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Abstract Sandwich composites made of particle-filled hollow sphere structures (PHSS) and steel sheets offer excellent lightweight and damping properties, ideal for high-speed machine tool components under dynamic loads. Previous research has focused on single particle-filled hollow sphere (PHS) and small PHSS, leaving a gap understanding of PHSS/steel sandwich composites. In this study a test rig was developed, and Design of Experiments and Response Surface Analysis were used to investigate the effects of sheet and core thickness (design parameters), filling ratio, and particle size (particle parameters) on the damping performance of PHSS/steel sandwiches. The results indicate that the design parameters have a significant influence on damping performance. The interaction between design and particle parameters also substantially affects damping. Minimizing particle size, increasing filling ratio, thinning the face layer, and thickening the core layer significantly improve structural damping. To address manufacturing tolerances, a finite element (FE) model-based optimization was developed to accurately determine PHSS material parameters. These parameters were used in an FE model of the PHSS-steel composite, with identified contact parameters minimizing measurement and simulation differences. The homogenized material model and the linear model using global damping parameters accurately reproduce the dynamic properties of the PHSS-steel sandwich composite in low vibration modes.

Despite increasing digitalization and automation, complex production processes often require human judgment/decision-making adaptability. Humans can abstract and transfer knowledge to new situations. People in production are an irreplaceable resource. This paper presents a new concept for digitizing human expertise and their ability to make knowledge-based decisions in the production area based on ontologies and causal Bayesian networks for further research. Dedicated approaches for the ontology-based creation of Bayesian networks exist in the literature. Therefore, we first comprehensively analyze previous studies and summarize the approaches. We then add the causal perspective, which has often not been an explicit subject of consideration. We see a research gap in the systematic and structured approach to ontology-based generation of causal graphs (CGs). At the current state of knowledge, the semantic understanding of a domain formalized in an ontology can contribute to developing a generic approach to derive a CG. The ontology functions as a knowledge base by formally representing knowledge and experience. Causal inference calculations can mathematically imitate the human decision-making process under uncertainty. Therefore, a systematic ontology-based approach to building a CG can allow digitizing the human ability to make decisions based on experience and knowledge.

Sensor-based monitoring of process and tool condition in milling is a key technology for improving productivity and workpiece quality, as well as enabling automation of machine tools. However, industrial implementation of such monitoring systems remains a difficult task, since they require high sensitivity and minimal impact on CNC machines and cutting conditions. This paper presents a novel multi-sensory tool holder for measurement of process forces and vibrations in direct proximity to the cutting tool. In particular, the sensor system has an integrated temperature sensor, a triaxial accelerometer and strain gauges for measurement of axial force and bending moment. It is equipped with a self-sufficient electric generator and wireless data transmission, allowing for a tool holder design without interfering contours. Milling and drilling experiments with varying cutting parameters are conducted. The measurement data are analyzed, pre-processed and verified with reference signals. Furthermore, the suitability of all integrated sensors for detection of dynamic instabilities (chatter) is investigated, showing that bending moment and tangential acceleration signals are the most sensitive regarding this monitoring task.

Industrial robots are increasingly prevalent due to their large workspace and cost-effectiveness. However, their limited static and dynamic stiffness can lead to issues like mode coupling chatter and regenerative chatter in robotic milling processes, even at shallow cutting depths. These problems significantly impact performance, product quality, tool longevity, and can damage robot components. An active inertial actuator was deployed at the milling spindle to enhance dynamic stiffness and suppress low-frequency vibrations effectively. It was identified that the characteristics of the actuator change with its mounting orientation, a common scenario in robotic machining processes. This variation has not been reported in the literature. Our study includes the identification of model parameters for the actuator in both horizontal and vertical mountings. Additionally, the novelty of the present work lies in the specific design and implementation of compensation filters tailored for the active inertial actuator in both horizontal and vertical configurations. These filters address the unique challenges posed by low-frequency vibrations in robotic milling, offering significant improvements in dynamic stiffness and vibration suppression. Traditional model-based compensators were effective for vertical mounting, while pole-zero placement techniques with minimum phase systems were optimal for horizontal mounting. These compensators significantly enhanced dynamic stiffness, reducing maximum low-frequency robot structural modes by approximately 100% in horizontal mounting and approximately 214% in the vertical configuration of the actuator. This advancement promises to enhance industrial robot capabilities across diverse machining applications.

Additive manufacturing of plastic components in medical technology enables greater freedom of design when designing patient-specific products, in particular, in production of customised medical products, such as orthoses. In the present contribution, the advantages of a digital process chain are combined, from the 3D scan of the patient to CAD-supported modelling of the corrective form and the orthosis design until the path planning of a printable geometry. The main disadvantages of current additive printing techniques, such as the fused filament fabrication (FFF) process, are high printing times (>12 h) for larger components as well as the low degree of freedom in the 2.5D printing technique that prevent the subsequent application of geometry features to the product. The fast SEAMHex (Screw Extrusion Additive Manufacturing) printing technology with a hexapod kinematic printing bed provides a solution to the mentioned difficulties. Consequently, the high-performance printer has been prepared for the individual requirements of medical technology in terms of materials and geometries. An effective additive manufacturing process has been realised and tested in combination with a digital process chain for orthosis modelling.

Abstract Machine Learning has become more important for materials engineering in the last decade. Globally, automated machine learning (AutoML) is growing in popularity with the increasing demand for data analysis solutions. Yet, it is not frequently used for small tabular data. Comparisons and benchmarks already exist to assess the qualities of AutoML tools in general, but none of them elaborates on the surrounding conditions of materials engineers working with experimental data: small datasets with less than 1000 samples. This benchmark addresses these conditions and draws special attention to the overall competitiveness with manual data analysis. Four representative AutoML frameworks are used to evaluate twelve domain-specific datasets to provide orientation on the promises of AutoML in the field of materials engineering. Performance, robustness and usability are discussed in particular. The results lead to two main conclusions: First, AutoML is highly competitive with manual model optimization, even with little training time. Second, the data sampling for train and test data is of crucial importance for reliable results.

Multi-Material Jetting (MMJ) is an additive manufacturing process empowering the printing of ceramics and hard metals with the highest precision. Given great advantages, it also poses challenges in ensuring the repeatability of part quality due to an inherent broader choice of built strategies. The addition of advanced quality assurance methods can therefore benefit the repeatability of part quality for widespread adoption. In particular, quality defects caused by improperly configured droplet overlap parameterizations, despite droplets themselves being well parameterized, constitute a major challenge for stable process control. This publication deals with the automated classification of the adequacy of process parameterization on green parts based on in-line surface measurements and their processing with machine learning methods, in particular the training of convolutional neural networks. To generate the training data, a demo part structure with eight layers was printed with different overlap settings, scanned, and labeled by process engineers. In particular, models with two convolutional layers and a pooling size of (6, 6) appeared to yield the best accuracies. Models trained only with images of the first layer and without the infill edge obtained validation accuracies of 90%. Consequently, an arbitrary section of the first layer is sufficient to deliver a prediction about the quality of the subsequently printed layers.

The deformation of press bolster plates under load can significantly impact the quality of manufactured workpieces. Consequently, press manufacturers are usually obliged to provide the metric proof of the permissible deformation values during the press commissioning process. Unfortunately, the existing measurement methods for determining bolster plate deformations present flaws in the measurement accuracy, the application flexibility, the metrological effort, and other aspects. These issues have been addressed through the development of a new measurement method using multiple inclination profiles on the surface of the measured object. Based on the difference in the inclination between the unloaded and loaded states, the method approximates the inclination surface and calculates the deformation of the measured object through integration. The measurement method was first used for commissioning tests of forming presses. A comparison with the results obtained with a measurement frame equipped with high-resolution measurement probes demonstrates an accuracy of the new method of less than 20 µm.

Thermally induced position errors are one of the main error sources on the workpiece caused by the behavior of the machine tool. In today’s industrial environment, the correction of thermal errors is usually based on simple regression approaches, where the characteristic diagrams for correction are generated experimentally. The performance of these approaches is only valid for the corresponding load regimes, which often results in insufficient correction quality in practical applications. Consequently, there is only a limited benefit or even a deterioration in machine behavior if the correction characteristic is based on an inapplicable load case compared to the initial experiment. Simulation-generated characteristic diagrams using finite element models solve this disadvantage, but do not answer the question about the choice of the right characteristic matching the current load situation, and, in addition, calculate very slowly. Structural model-based correction using reduced models, on the other hand, calculates quickly, but requires a high modeling effort for accurate correction. The approach, presented in this contribution, combines simulation-generated characteristic diagrams and a structural model-based decision algorithm for a new hybrid model in order to select the appropriate characteristic diagram for the present load situation in the control system. This paper presents the simulative characteristic diagram generation by a finite element model validated by experiments in a climate chamber and a validated structural model including the concept for the decision algorithm.

In recent years, space agencies, such as the National Aeronautics and Space Administration (NASA) and European Space Agency (ESA), have expanded their research activities in the field of manufacturing in space. These measures serve to reduce limitations and costs through fairing size, launch mass capabilities or logistic missions. The objective, in turn, is to develop technologies and processes that enable on-demand manufacturing for long-term space missions and on other celestial bodies. Within these research activities, in-situ resource utilization (ISRU) and recycling are major topics to exploit local resources and save transport capacity and, therefore, costs. On the other hand, it is important to carefully consider which items can be brought and which must be manufactured on the Moon. Consequently, on-demand needs in future space missions are considered regarding frequency, raw material and required manufacturing processes according to investigations by ESA and NASA. In conclusion, manufacturing in space state-of-the-art shows a strong focus on additive processes, primarily considering semicrystalline or amorphous plastics. The subtractive processing of metallic or ceramic materials, in turn, currently represents a research gap. Consequently, an approach for in-situ resource utilization-based subtractive manufacturing in space is presented to supplement the existing processes. The latter uses a high-pressure jet of water, with regolith simulate as abrasive in suspension, being directed at the workpiece, which is moved to separate metal and glass. Proof-of-concept results are presented, including suitable process windows, achieved cutting geometries, as well as the effects of parameter variations on the system technology and consumables used. The focus of the investigations supplements the general requirements for the design of machine tools for space applications with inertial process-specific boundary conditions as a step towards higher technology maturity.

Human-robot collaboration with traditional industrial robots is a cardinal step towards agile manufacturing and re-manufacturing processes. These processes require constant human presence, which results in lower operational efficiency based on current industrial collision avoidance systems. The work proposes a novel local and global sensing framework, which discusses a flexible sensor concept comprising a single 2D or 3D LiDAR while formulating occlusion due to the robot body. Moreover, this work extends the previous local global sensing methodology to incorporate local (co-moving) 3D sensors on the robot body. The local 3D camera faces toward the robot occlusion area, resulted from the robot body in front of a single global 3D LiDAR. Apart from the sensor concept, this work also proposes an efficient method to estimate sensitivity and reactivity of sensing and control sub-systems The proposed methodologies are tested with a heavy-duty industrial robot along with a 3D LiDAR and camera. The integrated local global sensing methods allow high robot speeds resulting in process efficiency while ensuring human safety and sensor flexibility.

Manual annotation for human action recognition with content semantics using 3D Point Cloud (3D-PC) in industrial environments consumes a lot of time and resources. This work aims to recognize, analyze, and model human actions to develop a framework for automatically extracting content semantics. Main Contributions of this work: 1. design a multi-layer structure of various DNN classifiers to detect and extract humans and dynamic objects using 3D-PC preciously, 2. empirical experiments with over 10 subjects for collecting datasets of human actions and activities in one industrial setting, 3. development of an intuitive GUI to verify human actions and its interaction activities with the environment, 4. design and implement a methodology for automatic sequence matching of human actions in 3D-PC. All these procedures are merged in the proposed framework and evaluated in one industrial Use-Case with flexible patch sizes. Comparing the new approach with standard methods has shown that the annotation process can be accelerated by 5.2 times through automation.

BackgroundThe electronic health record (EHR) targets systematized collection of patient-specific, electronically stored health data. The EHR is an evolving concept driven by ongoing developments and open or unclear legal issues concerning medical technologies, cross-domain data integration, and unclear access roles. Consequently, an interdisciplinary discourse based on representative pilot scenarios is required to connect previously unconnected domains. ObjectiveWe address cross-domain data integration including access control using the specific example of a unique device identification (UDI)–expanded hip implant. In fact, the integration of technical focus data into the hospital information system (HIS) is considered based on surgically relevant information. Moreover, the acquisition of social focus data based on mobile health (mHealth) is addressed, covering data integration and networking with therapeutic intervention and acute diagnostics data. MethodsIn addition to the additive manufacturing of a hip implant with the integration of a UDI, we built a database that combines database technology and a wrapper layer known from extract, transform, load systems and brings it into a SQL database, WEB application programming interface (API) layer (back end), interface layer (rest API), and front end. It also provides semantic integration through connection mechanisms between data elements. ResultsA hip implant is approached by design, production, and verification while linking operation-relevant specifics like implant-bone fit by merging patient-specific image material (computed tomography, magnetic resonance imaging, or a biomodel) and the digital implant twin for well-founded selection pairing. This decision-facilitating linkage, which improves surgical planning, relates to patient-specific postoperative influencing factors during the healing phase. A unique product identification approach is presented, allowing a postoperative read-out with state-of-the-art hospital technology while enabling future access scenarios for patient and implant data. The latter was considered from the manufacturing perspective using the process manufacturing chain for a (patient-specific) implant to identify quality-relevant data for later access. In addition, sensor concepts were identified to use to monitor the patient-implant interaction during the healing phase using wearables, for example. A data aggregation and integration concept for heterogeneous data sources from the considered focus domains is also presented. Finally, a hierarchical data access concept is shown, protecting sensitive patient data from misuse using existing scenarios. ConclusionsPersonalized medicine requires cross-domain linkage of data, which, in turn, require an appropriate data infrastructure and adequate hierarchical data access solutions in a shared and federated data space. The hip implant is used as an example for the usefulness of cross-domain data linkage since it bundles social, medical, and technical aspects of the implantation. It is necessary to open existing databases using interfaces for secure integration of data from end devices and to assure availability through suitable access models while guaranteeing long-term, independent data persistence. A suitable strategy requires the combination of technical solutions from the areas of identity and trust, federated data storage, cryptographic procedures, and software engineering as well as organizational changes.

Most motion planners generate trajectories as low-level control inputs, such as joint torque or interpolation of joint angles, which cannot be deployed directly in most industrial robot control systems. Some industrial robot systems provide interfaces to execute planned trajectories by an additional control loop with low-level control inputs. However, there is a geometric and temporal deviation between the executed and the planned motions due to the inaccurate estimation of the inaccessible robot dynamic behavior and controller parameters in the planning phase. This deviation can lead to collisions or dangerous situations, especially in heavy-duty industrial robot applications where high-speed and long-distance motions are widely used. When deploying the planned robot motion, the actual robot motion needs to be iteratively checked and adjusted to avoid collisions caused by the deviation between the planned and the executed motions. This process takes a lot of time and engineering effort. Therefore, the state-of-the-art methods no longer meet the needs of today’s agile manufacturing for robotic systems that should rapidly plan and deploy new robot motions for different tasks. We present a data-driven motion planning approach using a neural network structure to simultaneously learn high-level motion commands and robot dynamics from acquired realistic collision-free trajectories. The trained neural network can generate trajectory in the form of high-level commands, such as Point-to-Point and Linear motion commands, which can be executed directly by the robot control system. The result carried out in various experimental scenarios has shown that the geometric and temporal deviation between the executed and the planned motions by the proposed approach has been significantly reduced, even if without access to the “black box” parameters of the robot. Furthermore, the proposed approach can generate new collision-free trajectories up to 10 times faster than benchmark motion planners.

Increasing the output of a processing machine should often be achieved by increasing the machine’s operating speed. Associated with the increasing operating speed are undesirable effects occurring on the machine or process, e.g. vibrations that limit the operating speed. This paper discusses some selected causes for limitations that may occur on processing machines. An already developed approach to dealing with the problems is shortly presented. It is discussed why it is effective in some processes but not in others. Consequently, the existing approach is extended by an adaptive interpolation approach that deals with the changing operating speed. This extension has the effect that the advantages of the new approach can be applied to processes that have been insufficiently supported so far. With this approach, the calculation effort for motion optimization and path preparation can be reduced. The resulting amount of data decreases significantly as well. Eventually, the advantages of the novel interpolation approach are demonstrated by a process simulation with a validated numerical model.

The production of large components currently requires cost-intensive special machine tools with large workspaces. The corresponding process chains are usually sequential and hard to scale. Furthermore, large components are usually manufactured in small batches; consequently, the planning effort has a significant share in the manufacturing costs. This paper presents a novel approach for manufacturing large components by industrial robots and machine tools through segmented manufacturing. This leads to a decoupling of component size and necessary workspace and enables a new type of flexible and scalable manufacturing system. The presented solution is based on the automatic segmentation of the CAD model of the component into segments, which are provided with predefined connection elements. The proposed segmentation strategy divides the part into segments whose structural design is adapted to the capabilities (workspace, axis configuration, etc.) of the field components available on the shopfloor. The capabilities are provided by specific information models containing a self-description. The process planning step of each segment is automated by utilizing the similarity of the segments and the self-description of the corresponding field component. The result is a transformation of a batch size one production into an automated quasi-serial production of the segments. To generate the final component geometry, the individual segments are mounted and joined by robot-guided Direct Energy Deposition. The final surface finish is achieved by post-processing using a mobile machine tool coupled to the component. The entire approach is demonstrated along the process chain for manufacturing a forming tool.

The flexibility and efficiency in parts production can be significantly increased through the technological cooperation of industrial robots and machine tools. The paper presents an approach in which a robot, in addition to the classic handling tasks, enhance machine tools by additional manufacturing technologies and thus beneficially supports workpiece machining. This can take place in various configurations, starting with pre- and final machining by the robot outside the machine, through sequential cooperative machining of the workpiece clamped in the machine, to parallel, synchronized machining of a workpiece in the machine. The approach results in a novel type of collaborative manufacturing equipment for matrix production that will improve the versatility, efficiency and profitability in production.

Industrial robots and cobots are widely deployed in most industrial sectors. However, robotic programming still needs a lot of time and effort in small batch sizes, and it demands specific expertise and special training, especially when various robotic platforms are required. Actual low-code or no-code robotic programming solutions are exorbitant and meager. This work proposes a novel approach for no-code robotic programming for end-users with adequate or no expertise in industrial robotic. The proposed method ensures intuitive and fast robotic programming by utilizing a finite state machine with three layers of natural interactions based on hand gesture, finger gesture, and voice recognition. The implemented system combines intelligent computer vision and voice control capabilities. Using a vision system, the human could transfer spatial information of a 3D point, lines, and trajectories using hand and finger gestures. The voice recognition system will assist the user in parametrizing robot parameters and interacting with the robot’s state machine. Furthermore, the proposed method will be validated and compared with state-of-the-art “Hand-Guiding” cobot devices within real-world experiments. The results obtained are auspicious, and indicate the capability of this novel approach for real-world deployment in an industrial context.

Decreasing batch sizes lead to an increasing demand for flexible automation systems in manufacturing industries. Robot cells are one solution for automating manufacturing tasks more flexibly. Besides the ongoing unifications in the hardware components, the controllers are still programmed application specifically and non-uniform. Only specialized experts can reconfigure and reprogram the controllers when process changes occur. To provide a more flexible control, this paper presents a new method for programming flexible skill-based controls for robot cells. In comparison to the common programming in logic controllers, operators independently adapt and expand the automated process sequence without modifying the controller code. For a high flexibility, the paper summarizes the software requirements in terms of an extensibility, flexible usability, configurability, and reusability of the control. Therefore, the skill-based control introduces a modularization of the assets in the control and parameterizable skills as abstract template class methodically. An orchestration system is used to call the skills with the corresponding parameter set and combine them into automated process sequences. A mobile flexible robot cell is used for the validation of the skill-based control architecture. Finally, the main benefits and limitations of the concept are discussed and future challenges of flexible skill-based controls for robot cells are provided.

In the era of Industry 4.0 and agile manufacturing, the conventional methodologies for risk assessment, risk reduction, and safety procedures may not fulfill the End-User requirements, especially the SMEs with their product diversity and changeable production lines and processes. This work proposes a novel approach for planning and implementing safe and flexible Human-Robot-Interaction (HRI) workspaces using multilayer HRI operation modes. The collaborative operation modes are grouped in different clusters and categorized at various levels systematically. In addition to that, this work proposes a safety-related finite-state machine for describing the transitions between these modes dynamically and properly. The proposed approach is integrated into a new dynamic risk assessment tool as a promising solution toward a new safety horizon in line with industry 4.0.

Data-driven methods based on artificial intelligence (AI) are powerful yet flexible tools for gathering knowledge and automating complex tasks in many areas of science and practice. Despite the rapid development of the field, the existing potential of AI methods to solve recent industrial, corporate and social challenges has not yet been fully exploited. Research shows the insufficient practicality of AI in domain-specific contexts as one of the main application hurdles. Focusing on industrial demands, this publication introduces a new paradigm in terms of applicability of AI methods, called Usable AI (UAI). Aspects of easily accessible, domain-specific AI methods are derived, which address essential user-oriented AI services within the UAI paradigm: usability, suitability, integrability and interoperability. The relevance of UAI is clarified by describing challenges, hurdles and peculiarities of AI applications in the production area, whereby the following user roles have been abstracted: developers of cyber–physical production systems (CPPS), developers of processes and operators of processes. The analysis shows that target artifacts, motivation, knowledge horizon and challenges differ for the user roles. Therefore, UAI shall enable domain- and user-role-specific adaptation of affordances accompanied by adaptive support of vertical and horizontal integration across the domains and user roles.

In times of societal development, sustainability has become a major concern for many manufacturers in the metal industries. In this context, surface texturing of cutting tools offers a promising approach in terms of reducing energy consumption and material waste. In this work, direct laser interference patterning is utilized for producing periodic line-like structures with spatial periods of 2.0 µm and 5.5 µm on rake-flank faces of cemented tungsten carbide cutting inserts. Structure depths up to 1.75 µm are reached by controlling the applied number of laser pulses. Turning experiments under lubricated conditions carried out on Al 6061 T6 parts with textured and untreated tools are performed to determine their tribological performances. The used textured cutting tools can effectively decrease machining forces up to 17% due to the corresponding improvement in frictional behavior at the tool/chip interface. Furthermore, the laser-processed tools produce thinner chips and decrease the surface roughness by 31% of the aluminum work piece.

Vibrations are limiting the productivity and the process quality of cutting machine tools. For the monitoring of these vibrations, often external sensors, such as acceleration sensors, are used. These external systems require additional cost and maintenance effort. This paper presents a virtual sensor, which is capable of detecting vibrations at the tool center point, based on internal machine data. External sensors are only necessary once for model identification. This reduces the overall cost of the system significantly. The virtual sensor uses the high-quality data of the linear position encoder near the ball screw nut and calculates the vibrations at the tool tip by using transmissibility functions. This paper explains the theory behind the used transmissibility functions and describes how they are measured, by comparing different experimental approaches to identify the modal parameters of cutting machine tools. After the identification of the sensor, a dynamical test cycle is used to prove the physical correctness.

The iterative development process of forming tools is cost- and labor-intensive. The main reason for this is the high complexity of interaction between forming machine, tool structure, and process, which is currently only partially taken into account in simulations and during tool tryout. By digitizing the tool development process, it is possible to save iteration loops during the design phase before tool production and during rework in the adjustment phase. In this article, a novel approach for the consideration of the elastic properties of the clamping surfaces of the forming machine within the forming simulation is presented. A case study on the virtualization of pressure distribution (PD) during the die spotting process and the derivation of recommended actions for the rework based on virtual PDs was conducted. The results demonstrate the potential for saving time, cost, and labor during the tryout process in sheet metal forming.

The high degree of individuality as well as complexity in metal-forming technology is still challenging regarding the development, integration, and operation of cognitive IoT technologies, such as sensors. In particular, the requirements for these systems in terms of robustness and sensitivity are often in conflict and prevent the widespread use of such systems. In this paper, a method for creating digital twin-based virtual sensors is introduced, which can resolve this target conflict. Furthermore, the method is linked to an approach for developing and identifying the digital twin representing the elasto-mechanical behavior of the machine under process condition to sensing technology. The resulting approach is demonstrated by creating virtual sensors to monitor the elasto-mechanical behavior of a servo-mechanical-forming press.

Forming machines and their subsystems, e.g., the drawing cushion in sheet metal-forming machines, pose high power as well as precision requirements in both the positioning and the force, which are directly linked to the quality of the produced part. In addition to ensuring quality, energy efficiency becomes increasingly important. The first step towards energy efficiency is utilizing direct drives, which reduce the energy consumed by the machine significantly, compared to valve-controlled applications. Additional potential for energy loss reduction lies in the implementation of a direct drive unit with the two control variables, motor speed and displacement volume. The control variable distribution results in an influence on the dynamics of the system. This influence is studied in this paper.

Although resistance spot welding (RSW) was invented at the beginning of the last century, the online-monitoring and control of RSW is still a technological challenge and of economic and ecological importance. Process, material and geometry parameters of RSW are stored in the database of the process control system. Prospectively, these accumulated data could serve as the base for data-driven and physics-based models to monitor the spot weld process in real-time. The objective of this paper is to present a finite-difference based parallel solver algorithm to simulate RSW time-efficiently. The Peaceman–Rachford scheme was combined with the Thomas algorithm to compute the electrical–thermal interdependencies of the resistance spot welding process within seconds. Finally, the electric–thermal model is verified by a convergence analysis and parameter study.

Due to the high share of industry in total electricity consumption, industrial demand-side management can make a relevant contribution to the stability of power systems. At the same time, companies get the opportunity to reduce their electricity procurement costs by taking advantage of increasingly fluctuating prices on short-term electricity markets, the provision of system services on balancing power markets, or by increasing the share of their own consumption from on-site generated renewable energy. Demand-side management requires the ability to react flexibly to the power supply situation without negatively affecting production targets. It also means that the management and operation of production must consider not only production-related parameters but also parameters of energy availability, which further increase the complexity of decision-making. Although simulation studies are a recognized tool for supporting decision-making processes in production and logistics, the simultaneous simulation of material and energy flows has so far been limited mainly to issues of energy efficiency as opposed to energy flexibility, where application-oriented experience is still limited. We assume that the consideration of energy flexibility in the simulation of manufacturing systems will amplify already known pitfalls in conducting simulation studies. Based on five representative industrial use cases, this article provides practitioners with application-oriented experiences of the coupling of energy and material flows in simulation modeling of energy-flexible manufacturing, identifies challenges in the simulation of energy-flexible production systems, and proposes approaches to face these challenges. Seven pitfalls that pose a particular challenge in simulating energy-flexible manufacturing have been identified, and possible solutions and measures for avoiding them are shown. It has been found that, among other things, consistent management of all parties involved, early clarification of energy-related, logistical, and resulting technical requirements for models and software, as well as the application of suitable methods for validation and verification are central to avoiding these pitfalls. The identification and characterization of challenges and the derivation of recommendations for coping with them can raise awareness of typical pitfalls. This paper thus helps to ensure that simulation studies of energy-flexible production systems can be carried out more efficiently in the future.

In processing machines, technological tasks are implemented using suitable processing solutions. Those processing solutions can in turn have very different characteristics and specific advantages and disadvantages, e.g., concerning sensitivity to changing operating speed. In state-of-the-art processing machine controls, executing one single processing solution is supported. The execution of various processing solutions together and the combination of their advantages is currently not supported at all. In this article, a motion control approach is discussed that allows combining seemingly incompatible process solutions for a given technological task into a hybrid process solution, using the example of processing machines. The objective of this approach is to increase the achievable process window of the machine in terms of operating speed. It is shown that combining different process solutions can merge their advantages and compensate for their disadvantages. The article brings together the lessons learned from previous work in a new application to exploit advantages and compensate for disadvantages.

With the trend of increasing sensors implementation in production systems and comprehensive networking, essential preconditions are becoming required to be established for the successful application of data-driven methods of equipment monitoring, process optimization, and other relevant automation tasks. As a protocol, these tasks should be performed by engineers. Engineers usually do not have enough experience with data mining or machine learning techniques and are often skeptical about the world of artificial intelligence (AI). Quality assurance of AI results and transparency throughout the IT chain are essential for the acceptance and low-risk dissemination of AI applications in production and automation technology. This article presents a conceptual method of the stepwise and level-wise control and improvement of data quality as one of the most important sources of AI failures. The appropriate process model (V-model for quality assurance) forms the basis for this.

The paper presents a framework for considering resilience as an integrated aspect in the design of manufacturing systems. The framework comprises methods for the assessment of resilience, supply chain and production planning, flexible execution and control as well as modular and skill-based methods for automation systems. A basic classification of risk categories and their impacts on manufacturing environments is given so that a concept of reconfigurable and robust production systems can be derived. Based on this, main characteristics and concepts of resilience are applied to manufacturing systems. As a lever of increased resilience on business and supply chain level, options for synchronized production planning are presented in a discrete event simulation. Furthermore, a concept to increase resilience on the level of business process execution is investigated, allowing manufacturing tasks to be rescheduled during runtime using a declarative approach to amend conventional business process models.

This paper presents a brief introduction to competition-driven digital transformation in the machining sector. On this basis, the creation of a digital twin for machining processes is approached firstly using a basic digital twin structure. The latter is sub-grouped into information and data models, specific calculation and process models, all seen from an application-oriented perspective. Moreover, digital shadow and digital twin are embedded in this framework, being discussed in the context of a state-of-the-art literature review. The main part of this paper addresses models for machine and path inaccuracies, material removal and tool engagement, cutting force, process stability, thermal behavior, workpiece and surface properties. Furthermore, these models are superimposed towards an integral digital twin. In addition, the overall context is expanded towards an integral software architecture of a digital twin providing information system. The information system, in turn, ties in with existing forward-oriented planning from operational practice, leading to a significant expansion of the initially presented basic structure for a digital twin. Consequently, a time-stratified data layer platform is introduced to prepare for the resulting shadow-twin transformation loop. Finally, subtasks are defined to assure functional interfaces, model integrability and feedback measures.

Integrated single-axis force sensors allow an accurate and cost-efficient force measurement in 6 degrees of freedom for hexapod structures and kinematics. Depending on the sensor placement, the measurement is affected by internal forces that need to be compensated for by a measurement model. Since the parameters of the model can change during machine usage, a fast and easy calibration procedure is requested. This work studies parameter identification procedures for force measurement models on the example of a rigid hexapod-based end-effector. First, measurement and identification models are presented and parameter sensitivities are analysed. Next, two excitation strategies are applied and discussed: identification from quasi-static poses and identification from accelerated continuous trajectories. Both poses and trajectories are optimized by different criteria and evaluated in comparison. Finally, the procedures are validated by experimental studies with reference payloads. In conclusion, both strategies allow accurate parameter identification within a fast procedure in an operational machine state.

In motion control, the generation of motion profiles for non-linear kinematics is usually computationally complex. In order to minimize the workload on the machine’s control system, the approach pursued is outsourcing complex calculation tasks to the offline area. In this offline motion preparation, predefined criteria have to be taken into account to guarantee process stability on the real machine. During the motion preparation, a high performance is desired, characterized by less data generated and at the same time little computing effort. The evaluation will use the example of a motion specification, which is characterized by a large amount of data compared to conventional motion specifications. Thus, the demands on performance become even higher. This paper examines the performance of different motion preparation approaches known from literature. On the one hand, selected spline-based algorithms are discussed and compared. A recursive algorithm based on monomial splines is recommended for use in the example. On the other hand, a very simple approach based on the linearization of the non-linear workspace of the mechanism is presented and applied on the algorithms. With this, the performance increased significantly again.

Multi-material jetting (CerAM MMJ, previously T3DP) enables the additive manufacturing of ceramics, metals, glass and hardmetals, demonstrating comparatively high solid contents of the processed materials. The material is applied drop by drop onto a substrate. The droplets can be adapted to the component to be produced by a large degree of freedom in parameterization. Thus, large volumes can be processed quickly and fine structures can be displayed in detail, based on the droplet size. Data-driven methods are applied to build process knowledge and to contribute to the optimization of CerAM MMJ manufacturing processes. As a basis for the computational exploitation of mass sensor data from the technological process chain for manufacturing a component with CerAM MMJ, a data management plan was developed with the help of an engineering workflow. Focusing on the process step of green part production, droplet structures as intermediate products of 3D generation were described by means of droplet height, droplet circularity, the number of ambient satellite particles, as well as the associated standard deviations. First of all, the weighting of the factors influencing the droplet geometry was determined by means of single factor preliminary tests, in order to be able to reduce the number of factors to be considered in the detailed test series. The identification of key influences (falling time, needle lift, rising time, air supply pressure) permitted an optimization of the droplet geometry according to the introduced target characteristics by means of a design of experiments.

The machining of cellular metals has been a challenge, as the resulting surface is extremely irregular, with torn off or smeared material, poor accuracy, and subsurface damage. Although cutting experiments have been carried out on cellular materials to study the influence of cutting parameters, current analytical and experimental techniques are not suitable for the analysis of heterogeneous materials. On the other hand, the finite element (FE) method has been proven a useful resource in the analysis of heterogeneous materials, such as cellular materials, metal foams, and composites. In this study, a two-dimensional finite element model of peripheral milling for cellular metals is presented. The model considers the kinematics of peripheral milling, depicting the advance of the tool into the workpiece and the interaction between the cutting edge and the mesostructure. The model is able to simulate chip separation as well as the surface and subsurface damage on the machined surface. Although the calculated average cutting force is not accurate, the model provides a reasonable estimation of maximum cutting force. The influences of mesostructure on cutting processes are highlighted and the effects in peripheral milling of cellular materials are discussed.

The subject of this splitted article is the commissioning of a new application that may be part of a processing machine. At the example of the intermittent transport of small sized goods, for example, chocolate bars, ideas for increasing the maximum machine performance are discussed. Therefore, optimal process motion profiles are synthesised with the help of a computer simulation. In the first part of the paper, the modelling of the process was shown. This second part focusses on implementing the simulated motion approaches on an experimental test rig, whereby the new motion approach is compared to the conventional approach. Hence, the increasing of the performance can be proven. Eventually, possibilities for an online process control are observed which are necessary to prevent unstable process conditions.

The subject of this splitted article is the commissioning of a new application that may be part of a processing machine. Considering the example of the intermittent transport of small-sized goods, for example, chocolate bars, ideas for increasing the maximum performance are discussed. Starting from an analysis, disadvantages of a conventional motion approach are discussed, and thus, a new motion approach is presented. For realising this new motion approach, a virtual process model has to be built, which is the subject of this article. Therefore, the real process has to be abstracted, so only the main elements take attention in the modelling process. Following, important model parameters are determined and verified using virtual experiments. This finally leads to the possibility to calculate useful operating speed–dependent trajectories using the process model.

The value of data analytics is fundamental in cyber-physical production systems for tasks like optimization and predictive maintenance. The de facto standard for conducting data analytics in industrial applications is the CRISP-DM methodology. However, CRISP-DM does not specify a data acquisition phase within production scenarios. With this chapter, we present DMME as an extension to the CRISP-DM methodology specifically tailored for engineering applications. It provides a communication and planning foundation for data analytics within the manufacturing domain, including the design and evaluation of the infrastructure for process-integrated data acquisition. In addition, the methodology includes functions of design of experiments capabilities to systematically and efficiently identify relevant interactions. The procedure of DMME methodology is presented in detail and an example project illustrates the workflow. This case study was part of a collaborative project with an industrial partner who wanted an application to detect marginal lubrication in linear guideways of a servo-driven axle based only on data from the drive controller. Decision trees detect the lubrication state, which are trained with experimental data. Several experiments, taking the lubrication state, velocity, and load on the slide into account, provide the training and test datasets.

Typically, the feed dynamics of machine tools are limited to reduce excitations of machine structure oscillations. Consequently, the potential increase in productivity provided by electrical direct drives cannot be exploited. The novel approach of the Kinematically Coupled Force Compensation (KCFC) combines the principles of redundant axes and force compensation to achieve an increase in the machine’s feed dynamics. Because the drive reaction forces are directly applied to the machine frame, they cancel out each other perfectly if the relative motion at the Tool Centre Point (TCP) is split according to the mass ratio of the slides. In this paper, the principle of KCFC is introduced briefly and possible improvements in the design of machine structures and control are presented. The results of experimental investigations obtained by means of a 1D-KCFC Test Bed illustrate the effectiveness of the principle. Moreover, a further increase of the compensation quality can be achieved by decoupling the force flow from the machine frame, by means of elastic elements. Finally, an outlook on future research with reference to the 1D-implementation as well as possible applications of the KCFC in highly productive processes is given.