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The term co-design describes a collaborative design process, usually across the boundaries of individual areas. A major aspect, and often an obstacle in the implementation of the co-design approach, is the establishment of a centralized data management system. In the past, the data management concept has been approached from different perspectives. Current approaches usually are oriented along the process chain and thus reflect a unidirectional flow of information. Neither the information feedback nor the reaction to adjustments in upstream model steps is supported, which makes continuous collaboration more difficult and at best reduces the co-design approach for continuous data usage. This paper addresses these weaknesses while outlining an integrative data processing concept enabling co-design between the domains of construction and industrial prefabrication. Geometrical information generated during architectural design and construction planning is reused for the production of components, manufacturing-related design adjustments are transferred back into the domain of construction. Changes throughout the entire process are centrally recorded. To ensure independence from the software tools used and to consider their great diversity, data exchange is largely based on standards, namely industry foundation classes and the standard for the exchange of product model data. In order to counteract the sequential processing according to a waterfall model and to support the co-design approach, the industrial prefabrication toolchain, consisting of computer-aided manufacturing software and downstream postprocessors, is largely automated, so that reprocessing of a workpiece for minor geometric adjustments no longer requires any interaction of the production planner.

State of the art machine tool controllers offer several Internet-of-Things (IoT) interfaces for machine data acquisition using industrial or edge computers. However, the available data exchange rates for these communication platforms are limited to a few hundred Hertz. As the data is not available in high frequency resolution, such a network communication is not suitable for monitoring and optimizing highly dynamic machining processes. This paper describes an efficient system architecture, which enables the acquisition of internal machine data as well as the high frequency sampling of external sensors. Based on this data, an Operational Modal Analysis (OMA) approach can be used to determine the tool tip dynamics during the machining process. Identification of tool tip frequency response requires the reconstruction of the excitation of the machine tool structure, i.e. the occurring machining forces. For this purpose, an approach relying on monitoring the commanded motor currents is applied.

The great flexibility provided by generative manufacturing offers immense potential for the structural component optimization. However, this freedom needs to be utilized by appropriate CAx tools. In this paper, we present a novel slicing method, enabling the automatic generation of topology-optimized Laminated Layer Manufacturing (LLM) parts. With this method a part of arbitrary shape can be automatically sliced and equipped with a uniform virtual grid that is suitable for manufacturing. The optimal density distribution is then mapped onto the individual slices and locally approximated through the introduction of variably shaped cavities. By using 3D modeling techniques all LLM sheets are modeled parametrically and assembled automatically. To verify the functionality of the presented method, it is applied to two practical parts and compared with a conventional topology optimization method.

Bin picking describes a robot system that picks unsorted objects from a bin based on sensor data. The object recognition is one of the main difficulties when calculating the grasping pose. In the worst case, a faulty object recognition leads to a system standstill. This work introduces a framework concept for improving the object recognition by predicting the configuration of all the objects in the bin based on an initial scan in form of a point cloud. This is done by taking advantage of an online simulation, with the goal of reducing the total number of scan cycles, which leads to a reduced risk of failure and improves the overall performance of a bin picking system.

Bei mehrstufigen Produktionssystemen rotierender Bauteile kann bereits ein kleiner Fehler zu Ausschuss führen. Um die Wettbewerbsfähigkeit europäischer Unternehmen zu gewährleisten, wird eine Null-Fehler Fertigung angestrebt. In diesem Beitrag wird dafür eine Web-App vorgestellt, die Bauteilvari- ationen automatisch erkennen und Fehlerkompensations- strategien ableiten kann. Das Potenzial des mehrstufigen Produktionssystems wird genutzt, um Fehler am Bauteil im Produktionsfluss durch eine geeignete Steuerung und Regelung zu beheben. In multi-stage manufacturing systems of rotating parts, even a small defect can lead to rejects. In order to maintain competitiveness, European companies aim for zero-defect manufacturing. We present a web app that can automatically detect part variations and derive defect compensation strategies. The potential of the multi-stage production system is used to eliminate part defects downstream by means of suitable control.

An important part of any reinforcement learning application is interfacing the agent to its environment. To enable an easier use of reinforcement learning agents in manufacturing and automation-related real-world environments, we propose an environment generator which acts as an adapter between the interface of the agent and existing industrial communication protocols. This paper describes the functionality and architecture of such an environment generator.

deformable linear objects (DLOs) such as hoses, cables or wires are often a limiting factor in robotic manipulation. For an industrial robot control, their nonlinear dynamics combined with their large variations in material parameters make it challenging to handle. Therefore, DLOs are usually manipulated manually in the industry, offering a huge potential for automation. This paper contributes to the field of robotic handling of DLOs by presenting and evaluating a novel model based controller and observer concept which is based on a multibody simulation. The presented closed-loop framework of a kinematic trajectory following controller with an online point cloud processing observer is first evaluated separately in the simulation and then in an experiment, showing its potential for cable routing scenarios.

Complex production systems, which have to handle product diversity and short product life cycles, can only be stable and efficient when successfully tested prior to their start-up. Also, changes in automation software within the life cycle of a production system should be verified through repetitive testing before applying the new software version. Therefore, test automation in combination with fault injection on a virtual model of the production system is seen as the next step to improve the verification and validation of a production system. This work presents an approach to achieve negative test cases for a test automation framework.

Due to digitalization, virtualization and Industry 4.0, the structure of the manufacturing industry is changing. One result of this development is cloud manufacturing, a new manufacturing paradigm. The number of new literatures related to this topic is increasing tremendously. Therefore, cloud manufacturing degenerates into a collective term, with a wide variety of definitions and research areas emerging. On the one hand, this paper deals with an automated statistical evaluation of the literature on cloud manufacturing. On the other hand, the main research areas of cloud manufacturing are identified by a statistical evaluation of the literature keywords from three selected databases. By means of natural language processing and machine learning methods, the keywords are automatically evaluated and grouped into clusters to obtain the main research areas.

This paper presents a method for reducing non-productive times in the planning and interpolation of lift movements during 2.5D machining. At the moment, this mostly vertical lift movement is executed separately from the position changing in the operating plane. This sequential processing of motions increases the non-productive time in processes with frequent changes of operating height, such as laser cutting. The reduction is realized by superimposing the planar path movements with lift motion. An asynchronous axis interpolation is applied to make use of the maximum machine dynamics. In addition, known obstacles can be taken into account by further parameters in the planning.

Most industrial robots are still controlled with motor-side feedback. To increase the accuracy of industrial robots, controllers with joint-side feedback and explicit consideration of the joint elasticity, such as linearization-based controllers, are needed. The key issue for the performance of linearization-based controllers is a high-fidelity model. Today, the drivetrains installed in the joints of industrial robots of the high payload class usually consist of a permanent magnet synchronous machine and a cycloidal drive. Such robot joints are highly nonlinear due to effects like hysteresis, torque ripples and friction. Therefore, the drivetrain dynamics are crucial for the experimental performance of linearization-based controllers for industrial robots. This paper identifies the challenges in linearization-based control of industrial robots with such a drivetrain configuration based on experimental results on a KUKA KR-210-2. Using an exemplary approach, it is shown that a linearization-based controller does not provide the theoretical performance due to needed model simplifications. For this purpose, simulation and experimental results are compared to a linear robot controller with motor-side feedback. These results indicate why such controllers are still a valid alternative for the practical application of similar industrial robots.

To enable the usage of standard hardware in safety-critical applications for production systems, new approaches for hardware fault tolerance are required. These approaches must be implemented on software level. As shown in the literature, arithmetic coding is a promising approach, but only supports integer calculations. For complex safety functions, e.g. in robotics, fast floating-point calculations are needed. Therefore, this paper presents a method for direct arithmetic encoding of floating-point calculations with low-performance impact. Moreover, a detailed residual error estimation is given.

One important design criteria of a cable-driven parallel robot is its workspace. Disturbance effects on drive systems, pulley mechanisms and cables have a major impact on the workspace due to their influence on cable forces. Thus, the computed wrench-feasible workspace with ideal kinematic constraints may not be valid while operating the platform of a cable robot. To ensure that the platform moves within the workspace, cable forces can be controlled, whereby kinematic inaccuracies such as calibration errors affecting the platform’s positional accuracy. Hence, in this paper a concept of the velocity based hybrid position-force controller is presented in order to pretension cable forces on one hand, and ensure positioning accuracy on the other hand. The controller stability is investigated on extreme poses outside the wrench-feasible workspace hull of a dynamic model of a planar cable robot in simulation environment. Thereupon, the hybrid controller was implemented in a real-time control system and analyzed on the cable robot setup COPacabana with experiments inside and outside the computed workspace border. The analysis of measured signals is based on statistical investigation by performing multiple measurements of the same platform trajectory. Both, simulation and experiments show that the proposed velocity based hybrid position-force controller is feasible and can be applied to move the platform outside the workspace.

In information technology, package managers such as npm or NuGet are popular and help to download desired software packages from a corresponding central storage. This view on the mechanism is a consumer perspective. The reverse process for suppliers to distribute packages is relevant as well, because it leads to effort. That could be remedied by an additional service. Especially in automation technology, programmable logic controller (PLC) engineers have different previous knowledge regarding information technology. The presented concept addresses the distribution of control code by an approach with an additional engineering service. This service is adaptable via an orchestration of single sub-services. The following implementation proves the technical feasibility. In conclusion, there are automatable processes for the distribution of control code to support PLC engineers in their developing activities.

Ovine offal is a significant export co-product of meat processing in New Zealand. There is currently no automatic system available for its sorting. In this paper, we first analyze the requirements for robotic sorting of internal sheep organs, including the particular needs of the food industry, the components required for the robotic sorting system and the manipulation tasks. Current practices in the animal slaughtering and processing industry are followed. And then a robotic system for ovine offal sorting is proposed, and the problems to be solved are summarized. Next, we discuss the current methods and theories to solve the above problems and give potential solutions. Finally, the conclusion and future work of ovine offal harvesting are given.

The digitalization of production is a strategic goal in the automation industry, though visions and industrial reality remain far apart. A key issue is that today’s segmented communication architecture based on fieldbus networks does not fulfill the future requirements. Converged networks connecting IT and OT systems while maintaining deterministic guarantees for real-time communication are required instead. Time Sensitive Networking (TSN) in the context of IEEE 802.1 Ethernet is a key enabling technology for converged networks. The transition from current communication architectures towards converged networks will not be a disruptive change, but rather an evolutionary process involving many steps. In this paper, an analysis of different dimensions of this transition process and their evolutionary steps is presented. Potential migration paths from current to future production architectures and resulting intermediate brownfield scenarios are identified and analyzed with a focus on their communication architectures.

Deep Reinforcement Learning (DRL) methods often rely on the meticulous tuning of hyperparameters to successfully resolve problems. One of the most influential parameters in optimization procedures based on stochastic gradient descent (SGD) is the learning rate. We investigate cyclical learning and propose a method for defining a general cyclical learning rate for various DRL problems. In this paper we present a method for cyclical learning applied to complex DRL problems. Our experiments show that, utilizing cyclical learning achieves similar or even better results than highly tuned fixed learning rates. This paper presents the first application of cyclical learning rates in DRL settings and is a step towards overcoming manual hyperparameter tuning.

Switching losses in inverters for industrial electric drives have a noticeable impact on their energy consumption and lifetime. In order to fulfill the accuracy requirements of electric drives for production equipment, a high switching frequency is typically used. This results in high switching losses which are proportional to the switching frequency. However, a high accuracy is not always required. Therefore, a method for adapting the switching frequency of the drive inverter during runtime when high accuracy is not required is presented in this paper. The method includes the identification of operating states of the machine tool, automatic calculation of the controller parameters and strategies for changing the PWM frequency during runtime. Experimental results validate this novel approach.

Vibration compensation is important for many domains. For the machine tool industry it translates to higher machining precision and longer component lifetime. Current methods for vibration damping have their shortcomings (e.g. need for accurate dynamic models). In this paper we present a reinforcement learning based approach to vibration compensation applied to a machine tool axis. The work describes the problem formulation, the solution, the implementation and experiments using industrial machine tool hardware and control system.

Open Platform Communications Unified Architecture (OPC UA) is a relevant technology in the field of machine-to-machine communication that defines data transport protocols and standardizes information modeling. Instead of defining a generalized world model, domain-specific models enable interoperability. These domain-specific models can be developed and released as Companion Specifications. More than 40 Companion Specifications are currently in development or already released. In this paper the current approaches to generate an OPC UA information model or an OPC UA Companion Specification are analyzed. A new modeling tooling based on the Eclipse Modeling Framework (EMF) is presented. This tooling demonstrates the potential of a model-driven approach for the creation of the information model and the structured part of the descriptive text document of a Companion Specification.

A reconfigurable production is able to adapt to a new product in a short time. This can be built up from cyber-physical production systems (CPPS) which exchange various data with several devices simultaneously via a convergent network. In these future production systems, convergent networks will connect more participants than real-time production networks do today. In order to enable uninterrupted operation, reconfigurations must not affect existing communication. Due to this limitation, fragmentation of the configuration can occur, restricting future operations. This paper investigates fragmentation caused by reconfigurations using a simulation to determine whether fragmentation can lead to problems in future production networks.

This paper discusses an approach to enable step-wise velocity changes in machine tool feed drives while reducing the reaction force of the drive on structural machine components. The implementation is based on an additional actuator that transmits well-defined impulses on the table via mechanical impacts. Possible applications are seen in processes as beam processing or handling. The approach is introduced by means of a multi-body model and afterwards experimental results are shown. On the one hand, the reduction of the tracking error while following discontinuous velocity profiles is analyzed, on the other hand, the reduced excitation of the machine structure is shown. The experimental verification of the functional principle is performed on a single axis setup where the fundamental parameters in design, material and control are quantified. Concluding, a short outlook on remaining research topics regarding the shown approach is given., Deutsche Forschungsgemeinschaft, Projekt DEAL

For the automatic calculation of collision-free robot paths, an environment model is required for collision avoidance. The accuracy requirements for the environment model depend very much on the specific task of the robot. The computation time of the collision calculation increases strongly with higher resolution and dimensions of the environment model. For this reason, the demand for high accuracy and fast calculation of the collision-free path is contradictory. To solve this contradiction, we propose a method based on the multi-resolution property of octree based maps. Using a subdivision of the workspace into different subareas and a set of poses to be connected by path planning, a multi-resolution map is created. The individual resolution levels depend on the accuracy requirements of the subareas which are defined by the process requirements. The presented method is evaluated using the ”KaBa” path planning framework. For the evaluation, point clouds scanned with a line laser are tested in two path planning scenarios.

Motion-capture systems are important tools for several industrial and clinical applications. Although commercial optical motion-capture systems are available, their setup are often bulky, time consuming, and expensive, and require access to specialized laboratories. Several attempts have been made to introduce an alternative low-cost simple motion-capture system, though the performance has not been satisfactory addressed. In this paper, a compact, portable, low-cost, and simple planar-fiducial-marker-based motion-capture system (PFMS) is developed. The system comprises of one or two generic webcams for planar or spatial mandibular motion capturing, a tripod, planar markers printed on paper, and a computer for processing the real-time tracking of the markers. Sensor fusion of measurements from the two webcams in the PFMS results in accurate data for spatial mandibular motion capturing. The ability of the PFMS to record both planar and spatial motions is examined through capturing the complex 3-D movements of the incisal point of a human subject simultaneously by both the PFMS and a commercial research oriented optical motion-capture system. Least products regression and normalized root-mean-square error were used to compare the recordings by the two systems. The statistical analyses indicate a similar order of precision in the results obtained from the two systems, even though the PFMS is significantly cheaper and easier setup compared to the commercial 3-D motion-capture system.

The research and development of intelligent automation solutions is a ground-breaking point for the factory of the future. A promising and challenging mission is the use of autonomous robot systems to automate tasks in the field of maintenance. For this purpose, the robot system must be able to plan autonomously the different manipulation tasks and the corresponding paths. Basic requirements are the development of algorithms with a low computational complexity and the possibility to deal with environmental uncertainties. In this paper, an approach is presented, which is especially suited to solve the problem of maintenance automation. For this purpose, offline data from CAD is combined with online data from an RGBD vision system via a probabilistic filter, to compensate uncertainties from offline data. For planning the different tasks, a method is explained, which uses a symbolic description, founded on a novel sampling-based method to compute the disassembly space. For path planning, we use global state-of-the-art algorithms with a method that allows the adaption of the exploration stepsize in order to reduce the planning time. Every method is experimentally validated and discussed. Note to Practitioners —This paper was motivated by the current deficit to automate maintenance tasks in manufacturing systems. Today there exist many automatic methods for fault detection, but there is no possibility to restore the desired state. A typical application is the replacement of a defective component or service tasks such as refilling of cooling lubricant in machine tools. The focus of this paper is to provide automatic planning concepts for robot systems which deal with this problem. For this, a task planning algorithm is proposed which uses CAD and vision data. The advantage of this method is that it works on a contact analysis, based on a discrete approximation of the disassembly space, allowing a fast determination of possible robot manipulations. Also, a new preprocessing concept for path planning is developed which enables the adaptation of the stepsize of the path planner, based on the local occupancy of the environment. Through this, it is possible to drastically reduce the required planning effort.

The intrinsic advantages of parallel robots, such as higher stiffness, accuracy, and speed, make them appealing to both industry and academia. However, some inevitable drawbacks arising from their parallel structure such as limited workspace, singular configurations, and low dexterity, challenge their applications. Redundantly actuated parallel robots (RAPRs) have been proposed as a means to improve the aforementioned disadvantages. This paper provides a comprehensive review of the published literature on RAPRs, since this field emerged over three decades ago, to lay a foundation for next investigations and enhancing the state of the art. The relevant published works are studied and classified into four primary topics, including design, kinematics, dynamics, and control issues of RAPRs. Common structures of RAPRs are introduced and studied. The literature is also studied in terms of RAPRs applications, wherein the limited practical applications are pointed out. The current challenges and future directions of the field are discussed with the research gap identified. New applications for RAPRs are proposed.

This paper presents an approach for persistent data backends for OPC UA namespaces. OPC UA as M2M protocol is used in various manufacturing machines and provides data access to e.g. sensors and setpoints. The central and persistent data storage for e.g. data-mining and condition-monitoring services in information technology infrastructures is challenging. Therefore, a holistic approach for storing OPC UA information in lightweight directory access protocol server is presented including a model for storing namespaces, information models and values in LDAP trees of scalable enterprise infrastructures to fulfil manufacturing and IT requirements in the context of Industry 4.0.

In the field of machine tools, rack-and-pinion drive systems are one of the commonly used feed drive systems. In order to achieve the high accuracy specifications of modern production facilities, these drives are electrically preloaded to reduce backlash in the drive train. In most cases, the preload is fixed, even though the method of electrical preloading allows adjustment during operation. This paper describes a novel approach – called adaptive preloading – to adjust preload during operation. The objective is to increase the drive system’s energy efficiency by continuously adapting the preload, which is minimally required for maintaining the drive system’s accuracy.

Robots in daily human environments need to fulfill a lot of requirements and boundary conditions to be accepted and useful. To meet these, in this paper, a mechanism similar to a spherical joint is proposed and compared to others. It can be used to extend the workspace of manipulators and add a wide coverage for sensors mounted on the head of the system while being safe by design and enabling an agile and expressive display of gestures. Beside a generic description of the geometry and the main dimensions of the mechanism, also the kinematic calculation and properties of the workspace are shown and discussed.

The production of motors for the electric vehicles requires innovative and systematic quality control approaches to boost efficiency while moving from low volume towards mass production. In this context, end-of-line quality testing methods are usually applied to assess the product functionality at the end of the process chain. However, this approach does not allow process monitoring and the in-line prevention and correction of defects, leading to significant scrap rates and value losses. This paper presents a new system-level strategy for the in-line quality-oriented assembly of rotors in the production of automotive electric drives. The new strategy is based on a new cyber-physical system that optimizes the assembly strategy depending on the quality of magnetized stacks, monitored with data gathered by in-line inspection. For each batch, the magnetic stacks to be assembled and their orientation is selected according to an optimization algorithm, aiming at minimizing the deviation from the target total integral magnetic flux and maximizing the field uniformity in the magnetized rotor. The impact of the proposed strategy on the quality and productivity related performance measures are predicted by analytical methods. Experimental results based on an industrial case study are reported, showing that the application of the proposed strategy yields a significant increase in the production rate of conforming engines. The proposed approach paves the way to innovative zero-defect manufacturing strategies at system level in emerging, high-tech, manufacturing sectors.

Reconfigurable machinery systems challenge state-of-the-art engineering processes, especially towards safe design, documentation and operation. Assessments done manually by safety engineers are required after each reconfiguration and reduce the advantages of these agile systems. New approaches are needed to reduce the engineering effort in order to satisfy the demands caused by frequent changes of reconfigurable machinery. This paper analyzes today’s safety related processes, examines safety requirements and challenges, studies existing approaches from academia and proposes a concept for future safety approval processes in accordance to EC directives.

The precision of machine tools is essentially determined by the feed drive control. To improve the dynamic behavior of elastic feed drives, this paper presents a novel position control concept, substituting the conventional P position controller of the industrial cascade control with a continuous sliding mode controller (SMC) together with a Kalman Filter for state estimation. The novelty of this approach lies in the focus on practical applicability: The underlying PI-cascade structure leads to low commissioning effort and the SMC controller’s parameterization is simplified to a single tuning parameter. A general compliant feed drive model is used as plant, whose parameters usually are already known from commissioning of the velocity controller. The SMC position controller is derived analytically and its stability and damping properties are investigated for the dominating mechanical mode for uncertainties of the model parameters and a wide range of controller gains. The effectiveness of the SMC-PI controller is validated for an industrial ball screw feed drive. Extensive experimental results show a considerable decrease of tracking errors in command tracking, disturbance rejection and for a high speed cutting (HSC) milling experiment compared to P-PI cascade control, while stability margins remain almost independent of the controller gains. The parameterization is discussed on the basis of a theoretical analysis and measuring results. Furthermore, excellent robustness properties regarding model errors are demonstrated.

Classical computing is shifted gradually into the cloud offering completely new possibilities in information usage, computing power, and application of learning algorithms. In this paper, a cloud-based architecture of control systems is investigated showing the benefit for multistage production systems. All sequential manufacturing and assembly processes are connected via a cloud-based architecture, which allows using information from a previous production step in one of the subsequent steps for downstream deviation compensation. This strategy is applied to the rotor production of electric motors in the automotive industry, as the current production shows high defect rates due to the lack of adequate sensor signals and optimization algorithms. The magnetization process of permanent magnets is executed in saturation so that the generation of deviations cannot be avoided by process control or process optimization. Instead, the variance in the magnetization signal must be compensated in a downstream process, here the rotor assembly stage. Project results show how such a cloud-based architecture can increase the product quality while decreasing the amount of scrap parts in a real industrial scenario, consequently saving valuable resources such as energy and raw materials. Reduction of deviations is crucial for this emerging industrial sector as electric motor production for vehicles is moving towards mass production in the future.

Additive Manufacturing (AM) technologies require innovative design paradigms and guidelines that exhaust the offered freedoms in geometry and material. The aim of the Design for Additive Manufacturing is to provide design opportunities and to enhance workpiece properties taking into account constraints of the process, e.g. kinematics and limitations of print technology. Most AM applications are constrained to three-axis movements limiting the fabrication of workpieces layer by layer to one fixed building direction only. This causes limitations of strength properties, surface quality and the need of supporting structures. Multi-axis AM enables completely new design possibilities going beyond current optimization and design strategies of conventional AM workpieces. This innovation requires multiple design and manufacturing changes in the internal and external geometry of the workpiece as well as in machine and printing head technology. This paper identifies the requirements and capabilities of multi-axis AM and presents possible solutions to overcome the identified challenges. Preliminary results for multi-axis AM are described on an exemplary workpiece.

Inhalt: Die Hardware in the Loop-Simulation (HILS) von Maschinen und Anlagen wird eingesetzt, um die Zeiten zur Herstellung einer Maschine zu reduzieren und die Softwarequalität der Steuerung zu erhöhen. Inzwischen spielt sie auch bei der Projektierung und der Konzeptüberprüfung eine immer größere Rolle. Der vorliegende Beitrag zeigt auf, mit welchen Methoden der Modellierung wiederverwend-bare Modelle geschaffen und so eine automatische Generierung von HILS realisiert werden kann.

Until now, reconfigurable automation systems require a high engineering effort for the reconfiguration in order to begin production. Plug-and-play concepts are rare and usually only cover part of the disciplines involved in the mechatronic system. One of the challenges of reconfigurable systems considered in the research project DEVEKOS is the rearrangement of positioning axes into simple kinematic structures as it is required for the manufacturing process. In order to implement synchronous control of the multi-axes group, the calculation of the forward and inverse kinematics is necessary. This paper introduces a framework in which the kinematic description of the multi-axes group can be imported in a customized AutomationML-file. The framework then verifies the characteristics of the kinematics and considers common strategies for solving the inverse kinematics. The implementation presented recognizes four different types of basic kinematic structures and automatically calculates the inverse kinematic equations in those cases.

CNC controls are widely used in modern manufac-turing technology. They play a important role for the productivityof the machine they control. Already today, these controls arereaching their performance limits in certain scenarios. Thecontroller acts like a performance bottleneck for increasinglydynamic machine tools. To further increase the performance ofCNC controllers, this work proposes an novel method based onthe use of generative adversarial networks (GAN) for the directgeneration of tool paths within the CNC controllers. We presenta concept that enables interpolation with GAN. Based on this, a novel system architecture for the integration of GAN into anAI-module within CNC controls is developed. The new methodis evaluated on the basis of an example contour. GAN trained togenerate linear toolpaths are used for evaluation. A smoothingGAN is used to blend the sharp path transitions between thegenerated path segments.

If existing communication systems are converted to OPC UA, an OPC UA information model is required for the existing data structures. In small systems, an information model can be created manually with reasonable effort. In systems with numerous data structures, however, the effort for manual creation is too demanding. In order to continue using the existing data structures in the code without having to handle OPC UA-specific data structures, a transformation between the existing data structures and the OPC UA information model is necessary. This article presents a possibility of generating an OPC UA information model by a description of the existing data structures, e.g. generated based on ROS messages. The description of data structures is evaluated at runtime and linked with the existing data structures. The different representations for the different access types (read, request/response) in the OPC UA information model are discussed. In addition, an OPC UA client can read the information model from the descriptions of the data structures and write the read data back to the original data structure. In this way, both the provision of the data via OPC UA and the reception are possible. The adaptation effort of the existing software is minimal, since the existing structures are automatically populated and can still be used as usual.

The increasing digitalization of manufacturing processes allows manufacturing companies to access and store data from the shop floor in order to optimize technical and organizational processes by value-adding data analytics services. However, the realization of data analytics projects requires knowledge and resources that are often not available in manufacturing companies. We therefore propose a microservice-based service platform, where data scientists can provide production-ready data analytics services. The manufacturing company can use these services to analyze their data. In addition, the platform enables several services to be combined to form complex service solutions. This allows the establishment of individual, user-specific reactions to analysis results and connect different data sources. We illustrate the possibilities of the platform by means of an application example.

Current robot control is a rather static, monolithic application, where the application data has to be programmed, before the actual process starts. For the automation of more dynamical tasks, additional real-time control loops for process control and a more flexible robot control are needed. Therefore, this paper introduces a robot control architecture for real-time and separate asynchronous communication, which is able to build up local real-time control loops as well as including massively scalable cloud components. In a proof-of-concept, a multibody dynamic simulation of a robot and a controller are virtualized as separate components. The real-time suitability of the architecture is evaluated in comparison with non-virtualized and a monolithic variant of the same application.

With the increasing number of sensors installed in production systems and the associated amount of recorded data, a high potential for optimizing manufacturing processes is offered. Based on the gathered information, data analyzing methods can be used to identify correlations and thus, optimize complex multi-stage production systems with the goal of zero-defect manufacturing. For this purpose, multiple statistical methods have to be applied, which is often a time-consuming task and currently requires trained and experienced specialists. Furthermore, the investigated data must be kept up to date for the early recognition of changes possibly leading to cost-intensive scrap and production downtimes. To address these issues, a web-based platform with an intuitive user interface was developed. The platform can access and process various data sources using modular methods. In addition, as part of continuous monitoring, the results of these analyzing steps can be dynamically calculated. The aim of the platform is to make complex analysis processes accessible to machine operators and thus to combine domain expertise and statistical knowledge. The paper describes the underlying architecture and the relevant interfaces of the platform.

Produktionsanlagen bieten im Laufe ihres meist mehrere Jahrzehnte andauernden Lebenszyklus diverse Nachrüstungs- und Optimierungspotentiale. Besonders die intensive digitale Datenerfassung und anschließende Auswertung von Mehrwertdiensten bewährt sich als Werkzeug, solche Potentiale zu identifizieren und gewinnbringend zu nutzen. Jedoch fehlt es Bestandsanlagen an den nötigen digitalen Schnittstellen die den Zugriff für die Datenerfassung ermöglichen. Im Kontext dieser Problemstellung exploriert das Konsortium des öffentlich geförderten BMBF-Projektes „RetroNet: Retrofitting von Maschinen und Anlagen für die Vernetzung mit Industrie 4.0 Technologie“ das Segment der IIoT-Geräte und präsentiert die Erfahrungen für ein methodisches Vorgehen, wie solche Geräte ökonomisch für das Retrofitting von Bestandsanlagen eingesetzt werden können. Dabei werden die Herausforderungen von der effektiven Nachrüstung von digitaler Sensorik sowie der Entwicklung von rückführender Meh...

This paper presents a new control principle to improve the bandwidth of feed drives without any additional sensor or actuator. The speed difference between the motor and the table is regarded as the mechanical vibration and fed back to the controller. The presented principle is feasible for feed drives with large inertia ratio, which can be achieved by utilizing a ball screw with high pitch and a high dynamic motor with small inertia. The stability condition is found through the Lyapunov criteria. The experimental results verify its effectiveness and robustness.

Increasing resource and energy efficiency for a sustainable production has led to the analyses of saving potentials and subsequently to the development of saving strategies in a multitude of disciplines. Each of those strategies resulted in numerous improvements, however, they still lack universal valid consideration. To overcome this deficit, a machine- and machine control-independent energy control system has been developed in the research group ECOMATION. The energy control system allows to execute any control- or operation-based energy optimizer. To use this energy control system more efficient machine tool controls need to be extended by additional functionality. The energy control system and the necessary migration strategy of machine tool controls will be discussed in this paper.

Robotic systems have the potential to automate deburring processes along edges of arbitrarily shaped workpieces. The desired robot movement can be realized by proper trajectory planning using the computer-aided design (CAD) model of the manufactured workpiece. A fundamental problem is that geometric shape deviations between the nominal CAD model and the manufactured parts might be beyond acceptable limits for correct execution of the deburring process. This paper proposes a novel, easy to implement and practical approach to detect shape deviations of workpieces for automatic adaptation of robotic deburring processes. The approach only uses dimensional tolerance specifications of the manufactured part provided by the product design to derive possible variations of the workpiece geometry model. A matching process is performed between point clouds for each of the considered variants and a measured point cloud from the manufactured workpiece using an Iterative Closest Point (ICP) approach. Resulting point distances are used for evaluation of shape similarity between the compared point clouds. Finally, the most similar geometry model is identified for subsequent trajectory planning and workpiece localization. Experimental validation is performed by an industrial robot equipped with a stereo camera for shape sensing and a milling tool for subsequent execution of a deburring process. The results demonstrate the effectiveness and practicality of the proposed approach in industrial applications and an increased deburring quality.

Sliding bearings have good damping properties and a high stiffness. However, stiction and poor sliding friction characteristics limit their use in applications, where high requirements are defined on the smoothness of motion. By inducing ultrasonic oscillations into the sliding contact it is possible to actively control the friction. The adjustable friction bearing uses this principle. This leads to a new bearing type that has the stiffness and damping properties of a sliding bearing, while at the same time, offering a linear friction characteristic without stiction. This paper presents basic principles for the design of ultrasonic bearings along with experimental results of a friction-adjustable machine table.