Artificial Intelligence

The digital transformation led to disruptive changes in business models of leading companies. Big Data serves as one of the key enables in this area. The transfer of this concept in the production domain towards an Industrial Big Data is key challenge for producing companies. Although exemplary key projects exist, no available characterization of structural elements in Industrial Big Data Processes exists. Therefore, this article aims at presenting initial structural elements of Industrial Big Data projects based on exemplary use cases.

Intensified networking and digitalization of systems affect an increasing number of sectors. At the same time a great variety of different concepts, ideas, expectations as well as fears have emerged around Industry 4.0. A look into the newspapers is enough to understand that the profound connection of critical structures does also hold profound dangers. For the future it is crucial to consider a way of using the new mass of data and information to protect these structures. Evaluating big data and transforming it into smart data with support of Artificial Intelligence will be a significant security factor in the future.

Optical systems are a popular choice for quality inspection because they are not only contactless and precise but also comparably fast. Particularly in cases where a 100% quality inspection is required low measurement and evaluation time is of essential importance. With high production rates of several parts per second, manual inspection is not feasible anymore and the evaluation needs to be automatically carried out by algorithms. In this article we propose a fast method for anomaly detection in image data based on principal component analysis and filtering. The method shows competitive performance on a data set of challenging surface inspection.

One of the implications of Industry 4.0 is the emergence of a new collaboration between humans, robots and virtual agents as teams - robots are no competitors, but typically take over tasks that are time-consuming, harmful or even extremely dangerous for humans. These Hybrid Teams must communicate efficiently, should be flexible and broadly applicable. How can one implement such a team and what has to be considered? The article describes the organization and properties of Hybrid Teams and proposes a system architecture that is based on experiences from the ongoing research project HyScoiaTea (FKZ 01IW14001, BMBF).

In contrast to the use of robots in standardized production processes, robots must be flexible and adaptable within dynamic logistics processes in order to cope with variable environmental conditions and non-standardized goods. Due to the recent advances in the field of artificial intelligence and networking through industry 4.0, robots will perform complex tasks in logistics in a reliable way in future. A crucial component of a robot system represents the interpretation of the work environment with the help of multi-modal sensor systems, especially image processing systems. This paper describes applications for robotic systems in logistics as well as a concrete example of focusing on the interpretation of multi-modal sensor data for the automation of a logistics task.

Global strategies for reduction of CO2-emissions and energy consumptions force large production facilities to reconsider rail transport as valid alternative for inbound in car manufacturing and for other time critical logistics. Both are demanding in time precision and volume. A disruptive approach using also CPS (cyber physical systems) is applied to introduce terminals with a direct access to motor ways and to rail infrastructures by autonomous systems as core in a new type of terminal superstructures. This brings about a leap frog improvement regarding dynamic and flexibility in response to factories demands’. A scenario is presented and promising qualitative key results are reported. The results have the potential to lay the foundation for further activities in planning and erecting a first terminal and a high volume and high quality multi model transport network in Europe within the context of TEN-T Urban Nodes developments.

Dieser Beitrag beschreibt das Forschungsge-biet Cognitive Computing. Als Beispiel wird das Gebiet der Beantwortung natürlichsprachlicher Fragen eingeführt und die besonderen Herausforderungen dieses Gebiets aufgezeigt. Eine Möglichkeit, diese Herausforderungen zu meistern, wird durch die detaillierte Präsentation des LogAnswer-Systems vorgestellt, das ein erfolgreiches System im Bereich der Beantwortung von natürlichsprachlichen Fragen ist.

To manage the increasing complexity in current hardware design processes, current systems are increasingly designed on abstract layers. While the more rapid development of prototypes is a clear advantage of this paradigm, these designs suffer from being closed up and hard to analyze. There is no simple way to extract a system’s structure from its description anymore. Nevertheless, the designers should get all the information they need during development. The computer is assisting in this process with the observation of its inner self: The simulated hardware is supervised by an artificial intelligence (AI). It learns about a system’s functions while the system itself is running. Dependencies and connections inside this system are retrieved independent from their availability, thus speeding up the development process.

Progress in the areas of 3D sensor systems and artificial intelligence provides new opportunities for the development of flexible robotic systems that are applicable in scenarios without predefined and constant environmental conditions or standardized processes. An example from the field of logistics is the automatic unloading of containers. The development of a suitable robotic system on the one hand requires applicable gripping technologies, on the other hand it requires a reliable object recognition method in order to recognize and localize differently shaped logistic goods within a packaging scenario. This paper presents an object recognition method for logistic goods from three different shape classes using point clouds acquired by a laser scanner. The method is evaluated with real packaging scenarios.

As a result of continuously increasing economic constraints for the producing sector in Germany due to competitors from low-wage countries, production paradigms are changing substantially. This paradigm shift is characterized by rapidly advancing automation processes. Hence, highly complex Cyber-Physical Production Systems (CPPS) are developed and put into practice. In combination with decreasing numbers of staff and the resulting loss of knowledge, this leads to a deficit of competence required to handle the increasing complexity of CPPS. As a result, a need for innovative assistance systems arises for the support of the remaining employees. The given article describes the challenges and problems and drafts a potential technical solution as well as challenges concerning organization and staff.