Production at the Leading Edge of Technology

In addition to the efficient use of materials, the significance of information as a resource increase in digital age. Digital tools are seen as a catalyst in generating and processing of digital data and information. One of the main challenges is to find the right information in the increasing oversupply of information as well as to manage this resource sustainably throughout the entire product life cycle. Misinformation is identified to be one of the main drivers of inefficiency in an engineering activity and is influenced by quantity and quality of data and information.To further reduce this inefficiency, the study presents a conceptual framework that pursues the approach of demand-orientation as well as the smallest possible and appropriate quantum of data and information. Senior experts confirm the conceptual framework in semi-structured interviews. The outlook refers to use cases for investigation in a multi case study.

When implementing machine learning (ML) methods, for example, in the context of quality control in production, a major concern for small and medium-sized enterprises (SMEs) is the effort, time, and thus cost required to train a powerful ML model. Since nowadays most manufacturers already have computer-aided design (CAD) models of their products, using these for training an ML algorithm could be beneficial. In this paper, the performance of the YOLO Object Detection Algorithm is evaluated on images directly rendered from a CAD model in Autodesk Inventor and real-world images of the same product. The main goal is to reduce the number of real-world images needed to train the model using CAD-Renderings. This is especially useful before the start of production since real-world images are not available at that point. This will continue to enable faster adaptation to new products and thus drive digitization in companies.

The importance of sustainable production is growing all the time. Lightweight design of production equipment is a possible measure to increase the sustainability. These potentials are often not used to their full extent due to a high focus on investment costs. To resolve this target conflict, this paper presents the lightweight design of production equipment based on a systematic development process and the corresponding digital toolchain. It includes a newly lightweight design technology planning tool to optimize cost, CO2 and weight from a production perspective. Based on a rough description of the component and previously modelled requirements an optimized shape, production process and material are recommended. Subsequently, the component can be further detailed with this knowledge. Through the digital toolchain the effects of different designs on the usage of the production equipment can be easily estimated. This enables the developer to identify the potential of lighter but more expensive components.

In the EU, 75% of buildings are energy inefficient, emphasizing the priority of their renovations or upgrades to reach the EU’s decarbonization goals. However, renovating existing buildings frequently entails physically demanding tasks, which can result in worker injuries, reduced productivity, and health issues.This document highlights the potential of using exoskeleton technology in collaborative tasks. These approaches’ effectiveness was assessed using two methods: electromyography (EMG) to assess muscular activity levels and a real-time motion tracking system to observe object manipulation.A muscle activation assessment was conducted on ten participants, analyzing four muscles across two distinct phases of the collaborative task. The results indicate that 55% of these cases displayed positive changes marked by a reduction in muscle activity when exoskeletons were employed. In 27.5% of the cases, no significant changes were observed; while 17.5% resulted in unfavorable outcome.

Due to the digitalization of production, a vast amount of data is being generated that, through analysis and processing, offers new opportunities for implementing efficiency improvements. To process the data in real time, existing computers at the edge of production networks can be interconnected to form an edge computing cluster to distribute additional tasks among these devices. On the shop floor, satisfying latency requirements is necessary. A distribution model to optimally distribute tasks to existing computing resources is presented. The distribution model relies on a model of the network formed by the distributed edge computing nodes and their connections. Both computing nodes and their connections are characterized with various attributes. A metaheuristic approach is used to solve the optimization problem of the best distribution of tasks regarding latency and reliability.

Artificial intelligence (AI) has already been the subject of extensive scientific and industrial research in order to solve current challenges in intelligent manufacturing. However, these approaches often do not yet reach industrial small and medium-sized enterprises (SMEs). To meet the demand for resilient and sustainable production processes, the aim is to develop concepts to transfer applications into cyber physical production systems. Therefore, AI methods can be introduced as part of a smart manufacturing chain. In this work, an infrastructure to efficiently handle production data which is independent of the individual production process such as machining, grinding or electrical discharge machining is presented. Subsequently, an exemplary roadmap for SMEs is given to establish AI applications within the scope of manufacturing industry 4.0. As a result, an increase in efficiency and profitability of individual production processes as well as the overall production chain can be achieved.

The clinching process in sheet metal production is affected by variations caused by material and production factors such as batch inconsistencies, non-uniform preconsolidation of sheet materials, and tool wear. These variations lead to fluctuations in important quality parameters such as undercut, neck, and bottom thickness, resulting in inconsistent joint quality. These inconsistencies can negatively impact the load-bearing capacity of a clinched joint. The research aims to enhance process monitoring by incorporating the recording of geometric joint characteristics. This will be achieved by using a rapid in-situ simulation during the joining process to determine the resulting quality parameters of the joint. A quick calculation model based on linear-elastic FEA will be developed and coupled with a semi-analytical approach to correct nodal displacements, thereby minimizing deformation energy in the system. This approach significantly reduces calculation time, as only a linear-elastic “forward” calculation is performed, without iterative loops. This novel process monitoring will enable medium-term control of clinching processes, laying the foundation for self-learning processes and intelligent process control.

Clinching is a representative of the mechanical joining processes defined by form and force closure. Traditionally, clinched joints are tested destructively, for example using shear tensile tests and metallographic sections. Transient dynamic analysis (TDA) offers the possibility of rapid non-destructive testing in addition to the established methods. TDA can be used during and after joining as well as during operational loading. In previously performed investigations TDA was carried out at discrete points and allowed the detection of defects in clinched joints. Utilizing a new approach, continuous measurement is made possible. First, the characteristic frequencies of the specimens are determined. The specimen is then excited harmonically with one specific determined characteristic frequency and the response is evaluated. The investigations were carried out on clinched shear tensile specimens made of aluminum. This allows the detection of emerging defects my means of TDA during the ongoing experiment.

The progressing digitization in the manufacturing industry and the enhancements in the field of machine learning (ML) offer new approaches for monitoring manufacturing processes. In this context, the image classification using ML methods represents a well-solved task that can be leveraged for evaluating processes and products. From this perspective, this paper presents an application of ML methods in order to classify defective deep drawn parts. It proposes a methodical approach which includes not only the data acquisition using a low-cost equipment, but also the data pre-processing as well as the setup and training of the ML model. The described method is applied on an academic part of rectangular shape. After collecting a dataset of nearly 1000 images, a convolutional neural network was trained to identify three different quality classes. The trained model was then evaluated on a test dataset containing 30% of the samples and shows an accuracy of 96,77%.

The prediction of workpiece quality in process planning, using machine learning models, is a common-researched topic. Until now, trained models were static and could not update themselves with new data. However, this aspect is crucial when considering the continuously changing manufacturing circumstances in regards to new process parameters, materials, and workpiece geometries. In addition, repeatedly training process models with an extended mixed dataset decreases the prediction quality due to the increased data divergence. This paper presents an approach to automatically generate sub-models, which maintain the prediction quality even if novel data is considered. The challenge is to define the amount and content of these sub-models through clustering. Tool grinding experiments will be conducted with different process parameters, materials, and workpiece geometries in order to obtain a divergent dataset. Subsequently, cluster approaches are compared to obtain dynamic growing models, which enable optimized planning for a more resource efficient process. Finally, the method will be generalized in order to ensure a process-independent usage.

Aircraft assembly involves many high-precision rivet holes, with around one-third drilled manually by experienced workers. Wireless electrical machines are increasingly replacing pneumatic drilling machines to reduce energy consumption. Nevertheless, the lifespan of the cutting tools varies between 20 and 70 drill holes in carbon fiber reinforced polymer (CFRP) depending on the individual experience and constitution of the worker, resulting in a large waste in cemented carbide. This article proposes a method that uses the internal sensors of the drilling machine to predict tool wear during manual drilling. The main challenge is the rapid wear of the tools and the unknown feed rate, which depends on the individual workers’ constitution and experience. By predicting tool wear, this method can help reduce waste and improve the efficiency, sustainability, and precision of manual drilling operations in aircraft structural assembly.

Wear monitoring is a critical aspect of maintaining the health and performance of machinery. During centerless grinding, grinding and regulating wheels as well as work rest blades wear out over time due to contact with workpieces, which impairs the geometrical accuracy and surface quality of the processed parts, and ultimately the productivity of the process. Acoustic emission sensors are a promising source of information for wear monitoring which in turn allows to optimize dressing intervals and prevent rejects. During an experimental series, acoustic emission signals were collected from a centerless through-feed grinding process to identify changes in the signal that could be indicative of wear. The collected acoustic emission signals were preprocessed by digital filtering and extracting a comprehensive set of features. A one-class support vector machine was used to quantify the signal evolution over the span of the experimental series. The resulting resemblance of the signal evolution to the grinding wheel wear effects observed in the geometric properties of the workpieces from the series suggests the validity of this approach.

The use of machine control data for process monitoring has many advantages. It represents a cost-effective alternative to expensive external measurement devices and can be retrofitted to existing CNC machines. External sensors can be affected by the machining environment, require setup, and are costly to procure. Furthermore, they influence the process since they are part of the machining process because they replace either the workpiece clamping technology or the toolholder. Machine control data such as spindle currents are in proximity to the process without directly affecting it. Since these data are already known to the NC, they can be used for adaptive control applications. This paper focuses on investigating the differences in position control data on three different machine tools. Using the same reference process. Correlations between feed forces and motor currents are calculated and compared. Correlations of motor currents between different machine tools performing the same process are analysed.

The ability to control product quality in milling processes is an essential target variable. Quality deviations can have different causes, which can be both systematic and stochastic. Tracing data from numerous sensors of modern machine controls and combining them with a material removal simulation (MRS) allows to predict the workpiece quality in a process-parallel manner. The accuracy of the prediction depends on the underlying model and its parametrization. Model parametrization usually requires measurements during which the machine tool cannot be used. Additionally, the necessary measuring equipment is often not available in companies. All this makes the implementation of MRS difficult in the manufacturing industry. This article presents an approach that circumvents the abovementioned disadvantages. With the help of a MRS, machine data is refined and contextualized with quality data. The refined and contextualized data is then used to fit various models, such as stiffnesses and process force. In future the presented approach should enable a cost-effective parametrization without machine downtime. With the parameterized models, MRS that predict quality can be used both in advance during CAM planning and in a process-parallel manner for quality monitoring.

Monitoring solutions like simulation models and digital twins enable methods like predictive maintenance, cutting down on idle time and improving the effectiveness and lifetime of machine tools. Creating these models for brownfield equipment is therefore sensible from both an ecological and economical view. Designing them, however, is a time-consuming process that requires a lot of expertise. Automating this has the potential to increase both the number and the quality of models. This paper presents an approach to automatically identify the parts of the feed axes of machine tools based on reference runs. For this, the control signals of exemplary machines are analyzed in order to develop a rule-based system to differentiate variations of parts. Furthermore, approximations of certain system parameters like gear ratios are determined. This constitutes a first step towards fully automated generation of functional models.

Both conventional processes such as internal turning and special processes such as bottle boring do not allow complex internal machining of small bores at large depths. For this reason, a new type of tool system is being developed for internal contouring of small diameters even at large machining depths. In a first development step, preliminary investigations of internal machining by means of internal turning were carried out to characterize the existing limits and to select suitable cutting inserts and cutting parameters. This was followed by the development, design and manufacture of the tool system, which was initially designed for guide bores with a diameter of D = 14 mm and a length-to-diameter ratio > 20. The development of the tool head was implemented using finite element analyses.

The application of sensor technology for monitoring and analysing manufacturing processes is becoming increasingly important and is well established for manufacturing processes with geometrically defined cutting edges. However, in grinding processes such as internal grinding, the use of sensor-based tool holders reaches its limits. Especially in these demanding processes, the use of sensor technology offers a high potential for increasing manufacturing accuracy, performance and process reliability. In this context, a demonstrator of a tool holder for high-speed applications with integrated measurement technology has been developed. In experimental investigations, the tool holder has been qualified for internal grinding processes. It has been possible to perform reliable high-speed internal grinding processes even at high tool speeds of up to 30,000 1/min.

The aim of this study is to use a Shapley value-based machine learning algorithm to investigate the cross-process chain impacts of the processes involved in a pinion shafts manufacturing chain on final gear quality with sustainability considerations. Process data is collected during gear hobbing and profile grinding and analyzed using a neural network model to calculate Shapley values. The results quantify the influence of process parameters and trace data of each process on the gear quality, thereby improving understanding and leading to better control and sustainability of processes within the manufacturing chain. By achieving better process control and evaluating sustainability, this study not only helps to save time, energy, and material resources but also promotes sustainable manufacturing practices that minimize waste and the environmental impact of gear production. Overall, this study provides valuable insights into the importance of processes in the gear manufacturing chain, aiding in its optimization.

Climate change and progressing urbanization urge to increase sustainability in all areas – not only, but especially in manufacturing. Digitization and particularly digital twins enable a more efficient, decentralized, and small-scale production. This helps companies to reduce the usage of natural resources and the overall environmental impact. Corresponding modular digital twins represent one configuration and must be configured specifically according to individual production orders. The determination of required type, amount and linkage of integrated data and partial models of the production processes and plants is complex. However, this determination is required to choose suitable tools for the digital factory and to adapt processes accordingly.The objective of this paper is to provide a concept for a modular digital production twin with integrated partial models as enabler for sustainable value creation. The concept will be concretized by means of requirements from an urban environment.

In modern markets, increasing quality requirements necessitate high performance quality assurance processes to guarantee the fulfillment of these requirements sustainably. Thus, the quality assurance must be able to take targeted countermeasures which efficiently correct process deviations. In this context, quality control loops play a major role for quality monitoring and control. Currently, however, a great deal of manual effort goes into the design and implementation of mostly knowledge-based control logics. A heterogeneous data landscape and the resulting data preparation processes cause high effort. Autonomous quality control loops represent a new development and are intended to provide an efficient and data-based approach to setting up quality control loops. As an enabling step for autonomous quality control loops, this paper discusses the development of an Asset Administration Shell based, standardized quality data model.

Vertically articulated robot arms (VAR) are highly flexible and cost-effective robots, representing the largest market share of industrial robots. Due to their kinematic structure, they are highly flexible but suffer from low repeatability. As a result, VARs are rarely used for precision assembly tasks, as expensive and complex compensation mechanisms are required. To enable these applications, a deeper understanding of the influences on their repeatability is required. Existing research largely analyzes the influencing factors in isolation, however, comprehensive systematic studies are lacking. Therefore, this work investigates a systematic analysis of the influences on the repeatability of VAR. To this end, a comprehensive experimental design was created and repeatability experiments were carried out using a KUKA-LWR4+. Influencing variables considered include, among others, the robot’s pose, thermal effects, backlash and external moments. Finally, practical recommendations for the use of VAR in repeatability critical processes are given.

The aim of this paper is to investigate the effects of different nozzle exit geometries in the FLM process on the deposition of curves and the interconnection of multiple lanes. To investigate the effects of the different nozzle exit geometries, curves were printed with different nozzles, as well as compounds of lanes next and on top of each other. Three unusual exit geometries, triangle, square and hexagon, were compared with the usual circular nozzle shape. Furthermore, the effects of a rotating nozzle were investigated when printing the curved lanes. The results show that the hexagonal exit geometry is the only exit geometry that produces an almost constant profile contour independent of orientation, which strongly resembles the profile contour produced with a circular exit geometry. Both, the triangular and the square exit geometry, exhibit strongly differing profile contours, due to the orientation during the curved deposition process. The evaluation of the multilayer specimens underlines the influence of the exit geometries on the contact areas, void sizes, and the distortion of the profiles.

Manufacturers of industrial robots rarely specify their products with reliable parameters for position accuracy and repeatability. Often the specifications only refer to individual axes or provide too little information about the distribution of the measured values. The ISO 9283:1998 “Manipulating industrial robots – Performance criteria and related test methods” provides instructions for the measurement of performance criteria, but is usually not applied. This paper introduces ISO 9283 with a focus on accuracy, repeatability and position stabilization properties. The standard is illustrated by measurements on a cartesian precision robot of the type Autoplace 411 (Unitechnologies SA). Based on the experience and the measurement, the authors discuss the advantages and disadvantages of the classification according to ISO 9283. Finally, we present proposals on how performance criteria can be defined more appropriately.

The amount of end-of-life products is increasing globally. Especially Waste of Electrical and Electronic Equipment (WEEE) is growing with more than 50 million metric tonnes annually. Effective treatment of end-of-life products aims to prevent landfilling and reduce the need for primary resources to manufacture new products. To contribute to sustainability targets, recycling must be energy- and resource-efficient. Industrial Cyber-Physical Systems are mechatronic systems that intend to improve efficiency within industrial infrastructure through near real-time digital modeling and simulation. In addition, coupling multiple Industrial Cyber-Physical Systems offers further potential to increase the overall system effectiveness by connecting and aligning digital models or simulations from different disciplines toward a common goal. The paper describes the concept of Industrial Cyber-Physical System of Systems and derives prospects and future research directions for its application within battery value networks.

To ensure the longevity of the bearings of a motor spindle, it is advantageous to know the precise loads on the bearings during operation. Since sensor-based monitoring involves a great deal of effort due to the limited space available, and simulating the bearing load is not real-time capable, we investigated how the bearing loads can be estimated using machine learning methods. To estimate the bearing load, a co-simulation was first set up that generates large amounts of training data based on measured cutting forces and spindle vibration velocities. Measured and simulated quantities are then used to train artificial neural networks. The best-performing neural networks can estimate the surface pressure between rolling elements and bearing rings with an error of less than 2%. This deviation refers to the contact stress that was calculated for comparison with the simulation results.

In the past decades, machine tools have been optimized for productivity, which has led to high feed rates and accelerations of drives. These high accelerations cause peak loads in the power grid. The electrical infrastructure for the machine supply must be designed to handle these peak loads even if peak loads occur simultaneously on several machines working in parallel. By reducing the resulting overall peak loads in the grid, components can be designed for lower currents and thus more resource-efficiently.In this study we investigated how total peak loads in a shared DC-grid can be lowered by synchronizing high-load-operations. It is possible to slightly delay individual load-intensive NC steps via OPC UA so peak loads of the individual machines no longer occur simultaneously. The approach is implemented on two machine tools. In a field test we achieve a peak load reduction of up to 8.3%. In a simulation, we show that a reduction of almost 20% can be achieved by extending the approach to a network of six machine tools.

Static configurations and slow adaption to changing requirements characterize today’s production systems. In order to cope with variable external influences, these systems need to become more flexible. One basic building block to achieve more flexible automation systems from the software perspective are modular real-time architectures which are realized as distributed real-time systems. The improved modularity and flexibility come at the price of greater complexity. In order to enable developers to design robust systems regarding real-time properties, a domain specific language is proposed, which allows reasoning and validation of modules and composed distributed real-time systems.

Data-based services play a major role in reaching sustainability targets as they contribute to optimizing the use of production resources and enable sustainability improvements in production processes. The development of data-based services poses a challenge for companies of the manufacturing industry due to lack of knowledge regarding value creation and top management involvement. This paper provides a view on related work on development processes for data-based services and sheds light on proposed development methods. Interviews are conducted with large sized companies of the manufacturing and logistics industries to qualitatively investigate practices and lessons learned regarding the development of data-based services. Applying Gioia methodology, the paper derives recommendations for data-based service development and proposes implications for further research on the involvement of top management and the use of value co-creation.

The linear economic model of the manufacturing industry requires a fundamental transformation towards a circular economy. To support this transformation in machinery and plant engineering, transparency about the environmental impacts along the entire product life cycle is necessary. Since more than 80% of the environmental impact is determined during product development, it is important to create a basis for decision-making that makes the consequences of different circular economy strategies visible and clarifies the sphere of influence of design decision. In this paper, an ecology analysis for machines and plants is introduced, taking into account different circular economy strategies, in order to enable a holistic comparison of product and process variants. For this purpose, the relationships between ecology, product life cycle and product architecture are investigated. A mathematical calculation procedure of the interrelationships is presented to determine the main levers for improving the overall sustainability of machines or plants.

The global earth overshoot day occurs earlier each year, highlighting the fact that industry and society consume more resources than the Earth can regenerate within a year. Nevertheless, the current focus on combating climate change is primarily on renewable energies and energy efficiency measures. To address resource scarcity as well, a shift from a linear economy to a circular economy is necessary. The adoption of circular strategies, which aim to minimize waste and maximize resource utilization, provides not only ecological benefits but also economic advantages. However, only a small number of manufacturing companies have fully integrated the circular economy into their business units. To identify the associated barriers and challenges, an exploratory literature review was conducted. Based on this, the critical capabilities of circular business modelling, circular design, transition management, circular networking as well as digital transformation were developed, to pave companies the way towards a circular economy.

A necessary precondition for a sustainability transformation is creating transparency about the environmental impact of products along their entire life cycle. However, possibilities for influencing sustainability are not directly apparent for product development using existing ecology metrics or methodologies such as Life Cycle Assessment, especially when dealing with complex machines and plants. This is a major challenge because up to 80% of a product’s environmental impact is defined during development. Therefore, this paper presents a methodology for structuring and selecting ecological indicators for the sustainability assessment of products within product development. Thus, a systematic literature review was conducted to identify relevant ecological indicators and elaborate a generic ecology structure. Simultaneously, the life cycle of machines and plants was analyzed and a generic process structure was developed to describe it from product development’s perspective. By identifying the relationships between processes and ecological indicators, a product-specific selection of indicators is made possible.

Ramp-up phases right at the beginning of batch processing in cold-forging of high volumes are accountable for a significant amount of scrap due to transient process conditions. To understand the emerging impacts of transient effects during ramp-up phases on the final dimensional product properties more deeply, process conditions like forming load, part temperature and part geometry were monitored exemplarily in this study.Using measured data will enable the application of a sensor-based actuator control system to adjust some process parameters in cold forging.A newly developed adjustable wedge device is introduced allowing for continuous adjustment of the punch in-between subsequent strokes and a precise positioning of the punch in the working direction. Therefore, a reliable way to control transient process conditions is proposed, aiming to reduce the amount of scrap production and shorten the ramp-up phase timewise.

Damage that exists in the form of microvoids in a material can be influenced by various process parameters during forming. Depending on these parameters, the static recrystallization (SRX) occurring during the holding time between two hot forming steps can also lead to a reduction in damage. Therefore, this work aims to investigate the influence of SRX on ductile damage after hot compression forming in the case hardening steel 16MnCrS5. First, stress relaxation tests are conducted to characterize the kinetics of static recrystallization in the material. Based on that, hot compression tests are performed at different temperatures and holding times. The void area fraction (VAF) after the hot compression and consecutive static recrystallization is determined based on combined image processing of scanning-electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). The results show a decrease in VAF with an increasing recrystallized volume fraction, which indicates a positive effect of static recrystallization on void healing.

7xxx series aluminium alloys have a high potential for lightweight constructions in automotive applications due to lower weight, similar strength and higher recyclability compared to steel. However, formability of such alloys at room temperature is relatively low. Elevated temperatures are one way of improving formability, e.g. by semi-hot forming. This work is therefore concerned with the analysis of strain rate and temperature dependent behaviour of AA7075 in T6 state. For this, experimental characterisation has been performed using uniaxial and shear tension, layer compression, plane strain compression and Nakajima tests over temperatures of 20 to 275 ℃ and strain rates of 0.01 to 1 s−1. The experimental results are used to calibrate and build a temperature and strain rate dependent plasticity model using Hockett-Sherby hardening law. For failure modelling, a temperature dependent forming limit curve was calculated. Additionally, various yield criteria were applied to predict a temperature dependent yield locus for a constant strain rate.

Lightweight aerospace structural components are usually milled out of plate-material, converting up to 95% of the material into difficult to recycle chips. Therefore, additive-manufacturing holds great resource saving potential through the near-net-shape production of thin-walled parts. However, these parts pose new challenges. For example, previous methods such as the “waterline”-path approach, which uses the residual stiffness of the solid-block to reduce the deformation of the thin-walls due to process forces, can only be applied to a very limited extent. This results in a greater deflection of the near-net-shape, additively-manufactured structure. In this paper the surface error is investigated using milling experiments. An analytical approach to qualitatively predict the surface characteristics is provided. The results get used to discuss whether an adjustment to the tool path is suitable to compensate for the dimensional error. Such a compensation method has the potential to efficiently machine near-net-shape semi-finished parts within the required tolerances.

Waste products from glass-fibre reinforced polymers (GFRP), which are used for wind turbine rotor blades, represent a substantial environmental and disposal challenge. According to the state of the art, thermal or chemical recycling methods require high energies and resource consumptions. Based on a new recycling approach, this study analyses the potential of mechanical recycling as a sustainable alternative for the management of GFRP waste. This paper presents a novel solution for mechanical recycling of GFRP waste and analyses the mechanical properties of recycled GFRP materials manufactured with mechanical recycled and processed GFRP waste. The study investigates the impact of particle size classifications on the mechanical properties of recycled GFRP materials, providing insights for optimising the recycling process. Tensile strength tests were carried out to measure the Young’s modulus E and ultimate tensile strength Rm to compare the properties of recycled GFRP specimens with newly manufactured GFRP materials and thus identify the suitability of these materials for specific applications. Furthermore, the GFRP material samples were analysed using computer tomography (CT) to determine the fibre lengths lf in order to analyse their influence on the mechanical material properties. First results showed a correlation between the particle length lp and the ultimate tensile strength Rm of the investigated GFRP material samples. The mechanical recycling approach enables more sustainable GFRP materials compared to the current state of the art, which significantly reduces the environmental impact of GFRP waste disposal.

Ultrasonic vibration-assisted machining (UVAM) is one of the most promising innovations in high precision machining of advanced materials such as titanium alloys for aerospace applications or silicon carbide materials for the semiconductor industry. UVAM improves the process efficiency by increasing tool life through lower cutting forces and temperatures. However, in vibration-assisted milling applications, it is necessary to isolate the spindle system from the vibrations induced by the oscillation actor in order to maintain the long-term accuracy of the machine. This paper presents an innovative approach based on the integration of high damping materials into the toolholder to improve its damping capacity while maintaining the necessary stiffness. Therefore, the tool holder was developed with cylindrical damping inserts in a radial pattern with shrink fit connections. According to the results it was shown that the damping capability of a conventional toolholder can be improved without unacceptably reducing its radial stiffness compared to a standard toolholder geometry.

Hybrid models which combine data-driven and physics-based models have emerged as a trend in manufacturing for improving process efficiency, reducing costs, and enhancing product quality. Hybrid models can deliver greater performance benefits than either model by itself. As sustainability becomes an increasingly important issue for the manufacturing industry, hybrid models show potential to assist the transformation of manufacturing processes towards increased sustainability. Hence this paper investigates the potential for reducing resource and energy consumption through the application of hybrid models, based on a use case of polishing stoneware tiles. A comparative analysis between a physics-based and a hybrid model-based approach is presented. For both approaches, the ratio of defective parts is calculated. Furthermore, the environmental impacts are estimated with a life cycle assessment. The findings suggest that the hybrid model-based approach enables reductions in energy and resource consumption, thereby improving the sustainability of the manufacturing process.

The increasing importance of sustainability leads to changing conditions and new requirements for the manufacturing industry. Thereby, the concept of the circular economy plays a decisive role in meeting these challenges. Continuous Innovation (CI) focuses on the ability of a company to continuously improve or renew products or features during the product usage phase. Thereby, it addresses the growing demand for a circular economy through the upgradability of the products, while at the same time increasing customer satisfaction. Although manufacturing companies are aware of the opportunities, the implementation is currently not successful. This paper attempts to close this gap by introducing artefacts for the implementation of CI for technical products. Thereby, the systematic identification of the artefacts focus on the dimensions product, process, business model and organization. Thus, the identified implementation artefacts support manufacturing companies in the implementation and operative realization of continuous innovation.

Increasingly difficult technical differentiation from the competition and growing demand for integrated customized solutions require companies to make strategic adjustments to their portfolios and business models. Due to the increasing possibilities of service and software applications in the provision of solutions, a holistic view of the portfolio is required, in contrast to the widespread focus on physical products. The transformation into a solution provider can only succeed through systematic combinations of product elements with value-added services to create customized solutions. These contribute to longer, more frequent and more intensive use of the physical product, as this is tailored specifically to customer requirements, and thus also to better resource utilization through a longer life cycle. Therefore, this paper presents a methodology that enables the planning of hybrid portfolios consisting of products and value-added services in machinery engineering. Furthermore, a procedure is described how customized solutions can be created under complexity-reducing aspects.

The cold atmospheric plasma metallization (CAPM) with copper is an alternative coating process to electroplating for applying a thermo-mechanical buffer onto bare dies to enable more powerful top-side interconnection technologies for power electronics. A complete life cycle assessment (LCA) is to be performed in order to evaluate improvements to increase sustainability of the copper metallization. Since life cycle assessment results strongly depends on the set system boundaries and availability of primary data, the scope of this study is determined by the availability of the latter. Since the coating properties are dependent on the plasma spray parameters, a variation of set parameters is assessed, which are currently used for the coating of the bare dies. The evaluation showed that electricity, plasma gas and powder production have major impacts on the sustainability. The necessary heat treatment is responsible for on third of the emissions if bare die production and plasma coating are included into the assessment. Potential improvements are the reduction of total conveyed powder and carrier gas, as well as increasing the number of bare dies treated in one annealing process.

The established process chain for fabrication of titanium powder for additive manufacturing (AM) consists of the production of titanium sponge, the forging of electrodes and the atomization. A high energy demand of about 85% of the overall process energy consumption is taken by the production of the electrodes, resulting in a high carbon dioxide footprint and costs. This paper introduces a novel process chain to recycle titanium chips from milling and powder residues directly for the use in fabrication of electrodes. A methodology to increase the recyclability of the chips by adapting the process parameters of the milling process is shown. Cleaning and compaction to electrodes by utilizing hot isostatic pressing enables the possibility to use titanium chips and powder residues as base material for atomization. By using this novel recycling process, the energy demand for the production of titanium powder can be significantly reduced by approximately 72% in comparison to standard powder production.

Flexible and reconfigurable production systems are composed of modules which fulfil a defined functionality and can operate within predetermined use cases. Some of these modules are fully integrated cyber-physical systems, others require electrical components located in control cabinets. Exchanging or adapting their functionality is limited, complex and typically requires manual effort due to hard-wired hardware and application-specific software. A new concept for control cabinets, designed as cyber-physical systems and based on reconfigurable hardware (FPGA) with flexible interfaces, is presented in this paper. The new approach overcomes existing limitations, offers more adaptability, reusability and extends the lifetime of the modules. Furthermore, the concept allows a seamless integration in a software-defined manufacturing environment. First results and experiences are based on a servo-controller which is extended with new features for an adaptive PWM by following the new design approach.

Disassembly is an important first step for new and innovative end-of-life solutions for critical components and materials on the way to a circular economy. Generating possible disassembly sequences and finding optimal sequences for a given disassembly target is a major challenge in disassembly. Using electric traction motors as an example, this paper presents an approach to automatically generate possible disassembly sequences and calculate the optimal path to a given disassembly state. This is based on geometric data in the form of polygon meshes enriched with additional information about non-geometric joints (e.g. adhesive joints). Geometric joints are automatically detected by analysing the contacts between parts. All parts, joints and resulting constraints are modelled in an undirected graph. By analysing the joints and movement constraints, a directed graph is generated. This graph contains all possible disassembly sequences, which can then be searched for an optimal sequence.

Rare Earth (RE) Magnets are crucial for the green transition of the European Union (EU). This applies especially for e-mobility where RE magnets are used for highly efficient traction drives. Permanent magnetic motors offer the best efficiency, but the use of Rare Earth Elements (REEs) is related to several drawbacks. The magnets and the required raw materials are almost exclusively imported. This causes problems for European industry due to supply chain risks and continuously increasing prices. In addition, primary production is associated with a poor ecological footprint. Therefore, the EU has declared REs as critical raw materials with highest supply chain risk and plans a compulsory recycling share of 15% until 2030. Currently, no industrially applicable processes exist for systematic recovery of RE magnets. In this context the following paper should present approaches for an automated process chain starting with an end-of-life traction drive until the extracted and demagnetized magnets.

The increase in demand for computational power along with society’s reliance on proprietary processor architectures, raise concerns about processor sovereignty and prompts the exploration of open standard instruction set architectures (ISAs) such as RISC-V. Concurrently, the impact of e-waste in the environment and society’s resource consumption highlights the need for circular economy principles and sustainable production methods within the electronic industry. In this study, computational power, open standard ISAs are explored to survey recent hardware implementations, particularly of RISC-V processors with a focus on power efficiency gains. We argue that integrating open standard ISAs, circular economy strategies, and sustainable production practices may contribute to resilient and responsible products. This approach addresses some of the questions in resource sustainability and invites further exploration of the potential of open-source processor implementations to improve the way that electronic devices are produced and consumed.

This paper presents a general concept for an in-line quality control system and the basis for component pairing using the example of a pressure valve. The aim is to improve the functional quality of the product while increasing dimensional tolerances to reduce waste. The proposed system requires an end-of-line (EOL) functional test and collects pre-existing sensor data from the production line. This data is used to train machine learning models to identify correlations between measurements and EOL test results. The system uses this information to predict future EOL test results. Anomaly detection and root cause analysis is performed by comparing predicted results with actual measurements. To improve the data set, additional sensors are integrated into the identified production steps. Once parameters with a high influence on the product function have been identified, these should be used to find ideal pairs of components with favorable parameter combinations in order to improve functionality. The EOL test is then used for validation.

In order to enhance resource efficiency and minimize waste in production, effective planning and monitoring of material flow is crucial, which can be facilitated by material flow simulations. There are various material flow models on different scales with different advantages and disadvantages. With a significant quantity of piece goods, transitioning or switching between different model scales becomes essential for balancing accuracy and computational efficiency. However, determining the appropriate switching points in space or time can be challenging. This paper presents a signal-based methodology for identifying appropriate switching points between different models, which can be customized by the user. We concentrate on the switch from a physics-based model on a micro scale to a flow model on a macro scale. By defining switch conditions based on necessary information, signals can trigger switches between models. The proposed approach provides a flexible solution for simulating complex material flow systems with different scales.

Global production networks have become a dominant feature of the global economy, with companies relying on dispersed and closely linked production activities in different regions. However, various disruptive events have highlighted the vulnerability of such networks. Companies that are robustly positioned against these events have a significant competitive advantage. To understand this strategic advantage better, a systematic literature review is necessary to compile elementary factors that describe the resilience of networks and identify further research gaps. The main objective is to identify (1) the central weaknesses in global networks, (2) how the vulnerability of the network can be determined and (3) which approaches make global production networks more resilient. The paper provides a comprehensive analysis of the current research and highlights the implications for global manufacturing companies.

Globalization has led to cost efficient but highly fragile value chains in industrial manufacturing, as demonstrated by the corona pandemic. This paper proposes a rethinking of value chains towards software-defined value networks (SDVN) to achieve resilience advantages in a VUCA world. To unveil these advantages, a holistic view of the causes of global value chain disruptions was set up. In a novel approach, an initial set of causes was extracted from ChatGPT and combined, structured, and expanded with an automated analysis of news and research literature. On this basis, levels of SDVN adaptivity and their corresponding application-oriented requirements are deduced. From this, current barriers in digitalization at the value-added level are derived. To reflect the contribution made by current developments, these are assigned to the barriers. Finally, open research questions towards the transition to SDVN are identified. The result holistically shows how the flexibility, redundancy, scalability, and data efficiency of SDVNs lead to success.

In recent years, unforeseen and rare events in the environment of manufacturing companies have been a dominating influence in production planning. Examples are dynamic product demand developments due to the COVID-19 pandemic and geopolitical conflicts or changes in production resource availabilities due to technological disruptions and shortages. In addressing these unforeseen events, large changes in the production systems can cause errors or disruptions raising the need for the inclusion of risk management in production planning. In order to insure applicability for unforeseen and rare events, risk management approaches in production planning require realistic and objective risk assessments. This paper conducts a literature review for risk management in production planning with the aim of assessing the state of the art in regards to realistic and objective risk assessment and concludes with the recommendation for further consideration of rare and unexpected events in future methodologies.

Rubber extrusion processes involve a high number of control parameters and batch-dependent material fluctuations, leading to lengthy and error-prone testing cycles for new products. To address this challenge and enable a more sustainable production, an artificial neural network-based data mining algorithm is developed that identifies correlations between process parameters and product characteristics. This approach allows for consideration of a larger quantity of non-linear correlations that improve with each new application, enabling more precise temperature control of rubber production. Real-world manufacturing data are used to validate the model, which incorporate batch- and recipe-dependent material variations. The results provide new insights into the complex relationships between process parameters and product characteristics, highlighting the potential of data mining algorithms to drive sustainable production in the rubber extrusion industry. This paper presents the iterative development of the data mining algorithm for rubber extrusion lines and demonstrates its practical application in the industry.

The growing demand for lithium-ion battery cells, driven by increasing electrification in daily life, is currently met by inflexible mass production plants using rigidly linked manufacturing lines. These are set up in large dry rooms to protect moisture-sensitive battery materials. Since up to 30% of the total energy demand is caused by the operation of these dry rooms, the agile and customized battery cell production is based on compact local dry rooms, also called microenvironments. This publication presents the development and validation of a diffusion-tight transport box that enables the transportation of moisture-sensitive intermediate products between these microenvironments. The flexible and configurable material flow can incorporate alternative process routes. Furthermore, a modular expansion of the production system is enabled. By combining reusable diffusion-tight, automatically handled transport boxes and driverless transport systems, this approach enables an agile material flow in battery cell production.

Regeneratively fed Direct Current (DC) grids play a key role in the energy transition. The promising supply architecture for industrial plants is not only materially efficient in its topology, but it also distributes energy without large conversion losses, especially for electric drives. While the architecture already offers a particularly efficient integration for renewable energies, the natural fluctuation must be mastered in energy management. Depending on the orientation, dimensioning and geographical conditions, this paper describes an approach and implementation of power forecasting for photovoltaic (PV) systems based on numerical weather predictions. Furthermore, the associated energy management scenarios for storage and loads in DC-supplied production plants are presented. The advantages of energy forecasting lie in the optimal design of the DC-Grid components, the improvement of peak shaving and the early energy optimisation of production control through connection to a Manufacturing Execution System (MES).

Stringers in aircrafts have a longitudinally variable, load-adapted material thickness, which has been realized to date by chemical milling. A new flexible rolling process for T-profiles is being developed to enable more environmentally friendly and resource-efficient production of stringers in the future. The tailored distribution of wall thicknesses in the T-profiles in the longitudinal direction requires geometries of rolls that enable uniform rolling of the web and flange. This can be achieved by rolls with varying radii. However, due to the varying roll radii, varying longitudinal strains are introduced across the profile cross-section, which in turn lead to unwanted profile bowing.This paper presents the basic idea of a new flexible rolling process for T-profiles - Flexible T-Profile-Rolling. Numerical investigations with different parameters of the roll circumferential speed and the roll gap are performed. The results show that accurate and straight T-profiles with longitudinally variable thicknesses can be produced by the proposed process.

The development of components with tailored properties is one of the ways to increase efficiency in areas such as the automotive sector. Hybrid components, in particular plastic-metal composites, meet this requirement by combining different material properties. In this context, this paper presents preliminary investigations regarding the use of fibre-reinforced plastics as an active medium for forming metallic materials. Several challenges arise for such a processing method, such as, the compressive strength of the plastics depending on the fibre fraction, -length and -orientation. Furthermore, the dissimilar elastic behavior of the two materials has a decisive influence on the composite- and bond strength. Therefore, in a first step, a material selection was performed, followed by material characterization. In addition, this paper presents a preliminary numerical analysis showing the feasibility of manufacturing a hybrid component using as example a spur gear with a polymer core made of glass fibre reinforced polypropylene and an outer geometry made of aluminum alloy AA6082.

Reducing plastic waste is an important challenge in the consumer goods industry´s transformation towards greater sustainability. Natural fibre-based materials such as paper can already partially substitute plastic-based packaging. Shells with wrinkle-free sealing areas, as needed for gastight packaging, can be achieved by active media-based forming. Nevertheless, the specific material properties of paper, such as limited plastic elongation or anisotropy, pose challenges regarding the products quality. The evaluation of the geometric characteristics of formed paper products and how they can be adjusted by appropriate selection of process parameters has been mostly empirical.This paper presents an approach for the quantitative evaluation of the product quality in the active media-based forming of paper. The methodology is applied to different materials, material conditions and process parameters to evaluate the factors influencing the products geometry. A systematic evaluation of the experiments reveals that using material-adapted process control enables a significant increase in geometric flexibility.

Hot-stamped ultra-high-strength steels, as established in automotive body-in-white, have already been considered for add-on parts of commercial vehicle chassis frames such as rear and front underride guards. Replacing thinner hot-formed steels for the fine-grained structural steels with plate thicknesses of up to 9 mm usually used in this area offers enormous potential for weight-savings. In order to fully exploit these advantages, the substitution must be extended to other components, such as load-bearing structures. For this purpose, the material has to meet high fatigue strength requirements. Therefore, a modified hot forming process is presented which allows the adjustment of the advantageous fatigue properties of the manganese-boron steel 22MnB5. In this paper, different heat treatments are investigated to produce favorable reference manufacturing parameters in terms of fatigue strength. The material samples produced by different heat treatments are tested for their fatigue strength. In addition, the influence of sandblasting on the fatigue strength is investigated.

Hot forming processes involve several interactive material phenomena. This includes transformation-induced plasticity (TRIP), which significantly affects the resulting deformations and residual stresses of the workpieces. The magnitude and orientation of TRIP strains are dependent on stress states that occur during cooling, making it essential to consider TRIP effects in process design. Material parameters required for modelling this were previously recorded and validated through experiments on AISI 52100. In this study, a material model based on this data is used to investigate a forged component by predicting the distortion occurring over the course of die forging, deflashing, and cooling. By comparing simulations with and without TRIP effects, the study demonstrates the potential for resource-efficient design of forging process routes, minimising scrap due to distortion and maximising material savings, up to the limit of a distortion-free component.

Multi-resistant bacteria pose a global threat, as infections can lead to life threatening conditions. In orthopaedics, implant surfaces are critical for infection prevention. Implants with a fine-grained surface are a promising way to achieve antibacterial properties by inhibiting biofilm formation.In this paper, the continuous manufacturing process for fine-grained materials called Equal-Channel-Angular-Swaging (ECAS) is applied to the most commonly used medical titanium alloy Ti-6Al-4V ELI. First, the homogeneity of the shear strain (which is crucial for grain refinement) is investigated in a numerical simulation under different process parameters (e.g. feedrate, counter pressure, number of passes). In the ECAS process, the material fails due to shear bands. Using the process parameters found in the simulation, damage free forming is possible. The material properties are discussed using microstructural analyses, hardness measurements and tensile tests.

During forging, tool wear occurs as a result of thermomechanical stress. In addition, the deterioration of material behavior due to an overheated surface zone leads to reduced tool service life resulting in higher production costs. Heatpipes can be used to dissipate heat from the loaded tool surface area and thus optimize the material behavior of the tool. The performance of this method is strongly influenced by the heat conductivity between tool and heatpipe. To evaluate this novel method, the influence of the force-fit connection between heatpipe and tool on the thermal load is investigated during a forging process. In addition, the influence of the connection surface finish has been investigated by varying the roughness of the contact surface. Reference tools without heatpipes and with loose connection of the heatpipes were used for comparison. All tools were tested with 1,000 strokes in a fully automated forging process. The die temperatures were recorded to evaluate the resulting wear behavior of the tools. Based on the tests, reduced wear was observed using the heatpipes applied.

In forging, premature die failure caused by high cyclic thermo-mechanical loads leads to higher production costs. The die life can be increased by using high strength materials like nickel-based alloys (Inconel). However, the application of Inconel as die material is challenging due to its high costs and poor machinability compared to conventional tool steels. Therefore, an approach to produce hybrid dies consisting of hot working tool steels and Inconel according to the Tailored Forming concept is presented. The dies are manufactured by forging pre-joined semi-finished workpieces. To resolve challenges like different flow behaviours, first a material characterisation of two hot working tool steels (1.2343/1.2367) and two Inconel alloys (625/718) was carried out using uniaxial compression tests. Based on the determined mechanical properties suitable process windows were investigated and FE-simulations for the hot forming of hybrid Inconel and tool steel semi-finished workpieces were carried out to investigate the resulting material distribution.

An accurate understanding of the loads on the active parts of highly loaded forming tools is crucial for the robust design and subsequent optimisation of the tools. This publication presents a numerical simulation-based methodology for reliably predicting the load and structural behaviour of hot forming tools, addressing the challenge of process-related mechanical-thermal loads.In this approach, the first step is to determine the mechanical and thermal loads acting on the active tool parts during the forming process by means of process simulations. Subsequently, the effects of these mechanical and thermal process loads on the deformations and stress distributions in the forming tool are investigated by means of a coupled thermomechanical simulation. The simulation results are validated using real tests. The developed methodology based on the numerical approach shows good agreement with the results of the real experiments and can be used for topology optimisation and design of the cooling system.

Multi-material structures are promising in the automotive industry for achieving lightweight design body construction and functionalization due to their favorable mechanical properties and low structural weight. These structures typically combine metal and plastic materials to create final components with enhanced properties compared to mono-material structures.The focus of this paper lays on the creation of a manufacturing cell that combines joint forming and heat-assisted press joining techniques to produce components made from steels and continuous fiber-reinforced thermo-plastics in the form of unidirectional carbon-fiber tapes. To improve production efficiency and reduce costs, a manufacturing cell was created and tested, which utilized two robots for automated handling and an isothermal, two-stage forming tool concept to shorten cycle times. The resulting composite components were tested for mechanical performance. Compared to pure steel components, all composite components exhibited a higher specific load capacity. Cycle times in well under 60 s were achieved. The new manufacturing cell led to a significant reduction in process time compared to variothermal tool concepts.

By using high-strength aluminum, weight savings can be achieved. However, limited formability of these alloys at room temperature is a challenging aspect regarding the manufacturing of components with filigree functional elements within sheet-bulk metal forming. So as to derive detailed process knowledge and to improve the achievable process results, forward extrusion of geared components made of EN AW 6082 sheets at room temperature is investigated within this work. Furthermore, robust large-scale industrial cold forming is a demanding task, as tool temperatures increase over time due to forming and frictional heat, leading to varying material and process properties. In order to determine measures for the compensation of these fluctuations, additional experiments are carried out with slightly increased tool temperatures. By analyzing workpiece and process related parameters for both sets of experiments, potential approaches to control the outcome of forming operations under variable conditions are discussed.

Deep-drawn components are found across all industries. With respect to the challenges of manufacturing towards a more climate-friendly and resource-efficient industry, various approaches such as lubricant-free forming, are being investigated. Deep drawing with macro-structured tools is a key approach in this regard. In addition to the expansion of the process window, other benefits can be achieved, such as the improvement in resource efficiency resulting from the reduction of induced residual stresses and the saving of lubricant.The investigation of the sheet thinning and drawing force in connection with the macro-structure with the intention to elaborate the sensitivity of the parameters is the subject of this work. Within a systematic investigation, the influence of a changing wavelength λ along with the associated immersion depth δ of the macro-structure is examined. Concluding, a contribution to the design methodology of the macro-structured tools based on the usable process window is presented.

High dynamic wood processing is manufacturing of sustainable resources at its limits. Facing challenges to determine the influence of minor tool and clamping imbalance regarding the surface quality of wood machining and structural behavior of the machine tool, this article is presenting an approach to evaluate and classify the impact of tool imbalance at high speed rotation.Therefore, a variety of process scenarios and tool configuration are investigated. To quantify the surface-quality several state of the art measurement systems and evaluation methods are used.Finally, it is possible to connect the structural surface information with the balance factor and implement routines to measure natural massive wood surfaces.

Milling is one of the most productive manufacturing processes for creating high quality surfaces. This process is divided into different types of milling operations, which differ in terms of cutting geometry and process kinematics. For NC-programming, the planner must take all of these into account in order to select the most appropriate type of operation. Most of the restrictions are linked to the topology of the surface and the shape of the overall component. Choosing the right type of finishing is crucial for automated planning of milling operations. This paper presents an approach for this. By considering the local geometry and global accessibility, it determines the technically possible types of finishing operations for any point on a surface. This creates the informational basis for the automated determination of processing types for entire surfaces and components. Conceivable applications are automated process planning (CAPP) or automated NC-programming.

In context of industrial mass production, innovative machining technologies offer a potential for significant gain of efficiency and sustainability. Whereas in conventional tapping processes the tool moves in and out in sync with the thread pitch, the kinematics of the novel helical thread forming technology allows to reduce the process time by half. This paper is about an extended process analysis of its specific process characteristics in contrast to conventional tapping. By high-speed-microscopy and advanced analysis of mechanical tool loads, it is possible to evaluate specific differences in the forming process of the thread profile. In context of sustainable production, aluminum cast alloy AlSi10Mg – which represents a recyclable material for near-net-shape components in mobility sector – is regarded in this study. In addition, the resulting quality of the produced M6 threads is evaluated in context of decreased production time and aspects of sustainability.

The machining of wood and wood-based materials is becoming increasingly interesting in view of the progressive application of this class of materials in various industrial sectors, such as furniture, doors, windows, interior fittings and sustainable construction in the building sector. In order to promote the establishment and further development of wood construction in particular, it is necessary to research and develop approaches to solutions in the field of machinery, processes and tools in order to optimize manufacturing processes. In the industrial environment, wood and wood-based materials are usually processed dry. Unfavorable process parameters often lead to thermal problems, which can have a negative impact on the machining qualities. In contrast to dry cutting, there is hardly any scientific knowledge available for wood machining using minimum quantity lubrication (MQL). In this paper, the results on the influence of the use of MQL when sawing solid wood are discussed. Within the framework of experimental investigations, various process parameters were varied and the effects on tool temperature and tool wear were analyzed.

In the context of resource efficient production, forming technologies such as cup backward extrusion become increasingly more important. The substitution of conventional materials by materials with similar mechanical properties and high lightweight potential, for instance the aluminum alloy EN AW 6082, leads to new challenges in the automotive industry. In order to avoid high economic damage caused by the adhesion tendency of aluminum alloys, which subsequently leads to increased tool wear, the tribological system must be optimized.Through drop shape analysis, the influence of the wetting behavior of the lubricants Gardomer L6261 and Gardolube L301/1 on workpieces, which were shot peened using different materials, on the punch force during cup backward extrusion was investigated. In addition, visioplasticity tests were employed in order to understand the change in flow behavior in different tribological systems. It was shown that both suitable lubrication as well as workpiece structuring positively influence the tribological system.

Nowadays, companies face challenges such as globalization, individualization and shorter product lifecycles, resulting in a constant stream of new product development processes (PDP). Modularized product families represent a powerful concept for reducing complexity and increasing resource efficiency in the PDP and beyond. Despite existing approaches and methods in the development of modular product families, there are deficits in the state of the art regarding their transfer and application to the engineer-to-order (ETO) sector, as well as for neutral indicator-based evaluation. Therefore, this paper derives a generic modularization procedure for the ETO sector and verifies it in an industrial use case. For this purpose, a heuristic swapping algorithm has been developed for grouping the components of a product family into clusters and enabling an objective mathematical evaluation. By integrating modular product structures into organizational processes, ETO manufacturers can strengthen their competitive position as well as increase their resource efficiency.

For a sustainable production and increasing energy demands, physically smaller and more efficient heat exchangers are required. Hence, one challenge is to increase the surface area to volume ratio, therefore enabling the production of finer structures. Additive Manufacturing (AM) enables the efficient production of advanced heat exchangers. Especially Laser Powder Bed Fusion (PBF-LB/M) increases in industrial interest with continuously growing readiness level. The structure size in PBF-LB is limited by melt pool size and directional inhomogeneities, using equal strategies as for bulk solid components. To overcome this, the scalability potential and alteration of a single contour strategy was investigated on 316L to compensate geometric deviations during design phase. This also enables the possibility to explicitly adjust boundary parameters for compensating the directional overmelting of individual inclined struts. Thus, this approach showed a viably strategy to reduce geometric inaccuracies, enabling a predictable manufacturing of homo-genous microlattices.

Cement accounts for a large proportion of global CO2 emissions, which can be reduced by prefabricating light weight modular concrete structures. For such modular construction methods with high accuracy requirements and rapid construction the separation of planning and production is futile due to occurring deviations in geometry, time, process and material. The processes must be connected in order to be able to compensate for deviations. For this purpose, a reinforcement learning agent can be used, which dynamically assigns modular concrete structures to a curing chamber and their processing time. In contrast to most approaches for RL in the literature, the considered problem is located at the interface between production control and process control. Thus, this paper presents a framework that outlines the use of RL in this problem domain by describing the simulation model, the actions of the RL agent as well as a possible state space and reward function.