Method for evaluating the monetary added value of the usage of a digital twin for additive manufacturing

By combining the additive manufacturing (AM)-process with digital concepts, such as the digital twin (DT) or the digital part file (DPF), the competitiveness of additive manufacturing is increased. A quantitative approach to evaluate the usage of a DPF in AM will be introduced within this paper. The focus is set on the production as an early lifecycle-phase, which means that the AM-production process gets analyzed regarding potential advantages of using a DPF. These advantages are transferred into a monetary value with our approach. By calculating the total costs of the DPF, a monetary overall value is obtained

Digitaler Zwilling für die additive Fertigung

Additive Fertigungsverfahren ermöglichen die Herstellung von sowohl gleichen als auch unterschiedlichen Komponenten in einem Bauvorgang. Um insbesondere für die additive Serienfertigung eine gleichbleibende Bauteilqualität zu gewährleisten, kann der Einsatz eines digitalen Zwillings sinnvoll sein. Dieser stellt hohe Anforderungen an die Datenbasis in Bezug auf Strukturierung, Rechenleistung und Datensicherheit und muss auf die relevanten Funktionen des Einsatzbereichs zugeschnitten sein.Additive manufacturing enables the production of both several identical and different components in a single printing process. To ensure consistent component quality, especially for additive series production, the application of a digital twin can be useful. This places high demands on the data basis in terms of structuring, computing power and data security and must be tailored to the relevant functions of the respective application

Towards robustness of production planning and control against supply chain disruptions

Just-in-time supply chains have become increasingly popular in past decades. However, these are particularly vulnerable when logistic routes are blocked, manufacturing capacities are limited or customs are under strain, as has been seen in the last few years. The principle of just-in-time delivery requires a coordinated production and material flow along the entire supply chain. Challenges in the supply chain can lead to various disruptions, so that certain manufacturing jobs must be changed, postponed or cancelled, which will then impact supply down the line up to the consumer. Nowadays, many planning and control processes in the event of a disturbance are based on the procedural knowledge of employees and undertaken manually by those. The procedures to mitigate the negative effects of disturbances are often quite complex and time-critical, making disturbance management highly challenging. In this paper, we introduce a real-world use case where we automate the currently manual reschedule of a production plan containing unavailable jobs. First, we analyse existing literature regarding the classification of disturbances encountered in similar use cases. We show how we automate existing manual disturbance management and argue that employing stochastic optimization allows us to not only promote future jobs but to on-the-fly create entirely new plans that are optimized regarding throughput, energy consumption, material waste and operator productivity. Building on this routine, we propose to create a Bayesian estimator to determine the probabilities of delivery times whose predictions we can then reintegrate into our optimizer to create less fragile schedules. Overall, the goals of this approach are to increase robustness in production planning and control

Towards Robustness Of Production Planning And Control Against Supply Chain Disruptions

Just-in-time supply chains have become increasingly popular in past decades. However, these are particularly vulnerable when logistic routes are blocked, manufacturing capacities are limited or customs are under strain, as has been seen in the last few years. The principle of just-in-time delivery requires a coordinated production and material flow along the entire supply chain. Challenges in the supply chain can lead to various disruptions, so that certain manufacturing jobs must be changed, postponed or cancelled, which will then impact supply down the line up to the consumer. Nowadays, many planning and control processes in the event of a disturbance are based on the procedural knowledge of employees and undertaken manually by those. The procedures to mitigate the negative effects of disturbances are often quite complex and time-critical, making disturbance management highly challenging. In this paper, we introduce a real-world use case where we automate the currently manual reschedule of a production plan containing unavailable jobs. First, we analyse existing literature regarding the classification of disturbances encountered in similar use cases. We show how we automate existing manual disturbance management and argue that employing stochastic optimization allows us to not only promote future jobs but to on-the-fly create entirely new plans that are optimized regarding throughput, energy consumption, material waste and operator productivity. Building on this routine, we propose to create a Bayesian estimator to determine the probabilities of delivery times whose predictions we can then reintegrate into our optimizer to create less fragile schedules. Overall, the goals of this approach are to increase robustness in production planning and control