F. Hermann, B. Chen, G. Ghasemi, V. Stegmaier, T. Ackermann, P. Reimann, S. Vogt, T. Graf, and M. Weyrich, “A Digital Twin Approach for the Prediction of the Geometry of Single Tracks Produced by Laser Metal Deposition,” in 55th CIRP Conference on Manufacturing Systems, Lugano, Switzerland, Juni 2022, 2022.
Abstract
A key performance indicator for sensors in production systems is their accuracy. In order to respond to the flexible requirements posed to production systems, there are currently two possibilities to realize the needed accuracy. Either the sensors can be changed frequently, so that the lowest still sufficient accuracy can always be achieved or the production system can be equipped directly with highly accurate sensors. These two options come with high costs, originating from the manual effort and the proportionality of accuracy and cost for most of the commonly used sensors. Industrially used resistance-based sensors such as pressure, force or temperature sensors represent a special case here, because their inaccuracy traces back to few major sources. To eliminate some of these sources or to decrease their impacts, instance-specific characteristics are used in an accuracy model provided by an intelligent Digital Twin. Using varying accuracy models provides different levels of accuracy. The presented concept provides the needed accuracy permanently or as a service for periods the production system needs the higher accuracy, called Accuracy-as-a-Service (AaaS), benefiting setup efficiency, costs and flexibility. To validate the presented concept, the proposed model is used for an analog pressure sensor in a fluidic test setup. The control unit of the system is provided with a standard model as well as a more accurate one. It is demonstrated in the paper how the usage of the individual model can improve the accuracy of a special sensor significantly by a factor of four.BibTeX
V. Stegmaier, G. Ghasemi, N. Jazdi, and M. Weyrich, “An approach enabling Accuracy-as-a-Service for resistance-based sensors using intelligent Digital Twins,” in 55th CIRP Conference on Manufacturing Systems, Lugano, Switzerland, Juni 2022, 2022.
Abstract
A key performance indicator for sensors in production systems is their accuracy. In order to respond to the flexible requirements posed to production systems, there are currently two possibilities to realize the needed accuracy. Either the sensors can be changed frequently, so that the lowest still sufficient accuracy can always be achieved or the production system can be equipped directly with highly accurate sensors. These two options come with high costs, originating from the manual effort and the proportionality of accuracy and cost for most of the commonly used sensors. Industrially used resistance-based sensors such as pressure, force or temperature sensors represent a special case here, because their inaccuracy traces back to few major sources. To eliminate some of these sources or to decrease their impacts, instance-specific characteristics are used in an accuracy model provided by an intelligent Digital Twin. Using varying accuracy models provides different levels of accuracy. The presented concept provides the needed accuracy permanently or as a service for periods the production system needs the higher accuracy, called Accuracy-as-a-Service (AaaS), benefiting setup efficiency, costs and flexibility. To validate the presented concept, the proposed model is used for an analog pressure sensor in a fluidic test setup. The control unit of the system is provided with a standard model as well as a more accurate one. It is demonstrated in the paper how the usage of the individual model can improve the accuracy of a special sensor significantly by a factor of four.BibTeX
M. Müller, G. Ghasemi, N. Jazdi, and M. Weyrich, “Situational Risk Assessment Design for Autonomous Mobile Robots,” 32nd CIRP Design Conference 2022, Procedia CIRP, Vol. 109, pp. 72-77, 21. Juni 2022, 2022.
Abstract
The emerging autonomous mobile robots promise a new level of efficiency and flexibility. However, because these types of systems operate in the same space as humans, mobile robots must cope with dynamic changes and heterogeneously structured environments. To ensure safety, new approaches are needed that model risk at runtime. This risk depends on the situation and is therefore a situational risk. In this paper, we propose a new methodology to model situational risk based on multi-agent adversarial reinforcement learning. In this methodology, two competing groups of reinforcement learning agents, namely the protagonists and the adversaries, fight against each other in the simulation. The adversaries represent the disruptive and destabilizing factors, while the protagonists try to compensate for them. The situational risk is then derived from the outcome of the simulated struggle. At this point, the system’s Digital Twin provides up-to-date and relevant models for simulation and synchronizes the simulation with the real asset. Our risk modeling differentiates the four steps of intelligent information processing: sense, analyze, process, and execute. To find the appropriate adversaries and actors for each of these steps, this methodology builds on Systems Theoretic Process Analysis (STPA). Using STPA, we identify critical signals that lead to losses when a disturbance under certain conditions or in certain situations occurs. At this point, the challenge of managing the complexity arises. We face this issue using training effort as a metric to evaluate it. Through statistical analysis of the identified signals, we derive a procedure for defining action spaces and rewards for the agents in question. We validate the methodology using the example of a Robotino 3 Premium from Festo, an autonomous mobile robot.BibTeX
