A COMPREHENSIVE SAFETY FRAMEWORK FOR MINIMIZING MACHINE-INDUCED ACCIDENTS IN AUTOMATED MANUFACTURING

ARTICLE INFO: Date of Submission: May 17, 2025, Revised: May 25, 2025, Accepted: June 07, 2025, https://doi.org/10.56815/ijmrr.v4.sp1.2025.13-24

Authors

  • S. Rajabishek Scholar, K S Rangasamy College of Technology, Chennai, near Thiruchengode, Tamil Nadu, India.
  • M. Sasikumar Associate Professor, K S Rangasamy College of Technology, Chennai, near Thiruchengode, Tamil Nadu, India.

Abstract

Automated Manufacturing Systems (AMS) have dramatically improved the efficiency, accuracy, and size of industrial production. However beneficial AMS may be, their complicated nature, as well as the intricate relationship with human drivers, results in an alarming increase in machine driven accidents and unconstructive effects such as reduced productivity, safety concerns, and profit loss. This paper presents a holistic approach for safety management which can help reclaim the sole purpose of generating profit. Employing my methodology would eliminate the occurring hazards within manufacturing frameworks. The methodology allows capturing signs of safety risk deviances through continuous real-time monitoring of machines and operational conditions. Automation technologies, along with sophisticated predictive maintenance strategies enable prompt attention relieving looming failings of machinery prone to hazardous occurrences. In addition, dynamic safety protocols ensure the flexibility of responding to changing operating conditions while retaining the effectiveness of operational shifts, and state of the machinery along with production needs. The application of this heuristic approach can be simulated in manufacturing context for analysis through multi-step processes, enhancing not only safety but productivity and reliability on the systems. This heuristic approach explored opens multifaceted opportunities aiding professionals tasked with the responsibility of controlling worker exposure, unproductive idle time, and environmental sustainability.

Keywords:

Automated Manufacturing, Machine Safety, Accident Prevention, Predictive Maintenance, Real-time Monitoring

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