Session: 03-12-01: Digital Manufacturing Process Simulation and Validation

Paper Number: 113101

113101 - Optimizing a Manufacturing Pick-And-Place Operation on a Robotic Arm Using a Digital Twin 

Industry 4.0 is the ongoing evolution of manufacturing, characterized by implementing new technologies and methods to increase adaptability and efficiency. Today different corporations are exploring how new industry 4.0 technology trends could be applied to improve the next generation automation system. This research project is working in this direction. To realize the potential of Industry 4.0, integrating technological concepts is essential. This research will combine Digital Twins, process simulation, automation, and machine vision technologies to drive visibility and innovation. This research explores digital twins concepts to reflect the real-world system environment and using advanced algorithms to optimize the motion planning of a Fanuc robotic arm.  Previous research has explored digital twins in products and processes; however, this research is mainly focused on the exploration of digital twins in manufacturing system integration.

This research will demonstrate next generation manufacturing principles around a pick and place design using Fanuc robotics. The key aspects of our exploration yielded the following benefits of applying these concepts to digital twins: automation, predictive maintenance, optimization of production, remote monitoring, and improved safety. Exploring the connection between a physical sensor on the conveyor system and the virtual Tecnomatix environment, node red for SCADA application, and machine learning to diagnose tested operations and conduct predictive analysis can ensure efficiency, collision avoidance, and informed decisions on changes to the pick and place process.  This includes Siemens Product Lifecycle Management tools, the Node-RED open-source platform, Manufacturing Production Systems, and Industrial Internet of Things applications which will also be implemented throughout the product's life cycle.  Connecting the divergent phases of development, while supporting the exchange of information between specialized individuals and a network of field devices.

The digital twin will provide real-time insight into the environment, enabling data-driven decisions to be made that can improve the performance and efficiency of the physical environment.  It allows for modifications to the process which can be accurately simulated before implementation on the system.  Exporting these simulations, yields detailed visualizations for further analysis.  By connecting the digital twin to the physical product, we were able to monitor the performance of the chosen cell of the assembly process in real time, allowing immediate detection and address of any problems as they arose.  The motion planning of the Fanuc robotic arm will be improved by using advanced motion planning algorithms, such as path smoothing and collision avoidance, to reduce cycle times and increase throughput. The goal of this project is to safely facilitate greater quality control and system product recognition, production optimization of assembly routines, and increase the ease of industrial system modifications through the implementation of a digital twin. 

The findings demonstrate that it takes a lot of effort to create a full digital twin connecting all variables between the virtual and physical prototypes.  According to this research scope, we obtained a digital twin in a manufacturing system integration that connects key process variables and improves pick and place operation using a Fanuc robot. The results of this research show that the digital twin allowed for the physical prototype to function correctly and enhanced the reusability of engineering redesign efforts for next generation systems. The novelty of this research lies in the direction of how complex variables and feedback controls can be performed, rather than completing an entire digital twin.

Presenting Author: Fadi Hantouli Kennesaw State University

Presenting Author Biography: Fadi Hantouli is a PhD student at Kennesaw State University, GA, USA, in Interdisciplinary Engineering with a focus on Smart Manufacturing Engineering

Authors:

LaShaundra Perry Kennesaw State University
David A. Guerra-Zubiaga Kennesaw State University
Gershom Richards Georgia Tech Research Institute
Cecil Abidoye Kennesaw State University
Fadi Hantouli Kennesaw State University