, debinding, thermal cycling, and sintering; mismatched expansion, shrinkage, rheology, and material behavior can cause cracks, delamination, weak interfaces, dimensional errors, or inconsistent properties.
During thermal cycles, local temperature differences and material interactions can further affect overall component quality.
Developed within the CoMMCer EUROSTARS project, Simularge’s thermo-mechanical digital twin brings predictive intelligence to multi-material ceramic additive manufacturing.
By combining furnace and part-level physics models with real-time data, it predicts thermal behavior, material response, stress, shrinkage, and deformation before defects occur.
The result is faster process optimization, lower production risk, and more reliable component performance.
The digital twin can support:
Real-time prediction of furnace temperature distribution
Thermo-mechanical analysis of multi-material printed components
Crack and delamination risk prediction
Residual stress and deformation estimation
Debinding and sintering cycle optimization
Adaptive adjustment of process parameters using sensor data
Faster evaluation of new material and geometry combinations
The CoMMCer project targets the following measurable operational improvements:
5–10% Lower Raw Material Consumption
Optimized multi-material production can reduce material use while limiting failed builds and excess consumption.
Up to 15% Lower Energy Consumption
Thermal-cycle optimization can reduce the energy required during debinding and sintering.
70–90% Less Material Waste
LCM production can generate substantially less waste than machining, while unused suspension can potentially be reused.
More Than 50% Less Trial and Error
Digital-twin-supported process development can reduce physical experimentation and support decisions with predictive data.
Contact us to discover how Simularge can bring real-time, physics-based process intelligence to multi-material additive manufacturing and support more reliable production of electronic components.
References:
SIMULARGE
Unlocked Hidden Data from Legacy Injection Molding Machines
See how Simularge gave legacy injection molding machines live visibility in under 2 weeks, with no hardware changes and 99% accuracy.
BEKO
How Beko saved 2M Euro every year in Thermoforming Digital Twin
See how Beko used Simularge's digital twin to reach 96% thermal accuracy, improve OEE by 20% and save €2M annually.
Digital Twin for Composite Autoclave Manufacturing
Improve composite autoclave curing with a physics-based digital twin for shorter cycles, fewer defects, and consistent part quality.
Ready to get started ?
If this challenge sounds familiar, let’s discuss how Simularge’s physics-based digital twin technology can be applied to your production environment.
