AI-Assisted Quality Prediction and Quality Control of Chemical Mixtures in Polymer Manufacturing

Synopsis

Traditional quality control, based on manual inspections and sampling, often faces inefficiencies, delays in defect detection, and process complexities. The AI-supported method we have developed introduces real-time monitoring, predictive quality control, and automatic defect detection, allowing for immediate corrective actions and process optimization.
By integrating data from sensors typically present in production machinery, ERP systems, and laboratory analysis, AI establishes a relationship between the ongoing mixture in the production process and previously produced mixtures for which quality control results are known. The main advantages include increased sustainability, cost savings, enhanced safety, and continuous improvement.
Specific challenges in the formulation of dispersions (such as raw material variability, dispersion stability, consistency of rheological properties, etc.) and in polymer production (such as particle distribution, molecular weight control, and reaction parameter management) are addressed with AI-driven solutions, demonstrating its potential to revolutionize quality control in the chemical industry.