Industrial accidents involving cutting machines are a significant issue, with hand injuries being the second most common type of accident. According to the Bureau of Labor Statistics, over 1 million workers require emergency treatment annually, with 110,000 cases leading to lost work time. One of the primary reasons for these incidents is the lack of adequate training and access control, resulting in unauthorized or improper machine usage.

This study presents the development of an intelligent safety system designed to mitigate these risks through biometric authentication via fingerprint validation. The system ensures that only authorized and trained personnel can operate industrial cutting machines. Additionally, it records essential data such as user identity, usage duration, and access times, enabling enhanced monitoring and operational efficiency.

The proposed prototype integrates seamlessly with existing monitoring systems, offering a user-friendly interface that enhances accessibility and reliability. The research methodology involved defining market requirements, designing a functional model using a prototyping approach, and implementing biometric technologies with microcontrollers like Arduino.

Testing was conducted in a controlled environment to validate the system’s functionality, assess its reliability, and optimize its performance. The results indicate a significant reduction in unauthorized machine access, improved safety measures, and enhanced compliance with industrial regulations. This study highlights the potential of biometric authentication systems in industrial safety, providing a scalable and innovative solution for workplace risk management.