An additive manufacturing company digitised its FDM process data and used tailored ML models to optimise properties 60% faster than conventional methods.
An innovative additive manufacturing company sought out Polymerize to embark on the digitalisation of their extensive data, with the primary objective of optimising their process with the help of machine learning. The tailored models were able to predict experiments and optimise the properties 60% faster than conventional methodologies.
The characteristic feature of FDM lies in producing finely detailed products without post-processing intricacies. The crux of this technology is balancing thermo-mechanically stable products with aesthetics, while minimising costs.
The fresh data points from all the iterations and predictions demonstrated a reduction in error and a reduction in project completion time by 55%.
PLACEHOLDER QUOTE. The tailored models predicted our experiments and optimised properties far faster than our conventional approach, and the team trusted the results. Replace with a real customer quote.
See how Polymerize helps materials teams get results like these.
