AI-Driven Random Forest Model and the Six Sigma Approach for Enhancing Offset Printing Process and Product Quality

The Six Sigma methodology for quality improvement enabled a high degree of process compliance and enhanced process capability. This research develops a new model for optimizing the offset printing process based on the Six Sigma approach, with the aim of reducing process variability and achieving stable, predictable production outcomes. Special focus was placed on defining Critical Product Characteristics (CPCs) and Critical to Quality (CTQs) points and analysing their impact on process output quality, defined by the sigma level. Based on the research, variability limits of production parameters were defined to ensure consistency and high product quality. The integration of Artificial Intelligence (AI) within the Six Sigma framework allowed for additional automation and model adaptation to changing production conditions. The use of the Random Forest model enabled efficient analysis of critical variability points, prediction of potential deviations, and real-time process adjustment. AI is utilized to improve precision and efficiency in quality management, which further enhances process stability and optimization in line with the dynamic demands of modern production. The proposed model represents an innovative approach that facilitates maintaining stable production results and provides a sustainable foundation for future process optimizations in the printing industry.