Design and Implementation of Universal Cyber-Physical Model for Testing Logistic Control Algorithms of Production Line’s Digital Twin by Using Color Sensor

Abstract

This paper deals with the design and implementation of a universal cyber-physical model capable of simulating any production process in order to optimize its logistics systems. The basic idea is the direct possibility of testing and debugging advanced logistics algorithms using a digital twin outside the production line. Since the digital twin requires a physical connection to a real line for its operation, this connection is substituted by a modular cyber-physical system (CPS), which replicates the same physical inputs and outputs as a real production line. Especially in fully functional production facilities, there is a trend towards optimizing logistics systems in order to increase efficiency and reduce idle time. Virtualization techniques in the form of a digital twin are standardly used for this purpose. The possibility of an initial test of the physical implementation of proposed optimization changes before they are fully implemented into operation is a pragmatic question that still resonates on the production side. Such concerns are justified because the proposed changes in the optimization of production logistics based on simulations from a digital twin tend to be initially costly and affect the existing functional production infrastructure. Therefore, we created a universal CPS based on requirements from our cooperating manufacturing companies. The model fully physically reproduces the real conditions of simulated production and verifies in advance the quality of proposed optimization changes virtually by the digital twin. Optimization costs are also significantly reduced, as it is not necessary to verify the optimization impact directly in production, but only in the physical model. To demonstrate the versatility of deployment, we chose a configuration simulating a robotic assembly workplace and its logistics.

Digital Twin for a Multifunctional Technology of Flexible Assembly on a Mechatronics Line with Integrated Robotic Systems and Mobile Visual Sensor—Challenges towards Industry 5.0

“…In a digital twin, there are several solutions for digital transformations in manufacturing technologies [ 19 ]: Additive manufacturing: Additive manufacturing, also known as 3D printing, is a process used to create a physical or 3D object by layering materials one by one based on a digital model [ 20 ]; Autodesk software: Advanced manufacturing software allows you to make anything you want; Augmented reality: Augmented reality, virtual reality, and mixed reality involve immersive technologies to revolutionize data interaction and project collaboration between team members and how people interact with their data; Digital transformation: Digital transformation means convergence for connecting organizations using data, while bridging the gap to bring together different disciplines, such as computer aided design (CAD), computer aided manufacturing (CAM), or computer aided engineering (CAE), collaboration by accessing data via the cloud, for connecting your entire manufacturing ecosystem and automation for removing the delays, and using generative design and robotics to streamline multiple processes and accelerate product development; Generative design: Generative design quickly generates high-performing design alternatives and multiple solutions to solve the needs; Robotics: Robot programming software for manufacturing has a great impact on the collaboration between humans and robots [ 21 , 22 ]; Simulation: Simulation software allows predicting, validating, and optimizing products using accurate analysis. …”

State of the Art and Future Directions of Digital Twins for Production Logistics: A Systematic Literature Review

“…At this stage, it has been demonstrated that the VM accurately reflects the physical unit. The DT in this work is limited to presenting a concept for linking the physical entity and VMs, and, thus, no optimizations are presented [34]. A DT for the final stage of a truck assembly line also shows a successful linkage between the physical entity and the VM.…”

Simulation of Virtual Redundant Sensor Models for Safety-Related Applications

“…Such a system control approach is called the fault-tolerant control (FTC) approach [5,6]. Sensors are increasingly found in a wide range of applications, such as intelligent mobile devices, traffic management, automotive systems, industry [7][8][9], healthcare, environmental monitoring, health monitoring, and the like.…”

“…In this case, the values from sensors PS1, PS2 show smaller deviation (0.39) than defined limit TR (1.0), however the differences k 2 , k 3 between the values of the sensors PS2, PS3 (1.2) and PS1, PS3 (1.59) exceed the defined limit (1.0). Therefore, according to (7), (9), and (11), defined conditions are not met and the overall result has to lead to the negative validation (NOK). The value of the virtual sensor is set to zero (VS1 = 0), as shown by Figure 14 and the virtual sensor cannot provide the validated result.…”