Research on Optimal Fabrication of High-Quality Ceramic by Process Analysis Technology

We developed a composite recognition method combining hybrid sensor recognition and artificial intelligence image recognition techniques and optimized quality monitoring of the ceramic production process. By integrating deep learning models and image preprocessing techniques, appearance defects (such as cracks and color differences) were detected in ceramic products, and key parameters in the sintering process using temperature, pressure, and gas-sensitive sensors were monitored in real-time. The developed composite recognition method significantly improved the quality control level of ceramic production, reduced production energy consumption and wastes, and improved production efficiency.