Performance analysis of sensors in a mechatronic system for transformer magnetic core lamination cutting within an automated production line

Transformer magnetic core lamination cutting is a critical process in transformer manufacturing, directly affecting product quality and production efficiency. This paper presents an analysis of the sensor systems employed on an automated cutting line for silicon-steel transformer magnetic core laminations. The study identifies the types and roles of sensors used across the line's key subsystems (uncoiler, feeder, cutting station, and stacker) and evaluates their performance relative to industrial requirements. The findings indicate that conventional inductive, optical (e.g., photoelectric), and magnetic sensors, together with rotary encoders, provide the necessary feedback for automation, cutting precision (±0.1 mm), and control. However, they offer limited self-diagnostic capabilities, increasing down-time during troubleshooting. To enhance reliability and enable predictive maintenance, the integration of advanced "smart" sensors with real-time communication (e.g., EtherCAT, IO-Link) is recommended. The results demonstrate how sensor selection and placement influence cutting accuracy and system downtime and suggest improvements such as leveraging servo-drive feedback and IoT-based monitoring to optimize sensor usage. Practical insights are provided for improving automated production lines in accordance with modern industrial and Industry 4.0 trends, aiming to increase operational efficiency, reduce maintenance costs, and ensure high product quality.