A robotic assembly line cannot function without efficient and dependable production diagnostics. To increase the accuracy and efficiency of production diagnostics in a robotic assembly line, we offer a framework that incorporates machine modeling, vibrodiagnostics, energy consumption analysis, and material flow modeling.
Our technique includes vibrodiagnostics of robotic arms and other components to identify possible wear and tear concerns. We also examine the energy usage of robots and conveyors in order to improve energy efficiency and save expenses. Additionally, in the Montrac assembly line, we apply material flow modeling to detect possible bottlenecks and inefficiencies in the production process.
To boost the efficacy even further, we employ machine learning algorithms that evaluate data from the framework’s numerous components. This method will allow for real-time monitoring of manufacturing processes, detection of abnormalities, and forecast of possible problems.