This work-in-progress presents current research results on the development of an IIoT technology platform for integrating innovative manufacturing technologies into regional value creation networks, with the current phase focusing on fine grinding technologies (KombiFin). The platform addresses key barriers to utilizing advanced technologies, including complexity, skills shortage and system reliability, through the integration of Asset Administration Shell (AAS) and intelligent assistance systems. The platform employs an open-source AAS middleware framework with cognitive assistance functions and is designed to enable systematic modeling of complex manufacturing processes and expert knowledge. Current validation in precision machining processes demonstrates the theoretical and analytical potential for successfully integrating digital twins with time process control, automated parameter optimization, and cross-system interoperability. The standardized AAS-based architecture enables unlimited scalability, supporting any number of CNC machines (Computerized Numerical Control) and manufacturing cells through distributed processing capabilities. This research contributes to developing regional value creation networks by providing a unified technology platform that bridges the gap between advanced manufacturing capabilities.

In this paper, we present image-based methods for robust recognition of static and dynamic hand gestures in real-time. These methods are used for an intuitive interaction with an assistance-system in which the skin-tones are used to segment the hands. The segmentation builds the basis of feature extraction for the static and dynamic gestures. In the static gestures, the activation of particular region leads us to associated actions whereas HMM classifier is used to extract the dynamic gestures dependent upon the flow. The assistance-system supports the workers in manual working tasks in the context of assembling complex products. This paper is focused on the interaction of the user with this system and describes the work in progress with the initial results from an application scenario.

The potential of Augmented Reality (AR) to support industrial processes has been demonstrated in several studies. While there have been first investigations on user related issues in the long-duration use of mobile AR systems, to date the impact of theses systems on physiological and psychological aspects is not explored extensively. We conducted an extended study in which 19 participants worked 4 hours continuously in an order picking process with and without AR support. Results of the study comparing strain and work efficiency are presented and open issues are discussed.

Augmented Reality (AR) uses computer-generated virtual information to enhance the user’s information access. While numerous previous studies have demonstrated the large potential of AR to improve industrial processes by enhancing product quality and reducing production times it is still unclear if and how long term usage of such AR technology produces stress and strain. This paper presents an approach to use the analysis of Heart Rate Variability to objectively measure current user strain during different work tasks. Results of a user study comparing strain during an AR supported and a non-AR supported work task in a laboratory setting are presented and discussed.

In this paper the block-based motion analysis in monocular RGB-image sequences is considered. The motion analysis is realized using a combined matching criterion, that represents a robust similarity measure for image blocks in successive images. This criterion is calculated as a weighted combination of the 3 color-specific matching criteria. The weight of each of these channel-specific criteria is determined based on their reliability. The combined criterion is used to measure translational and rotational motion between 2 successive images. Assuming these measuring values, a model based recursive estimation technique (Kalman filter) is applied to estimate the position and the velocity of the respective region. In order to cope with problematic image situations (e.g. partial occlusion) an extension of the conventional filter by a recognition system is proposed. This system recognizes typical distorted matching criteria and controls the adaptation of the filter.

In this paper the utilization of artificial neural networks (ANN) for motion estimation is considered. By means of simple neural structures it is possible to improve the reliability and accuracy of block matching algorithms (BMA) by a postprocessing of the similarity criterion. An associative memory realizes an adaptive choice of these filtering structures depending on the image contents. The fundamental idea and some results will be described. The performance capability of the proposed method is shown for selected two-dimensional measuring situations which are not solvable with conventional BMA.