Optimizing a Manufacturing Pick-And-Place Operation on a Robotic Arm Using a Digital Twin

Abstract

Abstract Industry 4.0 is the ongoing evolution of manufacturing, characterized by implementing new technologies and methods to increase adaptability and efficiency. To realize the potential of Industry 4.0, integrating technological concepts is essential. This research combines process simulation, automation, and existing manufacturing infrastructure to develop a digital twin (DT) and implement it to drive visibility and innovation. This research seeks to optimize a live production system by developing a simulation to model a pick-and-place operation, creating a DT to reflect the real-world system environment. The simulation will allow for modifications to the process or system to better understand the impacts of changes to the operation. The provided definition of a DT outlines a clear set of criteria that must be met for a virtual model to be considered a DT. According to this definition, the creation of a virtual, kinematic replica of a physical model is essential. Connecting this kinematic model to the physical model results in the automation of the kinematic model and the creation of a DT. The discussion also identifies potential opportunities for improving the performance of a physical robot cell through the use of statistical analysis and machine learning algorithms.