Industrial halls consume a large part of the heat energy resources but are only a tiny part of the building infra-structure. Improving heating efficiency in this sector will signif-icantly contribute to reach climate neutrality. Therefore, a digi-tal twin backend architecture is developed using the Asset Ad-ministration Shell framework to generate a digital twin. This has created a new data basis that lays the foundation for a score for the comparability of heating systems in terms of efficiency. In addition, some client services are implemented, such as visu-alizing the data in dashboards and generating alerts. Another service is a planning support application via augmented reality. The first tests in an industrial scenario show that using the dig-ital twin can reduce energy consumption and improve resource efficiency. Due to the containerized architecture of the gener-ated twin, it can be used as a blueprint for other digital twins of heating systems.

Cyber-physical systems in Industrie 4.0 enable the use of high-frequency machine data for the reactive control of assembly systems. In order for manual activities to be integrated into these new, highly dynamic processes and for flexible assembly activities to be optimally supported, automated recording of these activities becomes necessary. By linking activity recognition and process mining, manual assembly processes in assembly can automatically be mapped as a workflow using appropriate sensor technology. This enables improved production planning and control, whereby the individual needs of the employee can be addressed. Companies are thus able to implement the manufacturing of highly individualized products efficiently and attractively for employees in order to meet the challenges of the future competitively.

Asset administration shells constitute a standardized, technical realization of digital twins and enable a unified description of important information within a production site for a safe, continuous and joint planning of intralogistics and manufacturing processes. In this paper, we show how an information and service infrastructure based on asset administration shells can be used to provide online-planners with unified, i.e. manufacturer-independent information facets of and respective control interfaces for involved assets, such as collaborative robots. To this end, we implemented a semi-virtualized production process using important industrial communication standards where the current state of real and simulated robots, as well as products, can be observed and manipulated at run-time and on site by means of an Augmented Reality-based user interface. In such dynamic scenarios, the worker safety needs to be maintained using the advances from the virtual representation of all assets. A technical demonstrator has been developed and discussed with domain experts to prove the underlying concept.

Industrial testbeds are spawning all across Europe, focusing on diverse topics. In our Human-Robot-Collaboration lab, we are researching how the lives of people in production can be made simpler through the use of collaborative robots, but still safe against the backdrop of the necessary changeability. Standardized digital twins based on Asset Administration Shells embedded in a service-oriented architecture are an ideal underlying information technology for this endeavor. In this paper, we describe such an infrastructure and outline its use based on two different use cases. We are convinced that this is not only relevant for testbed operators who want to achieve technical and semantic interoperability of their facilities, but can also serve as a blueprint for the implementation of Industrie 4.0 in small and medium-sized enterprises.

This paper and the accompanying demonstration video showcase an interactive counting aid implemented with PARTAS, our personalizable, Augmented Reality-based worker assistance system. PARTAS combines contour-based instructions with a pick-by-projection approach and is particularly designed to be adaptable for people with different cognitive disabilities.

Commercially available assembly systems for industrial settings do not address the needs and requirements of people with disabilities and sheltered workshops. This is due to the complexity of the system’s setup including hardware, software and application in combination with the workers’ and supervisors’ capabilities. In the case of sheltered workshops, resources for implementing functionalities and accessibility requirements push expenses beyond available resources. In this work, we present a prototype of an intuitive, adaptive and cost-efficient worker assistance system utilizing only one RGB camera, one projector and a small single-board computer. The system implements a combination of projected, contour-based instructions and pick-by-projection functionality with the goal of maximizing the instructions’ affordance. An interdisciplinary team including workers from a sheltered workshop, their caregivers, technicians and a psychologist followed an iterative user-centered design methodology approach based on two personas with different cognitive disability profiles. Finally, we conducted a user study based on eight participants matching the main persona. All participants showed a strong learning rate and performed successfully completely new assembly tasks after a short training phase with the proposed system.

This paper and the accompanying demonstration video show our use case of WALL-ET, a social, cognitive, mobile robot platform with a height-adjustable table unit, which assists workers in warehouses or supermarkets when performing unergonomic tasks. With the help of Augmented Reality-glasses and a camera-based activity detection, the system can infer a worker’s intention of lifting boxes from a specified shelf region.

Functionally described capabilities play an important role in the virtualization of manufacturing and the resource-specific realization with the skill-based approach. The importance of this modeling can be seen in a formalization of production capabilities for a holistic usage in a workflow from product orders towards manufacturing on the shop floor. This paper presents a concept for modeling and usage of functionally described capabilities along this workflow with combined modeling approaches. Therefore, the formalism and the setup of product requirements, functionally described capabilities, Petri Net Plans for creation of resource-specific process sequences, and at least their execution with skills are considered. The application and validation of this concept on a virtualized demonstrator example is shown. The achieved results for a given application from a product order towards its simulated manufacturing are presented.

Manual activities are often hidden deep down in discrete manufacturing processes. To analyze and optimize such processes, process discovery techniques allow the mining of the actual process behavior. Those techniques require the availability of complete event logs representing the execution of manual activities. Related works about collecting such information from sensor data unobtrusively for the worker are rare. Papers either address the sensor-based recognition of activities or focus on the process discovery part using process mining-compatible data sets. This paper builds on previous works to provide a solution on how execution-level information can be extracted from videos in manual assembly. The test bed consists of an assembly workstation equipped with a single RGB camera. A neural network-based real-time object detector delivers the input for an algorithm, which generates trajectories reflecting the movement paths of the worker's hands. Those trajectories are automatically assigned to work steps using hierarchical clustering of similar behavior with dynamic time warping. The system has been evaluated in a task-based study with ten participants in a laboratory under realistic conditions. The generated logs have been loaded into the process mining toolkit ProM to discover the underlying process model and to measure the system's performance using conformance checking.

Digital twins enable better control and provide a better understanding of automated manufacturing environments. However, some parts of production processes are not fully covered, as they are still carried out manually. In this paper, we present an approach for monitoring manual parts of an assembly and commissioning process efficiently. We combine scenery classification with an inventory control system based on high-precision scales. We use a head-mounted mixed-reality display to acquire images of the process context and to provide assistance to the worker. Hence, heterogeneous sensor information is combined to capture human activities and object interactions to create an accurate digital representation of actual process behavior. Additionally, sensor information is selected dynamically based on the context. The system was implemented and tested in a laboratory environment. For production environments, this provides a starting point to close a significant gap regarding digital twins in semi-automated manufacturing processes and enables context-aware worker assistance.

Airport management companies are facing increasing passenger numbers and the pressure to provide high-quality ground services to satisfy the passengers’ needs and expectations. A new kind of information system is required, monitoring passenger figures in real time to estimate the time and location when and where a service must be provided and responsible staff scheduled. We suggest an event processing platform which is built upon an industry-proven technology and aggregates streams of information about passenger frequency occurring in heterogeneous formats and with different frequencies to a key performance indicator reflecting the current state of selected areas at the airport. Following the design research principle, we conducted a requirements analysis and developed a prototypical system which efficiently fuses the data streams and generates alerts to notify staff if passenger satisfaction threatens to drop. The system was evaluated with historical data from one of the 20 largest airports in Europe. The evaluation is based on a simulation and provides evidence that the system has the potential to improve customer satisfaction. In addition, due to the event-based architecture, a generic API allows a smooth integration of new data sources, the parametrization of the event rules aggregating and processing all data, and thus, an intuitive usage by the airport operator. The final tool consists of the event processing module and a dashboard supporting airport managers in keeping track of the important quality attribute passenger satisfaction and scheduling staff in the right spot at the right time.

The rise of Industry 4.0 and the convergence with business process management provide new potential for the automatic gathering of process-related sensor information. In manufacturing, information about human behavior in manual assembly tasks is rare when no interaction with machines is involved. We suggest technologies to automatically detect material picking and placement in the assembly workflow to gather accurate data about human behavior and flexible support of human–process interaction. The detection of material picking is achieved by using background subtraction in combination with scales. For placement detection, two approaches are tested: image classification using convolutional neural networks and object detection using Haar wavelets. The detected fine-grained worker activities are then correlated with a hybrid model of the assembly workflow using the business process model and notation and case management model and notation, enabling the measurement of production time (time per state) and quality (frequency of error) on the shop floor as an entry point for conformance checking and process optimization. The approach has been evaluated in a quantitative case study recording the assembly process 30 times in a laboratory setup within 4 h. Under these conditions, the classification of assembly states using a neural network provides a test accuracy of 99.25% on 38 possible assembly states. Material picking based on background subtraction has been evaluated in an informal user study with six participants performing 16 picks each, providing an accuracy of 99.48%. The suggested method offers a promising approach to easily assess fine-grained timings and error rates of assembly steps which can be used to optimize the corresponding process.

The rise of Industry 4.0 and the convergence with BPM provide new potential for the automatic gathering of process-related sensor information. In manufacturing, information about human behavior in manual assembly tasks is rare when no interaction with machines is involved. We suggest technologies to automatically detect material picking and placement in the assembly workflow to gather accurate data about human behavior. For material picking, we use background subtraction; for placement detection image classification with neural networks is applied. The detected fine-grained worker activities are then correlated to a BPMN model of the assembly workflow, enabling the measurement of production time (time per state) and quality (frequency of error) on the shop floor as an entry point for conformance checking and process optimization. The approach has been evaluated in a quantitative case study recording the assembly process 30 times in a laboratory within 4 h. Under these conditions, the classification of assembly states with a neural network provides a test accuracy of 99.25% on 38 possible assembly states. Material picking based on background subtraction has been evaluated in an informal user study with 6 participants performing 16 picks, each providing an accuracy of 99.48%. The suggested method is promising to easily detect fine-grained steps in manufacturing augmenting and checking the assembly workflow.

Modern manufacturing faces a challenge of integrating data models from various sources/domains which may differ both semantically and technically when particular domain specific data models are designed by different users and stored in different formats. This paper introduces an approach for facilitating the design of domain-specific data models using semantic web technologies. In this approach, all the information required for managing the production (including a description of a product, processes involved in the production, and existing resources and their specifications) is captured in an ontology. The proposed Product, Process, and Resource (PPR) ontology defines fundamental conceptualization of the production that can be easily applied to the arbitrary domain. Application of the PPR ontology is demonstrated in the case of simple truck assembling by means of robots. Capturing the knowledge in the form of ontology provides the advantage of employing supporting tools such as reasoners for consistency checking or query languages for information extraction. The paper demonstrates the utilization of SQWRL for searching resources suitable to manipulate given truck parts on the basis of semantic matching between properties of particular elements.

The analysis of manufacturing processes through process mining requires meaningful log data. Regarding worker activities, this data is either sparse or costly to gather. The primary objective of this paper is the implementation and evaluation of a system that detects, monitors and logs such worker activities and generates meaningful event logs. The system is light-weight regarding its setup and convenient for instrumenting assembly workstations in job shop manufacturing for temporary observations. In a study, twelve participants assembled two different product variants in a laboratory setting. The sensor events were compared to video annotations. The optical detection of grasping material by RGB cameras delivered a Median F-score of 0.83. The RGB+D depth camera delivered only a Median F-score of 0.56 due to occlusion. The implemented activity detection proofs the concept of process elicitation and prepares process mining. In future studies we will optimize the sensor setting and focus on anomaly detection.

The ongoing development of industrial manufacturing towards more individualization and smaller lot sizes opens up a new range of challenges. Not only do the processes in the factories need adaptation, but the workers need more support as well. We showcase a system that is able to address both aspects: an instrumentation of a manual workplace provides direct feedback for planning engineers, while at the same time acquiring data that is helpful for giving the worker feedback. Within this demo we focus on bi-manual picking and assembly processes observed by a lightweight optical recognition system enhanced by ultrasonic sensors, but also give an outlook on other possible modules.

The increasing complexity and flexibility in future production environments leads to huge amounts of data and the difficulty to analyze them in order to react to short-termed events. To tackle this challenge, an electronic performance board and editor was developed that allows users to create dashboards with proper visualizations of production data. This information can be shared among stakeholders and might be displayed on various devices, from personal tablets up to large public screens in the factory hall.

Many commercial mobile applications or "apps" have surfaced in the past few years, such as Polar, Suunto, etc., to assist hobby runners in their personal fitness training. Although they partially consider vital parameters such as heart rate, blood pressure, etc., they do not consider the specific health constraints and requirements of rehabilitation patients. Nevertheless, the hobby scenario is also important for long-term, self-responsible rehabilitation training. As motivation is a key success factor in this phase, personal interests of the user have to be considered. The work presented in this paper tackles this problem context from a design science perspective, and derives a new concept for pervasive mobile assistance in the aforementioned scenarios. The approach covers specific route characteristics, its impact on the user and the user's personal preferences. The paper concludes with a description of an implemented proof-of-concept as a personal health system for self-motivated and self-controlled disease management.

The basic concept of the Internet of Things (IoT), to uniquely identify objects and to create a virtual representation based on technologies of the Internet, can be extended to so called digital object memories (DOMe), by attaching a virtual storage space to each physical object. This allows for collecting all object-related information generated along the life-cycle chain of this object. The research question, how an infrastructure for digital object memories has to be designed is addressed in this article. Primary goal is to identify and develop components and processes of an architecture concept particularly suited to represent, manage, and use digital object memories. In order to leverage acceptance and deployment of this novel technology, the envisioned infrastructure has to include tools for integration of new systems, and for migration with existing systems. Special requirements to object memories result from the heterogeneity of data in so-called open-loop scenarios. On the one hand, they have to be flexible enough to handle different data types. On the other hand, a simple and structured data access is required. Depending on the application scenario, the latter one needs to be complemented with concepts for a rights-and role-based access and version control. We present a framework based on a structuring data model and a set of tools to create new and to migrate existing applications to digital object memories.