What We Did:
We analyzed a set of thermocouple readings from two distinct areas of our RTM-6 material embedded with carbon fiber. This deep dive allowed us to understand the heating and cooling cycles intimately.Using our ML/Ai framework we demonstrated very accurate predictions in near real-time.
Key Insights:
- The upper and lower parts of the material exhibit different thermal behaviors. This understanding can help in optimizing the curing process tailored to each section.
- By leveraging a predictive model, we gauged the cure time and overshoot temperature resulting in a strong validation with real-world data, reinforcing the model’s reliability.
Why This Matters :
- Efficiency: Accurate predictions can reduce waste, save time, and ensure the product’s structural integrity.
- Cost-Effective: Minimizing errors and refining the curing process can lead to significant cost savings.
- Quality Assurance: Consistent and accurate curing means a high-quality end product, every single time.
Validation with Real Data:
Our partners and collaborators greatly enchanced our validation process with real data , showcasing the model’s robustness and reliability. We now plan for an even bigger run with a more diverse dataset. We need to highlight that these results are in almost sub-second time .