Abstract
Background: The reliability and safety of railway tracks are critically important for transportation infrastructure. The transition towards high-speed and heavy-haul operations intensifies dynamic loads on rail welded joints, accelerating fatigue processes and making service life dependent on welding quality. Current quality control methods at rail welding enterprises often rely on reactive pass/fail criteria without advanced statistical tools for proactive process management. Aim: The main objective of this research was to develop and validate an improved methodology for assessing the stability and quality level of the technological process for manufacturing welded rail joints at rail welding enterprises based on statistical analysis of internal defect rejection rates. Methods: The study employed statistical process control methodology using control charts for attribute data. Operational data from multiple rail welding enterprises were analyzed over a representative period, including 100% external inspection and ultrasonic testing results. The methodology included stability assessment at individual enterprises, comparative quality assessment across multiple enterprises, and defect distribution analysis. Results: The control chart system successfully identified process instabilities with statistical significance, enabling corrective interventions 4-6 weeks earlier than traditional inspection methods. The methodology detected >80% of assignable causes that would have been missed by conventional quality control. Defect distribution analysis revealed that lack of fusion and slag inclusions accounted for >60% of total defects, with the rail head being the most critical zone. Discussion: The implementation of statistical process control represents a paradigm shift from reactive defect detection to proactive process control in rail welding quality assurance. The methodology provides enterprises with real-time monitoring capabilities and enables centralized quality oversight across the entire network of welding enterprises. Conclusions: The research demonstrates that integrating statistical quality control into rail welding operations enhances both operational efficiency and railway infrastructure integrity. The proposed methodology is compatible with global quality assurance philosophies and shows potential applicability to other continuous welding processes in heavy industry.