Checking and ensuring product quality is an important element in many B2B scenarios. However, the process of quality management is often not yet digitalized and therefore usually comes with large efforts. This is especially true in food productions industry. This paper presents a model based approach for a digitalized quality audit in production of food beverages. The proposed method used the Asset Administration Shell (AAS) in combination with Business Process Model and Notation (BPMN) to provide an open interface for machine operators to conduct quality audits. The concept follows a model based approach, where every part can be processed on different IT infrastructure depending on the demanded requirements for data sovereignty and security.

In order to utilize the full potential of an assembly assistant, it is necessary for the system to be able to adapt to the worker’s individual needs and capabilities. The required amount of necessary assistance changes during the assembly work because the worker memorizes the steps and learns the task and might be bothered by unnecessary assistance. Finding the point in time where the learning phase of the worker is finished is therefore an important aspect. In this work, we propose and evaluate a novel method to identify the end of the worker’s learning phase. The method makes use of learning curve models and curve fitting in order to determine the progress of the worker.

In production environments, manual assembly is often used, when there is a high demand for flexibility which needs to be met economically. In this circumstance, assembly assistance systems are often used to ensure production standards. However, the individual requirements of each worker call for a way for the system to adapt towards the workers needs. This often requires modelling and configuration effort to include expert knowledge into an assistance system. This hinders an economical operation in an industrial environment. In this paper, an approach is presented, facilitating methods of online modelling, to generate a model, which represents the workers behavior during an assembly process. This behavior model is then used to deduce suitable adaptive assembly assistance in real time.

Many production processes are still realized with manual assembly, due to its flexibility and efficiency. The individual needs of the customers lead to a bigger variety of products and components in manual assembly workstations. With the variety of the components and products, the scope of the manual assembly workstation as well as its overall complexity increases. In this paper, we present a method to counter this effect by dividing the manual workstation into multiple subsystems. Our method minimizes the number of times a worker has to switch between the individual subsystems in order to search for components. Experimental results indicate that our method performs better on this task compared to other approaches and can this way be used to increase the productivity of a manual assembly workstation.