Real-time quality control in tire curing

Challenge

Raw material with varying characteristics complicates process control and end-quality forecasting. Heating, pressure and cycle times were kept static — but could be adjusted dynamically based on raw material.

Solution

Temperature-sensorised tire moulds track heat distribution during curing; comparing sensor data with master temperature data reveals the anomalies that influence quality.

• Sensorised moulds. Spot-based temperature tracking captures heat distribution in the rubber across the mould.
• Correlation modelling. Mould spot-temperature trends correlated to final quality KPIs, and to oven temperature, pressure and cycle time.
• ML uniqueness. Machine learning found previously unknown predictive factors and influenced production in real time.

Results

• Reduced energy cost via lower oven temperatures
• Increased throughput from shorter cycle times
• Reduced scrap through higher quality