Degree Type

Creative Component

Semester of Graduation

Spring 2021


Industrial and Manufacturing Systems Engineering

First Major Professor

Dr. Richard Stone


Master of Science (MS)


Industrial Engineering


In the current manufacturing era of advanced automation, cobots, and robots play an integral role in any manufacturing operation. Collaborative robots can share a workspace with humans to carry out day to day operations with safety. Although there are a lot of studies in the field of industrial-size robots and cobots and their programs or algorithms to make them safer, faster, efficient, the number of OSHA reported accidents due to cobots and robots has not decreased. Even though these cobots are considered inherently safe, they open more probability for accidents because they are not caged. Therefore, it is necessary for the manufacturing industries using cobots to consider the risk involved in human cobot interaction and the ways to attain safety and lower the risk of injury before installing cobots on assembly lines. This study has developed a lightweight cobot and a user-centric tool to perform a physical and psychological risk assessment using process- failure mode effect analysis (PFMEA) for different automation levels in human cobot interaction. A detailed analysis involving stratification of potential failure modes, their types, causes and effects are discussed, and the failure modes or safety incidents are then ranked based on severity, occurrence and detection. The study also tries to correlate the respiratory rate or heart rate and stress involved in human cobot interaction to improve fluency in assembly operation. The developed assessment tool generates a quantitative as well as qualitative assessment consisting of RPN and CN scores. It suggests recommended actions and various CAPA options to curtail physical injury. The tool also offers different training modules for the operators based on their perception of safety and the stress level involved in the assembly operation to reduce behavioral risks. Thus, the generated results provide insights about safety analysis that can be used by manufacturers to improve safe human cobot interaction.

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