This paper presents the results of a practical com-parative analysis of the LoRa modulation being used in different license-free sub-gigahertz radio frequency bands to realize local communication infrastructures inside forests. In particular, LoRa modulation is used in the European ISM (industrial, scientific and medical) bands at 169,433 and 868 MHz. Using comparable modulation parameters for the different frequency bands, the achievable range in the forest is compared. Since mobile networks are often not available in forests or rural areas, the setup of local wireless networks becomes inevitable when data infrastructures are required. A promising technology for such Low Power Wide Area Networks (LPWANs) is LoRa. The aim is to investigate if the de-facto standard frequency of 868 MHz in Europe is the ideal choice for LoRa communication inside forests. The results of this study can be consulted when setting up data infrastructures inside forests, for instance, for the digitization of forestry, for environmental monitoring networks or to build rescue chains for forest workers. Based on the measurements, it could be shown that using a lower frequency of 169 MHz is far superior to the higher and more common frequencies available for license-free wireless communication with LoRa inside forests.

Uniform semantic data representation along the value chain is important for correct data understanding and complex data processing based on it. Standardized information models (Companion Specifications, CS) in OPC Unified Architecture (OPC UA), address this problem in the area of industrial communication. However, control system manufacturers who enable an OPC UA connection of their products do not always allow individualized data modelling according to the process-relevant CS. Therefore, this paper presents a methodology for the integration of standardized information models into existing OPC UA servers, which should be used as a basis for further data processing mechanisms. The methodology describes the steps of the integration process and focuses in particular on mapping possibilities of the original models to those created according to CS.

This paper reports on a strategy to model a fully automated planning system for a production platform, exemplified by the iCellFactory, an automated cell production platform. First, we analyze the prerequisites, based on a PDDL-based planning system. Then we describe the structure and development of the data model in the web ontology language and the implementation of our data server to store the model. Finally, we evaluate the data model based on the requirements, draw conclusions about the effectiveness of our approach and give an outlook on further development.

Continual learning (CL) is a machine learning (ML) paradigm for learning continually from non-stationary data streams while simultaneously transferring and protecting past knowledge. Therefore, CL avoids catastrophic forgetting, a common problem that arises when training ML-models on new data. This paper presents a CL framework for data-driven learning of the dynamics model of a 6-degree-of-freedom serial industrial robot. This model can be used for model-based control algorithms, without the need for extensive identification of robot specific parameters such as mass inertia, and can additionally model complex effects such as friction. Furthermore, using CL, it can adapt to changes of the robot e.g., due to wear or new tasks. With the help of CL, the ML-based dynamics model is continually fed new data and improves over the operating period of the robot.

With the advent of collaborative robots, the demand for manipulators is constantly increasing, especially in SMEs. Hence, the risk of collisions and resulting damage due to programs written by inexperienced users is also simultaneously increasing. Due to the lack of 3D maps of the environment, there are no comprehensive support systems available to check programmed sequences for collision-free execution or to optimize them subsequently. This leads to longer process times and complicates commissioning, especially for inexperienced users. In this paper, a method is presented in which a collaborative robot can autonomously create a 3D map of its environment. Subsequently, the created environment map can be used to optimize existing processes, guarantee collision-free motions and support the operator during commissioning.

More and more industrial robots are being used in SMEs. Due to the lack of CAD models, they are rarely programmed using offline programming methods. With the methods presented in this paper, missing 3D maps of the environment can be flexibly created and even inexperienced operators can be enabled to program complex, collision-free motion sequences. In addition, already programmed sequences can be optimized by the downstream motion planning. The presented method also allows the omitting of intermediate points in complex sequences, which significantly reduces commissioning time and complexity.

In many sectors of the economy, modularization and linkage of distributed structures has significantly increased flexibility. Thereby, individual customer demands and changing conditions can be adressed. While this concept is often applied to closed systems whose control is coordinated by central components or can follow defined processes, concepts for the implementation in decentralized and open systems are missing. Such processes can be found in different domains of application, such as logistics, the construction industry or forestry. The use of active asset administration shells for modularized objects enables elements of a system or products to take over parts of the control and coordination. Production systems are enabled to act autonomously. A service infrastructure provides the necessary directory services to provide the asset administration shells with an entry point and to offer suitable routing services. The overall structure is demonstrated and evaluated using a representative example from forestry.

In the production industry, the volume, variety, and velocity of data as well as the number of deployed protocols increase exponentially due to the influences of the Internet-of-Things (IoT) advances. While hundreds of isolated solutions exist to utilize these data, e.g., optimizing processes or monitoring machine conditions, the lack of a unified data handling and exchange mechanism hinders the implementation of approaches to improve the quality of decisions and processes in such an interconnected environment. The vision of anInternet of Productionpromises the establishment of aWorldwide Lab, where data from every process in the network can be utilized, even interorganizational and across domains. While numerous existing approaches consider interoperability from an interface and communication system perspective, fundamental questions of data and information interoperability remain insufficiently addressed. In this article, we identifytenkey issues, derived from three distinctive real-world use cases that hinder large-scale data interoperability for industrial processes. Based on these issues, we derive a set offivekey requirements for future (IoT) data layers, building upon the FAIR data principles. We propose to address them by creatingFactDAG, a conceptual data layer model for maintaining a provenance-based, directed acyclic graph of facts, inspired by successful distributed version-control and collaboration systems. Eventually, such a standardization should greatly shape the future of interoperability in an interconnected production industry.

Due to agile product development, production systems have to be flexible and adaptable to meet high quality standards and a high productivity. As a result, set-up processes has to be shorter and more resilient because of the increasing number of variants. To meet future requirements, the processes need to be self-adaptive and reconfigurable at any time. Nowadays, a shift of the automation pyramid to interconnected cyber physical systems can be observed as well as emerging technologies as cloud and edge computing are introduced to production systems. These technologies in combination with the Digital Shadow, which provides information about all production assets, open up potential for an adaptive process control and an overall life cycle data management. For this, the Digital Shadow has to be used not only for the aggregation of data, but also for pushing data back into the system and to control the process. As a result, services in regard to an architecture based on edge computing as an enabling technology for an adaptive production together with the Digital Shadow are presented, implemented and discussed on the basis of an industrial use case.

Todays engineering processes utilize purchased parts from different manufacturers to set up a product model within engineering tools. As manufacturers provide product data using different catalog services, multiple interfaces have to be implemented to integrate data of purchased parts into engineering tools. By downloading the data into the engineering tool at a certain time, data consistency is interrupted. A holistic solution is still missing. This paper depicts an integrative concept to utilize Industry 4.0 components in the engineering process, allowing an extension of data consistency from manufacturers product descriptions over engineering tools to the product life cycle.

Due to the rising share of renewable energy sources in the energy market (e.g. 40 % in Germany 2018) the power grid has to be stabilized by other sources of balancing power than conventional power plants and the volatility of the energy price is expected to increase. Production machines in the industry have unused energy flexibilities in their production processes. Those flexibilities like the differences in energy consumption between various machining processes or even between single processing steps could be used for demand-side management. This paper presents an approach on how to model and control the energy consumption of CNC (Computerized Numerical Control) machines. While in previous studies on energy flexibilities, mainly high energy-consuming auxiliary units are investigated, the focus of this publication is to use explicitly the flexibilities inside the process. The main idea is to optimize automatically the production process to a certain volatile energy price without significantly delaying the process, increasing the workload on the shop floor with additional tasks or jeopardizing (much) the workpiece quality. The proposed method uses a material removal simulation in combination with an energy model of the flexibilized machine tool, to be able to model the energy consumption of each processing step, and its dependencies to other steps. The energy costs can be reduced by scheduling the various processing steps. The precondition for the “look ahead” is a workpiece model, a model of the machine and a tool model which are described in this publication. Therefore, it is not only suitable for series production but also for small batch production. The approach is especially interesting for long production processes (>>1 h) or a market with highly volatile energy prices.