Our Product Quality AI application reduces defect rates and variance from target specifications, resulting in less rework and wasted materials, and minimizing the total cost of quality for your operation.
Ensemble machine learning techniques predict product quality issues, indicate the major drivers of those issues, and recommend process and equipment parameters to improve product quality.
AI-driven analysis of abnormal process parameters helps uncover the most addressable drivers of quality to improve quality precision, reliably and affordably.
Predict quality issues and process anomalies based on high-accuracy plant simulation models trained on your plants' sensor data.
Plant simulation models provide recommended parameter and PDI settings to optimize quality distributions and minimize process anomalies.