Robotic Cell Reliability Optimization Based on Digital Twin and Predictive Maintenance

Robotic systems have become a standard tool in modern manufacturing due to their unique characteristics, such as repeatability, precision, and speed, among others. One of the main challenges of robotic manipulators is the low degree of reliability. Low reliability increases the probability of disruption in manufacturing processes, minimizing in this way the productivity and by extension the profit of the company. To address the abovementioned challenges, this research work proposes a robotic cell reliability optimization method based on digital twin and predictive maintenance. Concretely, the simulation of the robot is provided, and emphasis is given to the reliability optimization of the robotic cell's critical component. A supervised machine learning model is trained, aiming to detect and classify the faulty behavior of the critical component. Furthermore, a framework is proposed for the remaining useful life prediction with the aim to improve the reliability of the robotic cell. Thus, following the results of the current research work, appropriate maintenance tasks can be applied, preventing the robotic cell from serious failures and ensuring high reliability.

Automated summary

Robotic systems have become essential in modern manufacturing due to their unique characteristics. However, their low reliability poses challenges and affects productivity and profit. To address this issue, this research proposes a method based on digital twin and predictive maintenance to optimize the reliability of a robotic cell. The study includes simulation, machine learning model training for fault detection, and a framework for predicting remaining useful life. Applying appropriate maintenance tasks based on these results can prevent serious failures and ensure high reliability.