Production at the Leading Edge of Technology

In the hot-stamping process, a sheet blank, usually a manganese boron steel like 22MnB5, is heated up to the austenitising temperature in a roller hearth furnace and then formed in a cooled forming tool. This leads to tensile strengths of about 1500 MPa in the formed parts. The components produced in this process are used in particular in the automotive body construction. The roller hearth furnaces used require high investment costs, much space, are maintenance-intensive and have a low efficiency. As an alternative to roller hearth furnaces, resistance heating offers significantly higher heating rates (>100 K/s) and consequently an energy-efficient, cost-effective process for heating electrically conductive materials. The sheet material 22MnB5 is conventionally coated with an AlSi layer, specially developed for hot stamping and the purpose of scale protection. Therefore, the coated blanks must be heated for several minutes to achieve a sufficient intermetallic phase. Within the framework of SFB 1368, an experimental chamber was developed in which an uncoated 22MnB5 sheet metal is simultaneously heated and coated without scale in a nitrogen-silane atmosphere by means of resistance heating. In the process, nitrogen displaces the regular atmosphere in the test chamber. Subsequently, silane reacts with the residual oxygen, resulting in an oxygen-free atmosphere. The Ni700 coating material used is nickel-based and is specially designed for the high heating rates of resistance heating. The investigations prove that for the production of hot-stamped components, coating in an oxygen-free silane atmosphere during resistance heating is possible.

Lightweight construction continues to be of outstanding importance for many industrial sectors, and often relies on ribs or stringers to stiffen flat surfaces. Stringer sheet forming provides an efficient path to produce stiffened parts avoiding the disadvantages of milling or casting these structures. However, due to the deep penetration welding process, the first part of the process chain, stringer sheet parts are limited in terms of material choice and surface quality.This paper presents an approach to overcome both limitations by employing a fillet laser welding process to produce the semi-finished parts. It leaves no welding seam on the outer side of the stringer sheet, significantly increasing surface quality and therefore allowing application as a visible part e.g. in car bodies. The process furthermore allows for the fabrication of aluminum stringer sheet parts which can be formed within an acceptable forming process window. These parts are found to be equally efficient concerning the stiffening effect when compared with steel stringer sheets. In conclusion, fillet welding is found to be the superior option concerning surface quality, material and weld seam strength.

This paper describes the investigation of a compression molding process for the production of a battery housing structure made of glass mat reinforced thermoplastic (GMT) for electric vehicles. The battery housing structure was scaled to a demonstrator geometry in the form of a battery shell. To enable variant-flexible manufacturing, a modular forming tool was used, which can be modified by interchangeable inserts. The process chain involves the heating of the GMT, the transfer to the isothermally heated forming tool and the compression molding. The influence of various process parameters such as stamping force, stamping speed, GMT temperature and the GMT quantity on the mold filling were experimentally and numerically investigated. Finally, the results were transferred on different geometries where a complete mold filling was achieved.

Machine hammer peening (MHP) is a cold forming process for incremental finishing of metallic surface layers. The impact energy of the electrodynamic MHP results from conversion of electrical energy into mechanical energy and finally from energy transfer to the workpiece. The interactions between the impact energy and the material properties determine the resulting surface layer properties. Thus, the knowledge of the kinetic impact energy is crucial for defined process control. Nonetheless, the kinetic impact energy is difficult to determine due to complex influences of electrical and mechanical parameters during energy conversion. Therefore, the objective of this work is to identify the correlations between the impact energy resulting from electrical and mechanical influences. For this purpose, an electrodynamic MHP system was characterized experimentally and a databased multi-physical kinetics model was developed. By using machine-learning methods, the model enables the prediction and targeted design of impact energies depending on MHP process parameters.

Magnesium is classified as lightweight material and as biomaterial because of its low density and good biocompatibility and biodegradability in the human body. It is therefore expected to be applied as microforming technical components and medical engineering products. Because of its close-packed hexagonal lattice structure, Magnesium and typical magnesium alloys such as AZ31 are known to have low ductility and poor formability in cold forming processes.Therefore, dieless drawing with local heating by a high frequency generator offers an alternative processing opportunity for magnesium alloys such as AZ31. The dieless drawing can result in high reductions in the cross sectional area in a single pass by using a local heating source, which initiates a localized plastic zone under an external tensile load.For this purpose, a flexible experimental setup for a dieless wire drawing process is designed and manufactured. First experimental analysis with AZ31 wires are carried out in order to analyze the feasibility of the setup. The process parameters drawing speed, feeding speed and temperature are analyzed to achieve a uniform reduction in cross sectional area and therefore stability within the local deformation zone. First process limits are detected for dieless drawing of AZ31 wire.

For electronic components, micro parts are produced in large quantities. Hence, in modern production lines constantly increasing stroke rates are targeted to reach high productivity. The used foil material is usually fed by roll feeding systems, which limit the feeding rate and potentially cause damage to the foil’s surface. A promising alternative is a feeding concept based on the operating principle of a linear induction machine in which the feeding force is applied in the form of Lorentz Force.In this paper, a simulation study is presented, investigating the potential of the abovementioned feeding principle. This includes a parametric simulation study on the design parameters of a demonstrator to evaluate their influence on the resulting feeding force. In case the device is handling pre-processed foils, the potential feeding force can be affected by the processed material. Thus, the application of the electromagnetic acceleration on variously shaped cutouts in foils is investigated.

The aim of minimizing scrap material while increasing product diversification at the same time requires more efficient solutions for production processes. Due to longitudinal strains introduced during roll forming processes, deviations in straightness occur. The lack of universally valid rules for the setting correcting equipment leads to iterative approaches, wasted material and production downtimes. To enable maximum efficiency and minimum waste of material a targeted process control is needed.In this paper, a novel methodology for straightening profiles is presented. Process-related inhomogeneous longitudinal strain distribution in the cross-sections are levelled due to partial strip rolling. For this purpose, two hypotheses are investigated. The first hypothesis postulates that the relative longitudinal strain distribution over the profile cross-section is decisive for deviations in profile straightness. The second hypothesis states that deviations in straightness can be eliminated by specifically adjusting the longitudinal strain distribution. In the first step, numerical parameter variations are used to specifically provoke profile defects and longitudinal strain distributions. In the second step, a novel method for levelling the induced longitudinal strain distributions is developed, in which selected cross-section segments are partially rolled out. To enable targeted rolling out, the correlations between the longitudinal strain and the profile defects must be known. For this purpose, the influences of selected parameters and a quantitative correlation between the longitudinal strain and the profile defects are shown in numerical investigations.

Incremental sheet forming with active medium (IFAM) is a modification of the well-known single point incremental forming (SPIF) and allows convex forming operations by applying pressure of an active medium on the bottom surface of the blank. Both convex and concave forming can be combined to a multi-stage forming strategy when the part is turned between these two forming steps. Experiments on a truncated pyramid are conducted to evaluate the influence of the two-stage forming strategy on the geometrical accuracy in comparison with an equivalent single-stage forming strategy. The findings indicate that the height of the convex preform has a major impact on the final accuracy. If the height of preform amounts 75% of the target height, the undesired bending in the flange region is significantly reduced and can be explained by a decreased vertical forming force. This effect leads to an increased geometrical accuracy in contrast to single-stage SPIF.

By applying the rules of technical origami, flat raw materials can be folded into cellular structures. This also includes semi-finished sheet metal materials. Such folded sheet metal structures can be used as multifunctional core materials for sandwich-structured composites, offering outstanding strength and stiffness properties at comparatively low component weights. Applications of this type of sandwich-structured composites comprise, for example, optically appealing claddings, heat exchangers and, in particular, lightweight constructions.With regard to the production of such folded sheet metal structures, previous research work has shown that the desired shape can only be achieved when the bending axes are pre-shaped before folding. Thus, during design phase of component and process design, deformation and strain hardening effects of this pre-shaping must be taken into account. In this respect, the present paper deals with the evaluation of different forming methods for the pre-shaping of folding axes into sheet metal materials. As methods for applying appropriate grooves, roller pinching and embossing are investigated by means of experiment and simulation.Finally, this paper shows that embossing and roller pinching disclose highest potential for processes for the pre-shaping of grooves required prior to the folding of multifunctional core materials for sandwich-structured composites.

The focus of this study is to improve the clinching ability of an aluminum die casting alloy with a sheet metal by local annealing in order to clinch them in a process-safe manner. For this purpose, the influence of three different heating strategies (heating plate, resistance heating and inductive heating) on the generation of clinching joints are investigated. Furthermore, the strategies are compared with regard to process time, joining point formation and bonding strength. Finally, the different heating strategies were evaluated and the most promising process parameters were identified.

Functional lightweight design is an essential factor in production processes in terms of resource efficiency and environmental considerations. Optimized material utilization with the usage of the finite element method (FEM) or adapted tools for improving the material flow in forming processes repeatedly push process limits and production advances into new unknown fields. One possible approach to improve standard deep drawing processes is the use of macro-structured tools, as investigated by Mousavi et al.Investigations onto deep drawing with macro-structured tools are carried out regarding the effects of the draw-in behavior of the blank and the resulting residual stresses in the final component. Tests were performed on a T-cup tool, which serves as a demonstrator for industrial components. The components are characterized in terms of geometrical accuracy and resulting residual stress distribution in the wall and bottom area. Parts manufactured with a standard tool and a macro-structured tool are compared for this purpose. Finally, an assessment regarding effects of macro-structured tools on the process and the resulting component properties and especially the resulting springback behavior due to the induced stresses is given.

Proton exchange membrane (PEM) fuel cells generate electric current by means of the reactants hydrogen or methanol and oxygen, emitting only heat and water or water vapor. Fuel cells therefore represent a possible technological option for a CO2-neutral energy supply. A core component of fuel cells are bipolar plates (BPP) which are stacked alternately with membrane electrode assemblies (MEA) to form fuel cell stacks. The BPP must ensure leakage-free supply of the fuel gas and oxidation gas to the MEA and removal of the reaction products (electric current, heat and water). One approach to produce thin BPP sheets with high dimensional fidelity at a high quality and dimensional accuracy is hot pressing of graphite compound which, however, offers only limited productivity due to long process times. This paper presents a proof of concept for pressing thin BPP made of highly filled graphite compound, with wall thicknesses down to 0.3 mm, at a low temperature. For the experimental investigations, a multi-part press tool was developed for bipolar half plates (BP-HP) with a size of 540 × 140 mm and designed for high performance applications. The various challenges for forming with regard to material, BPP and die design as well as first pressing parameters such as pressure and temperature were investigated in a proof of concept.

The die roll height and width are quality-determining attributes of fine blanked parts that reduce the load-bearing capacity of functional surfaces. Depending on the blanked part requirements, a sheet thickness allowance is necessary to compensate the surface reduction, which must be removed by a downstream process step. Combining flat coining and fine blanking into a progression process offers the possibility of specifically influencing the material properties of functional areas of the blanked part. Due to an influence of the strength on the die roll formation, a local strain hardening of the shear zone has the potential to reduce the die roll and consequently the post-processing effort. Hence, the influence of pre-hardening by upsetting on the fine blanking process for case hardening steel 16MnCr5 was analyzed in an experimental investigation. It could be demonstrated that the die roll formation is significantly reduced due to near-surface strain hardening in the area of the shear zone.

Tailored Forming provides an innovative process chain for manufacturing hybrid components. This study focuses on a process design of a hybrid bearing bushing consisting of aluminium alloy EN-AW-6082 and Titan-Grade-2 or -5 using numerical simulation. The manufacturing process used was a closed die forging process with pre-joined hybrid semi-finished products in a radial arrangement. Due to the simultaneous forming of two different materials, a fundamental knowledge about the flow properties of each material is necessary to achieve a stable forming process. For this, a material characterisation by means of uniaxial compression tests was carried out. The aim was to identify the temperature range in which the flow properties of aluminium and titanium converge. These results were used in a numerical study of the forming process of a hybrid bearing bushing. In a pre-step heating simulation, a suitable inhomogeneous temperature distribution of the hybrid semi-finished product was calculated to achieve the desired temperature field. Subsequently, a numerical study of a closed-die forging process for the manufacture of a hybrid bearing bushing was carried out. A comprehensive stress analysis was carried out to identify a suitable initial temperature distribution to avoid the occurrence of high tangential stresses, which can lead to cracks during the forming process. The results of the presented numerical study will be used in further experimental investigations to improve the presented forming process.

Modern production chains are subject to constantly growing demands for agility, adaptability, variant diversity and robustness. These demands can push even sophisticated systems to their limits. For product validation during development, as well as for disturbance compensation, the macroscopic paraffin wax phase change actuator (PCA) is a proven tool. The PCAs also operate in highly rigid systems with achievable actuating forces of more than 85 N per square millimeter of effective actuator area. In addition, the particularly compact housing offers high integration capability. However, the low dynamics due to the low thermal conductivity of the paraffin wax is challenging. Response times of more than 20 min are often not acceptable to counteract process fluctuations, e.g. due to material influences. Therefore, performance improvements for the PCA are presented that use internal structures to significantly reduce thermal paths. As a result, the response time can be drastically reduced and additionally the actuator force is increased by 60%. Furthermore, the influence of additives in the paraffin wax is discussed.

Computer aided fixture design (CAFD) allows engineers to prototype quickly; however, it does not yet provide a reliable prediction of the system’s dynamic behavior. A desired design could be intelligent fixture, with the challenge being to make it even smarter. Thus, this research aimed to identify factors that affect the dynamic behavior of the workpiece-fixture system, and therefore need to be integrated into simulation models. The effects of factors such as locator positions, clamping force, clamping sequence, and others were studied. Results showed that the position of the fixture elements has the largest effect on system dynamics behavior. Selecting the optimum positions ensures a higher natural frequency while also it determines the optimal clamping force and points. Based on these results, CAFD models should consider the dynamic behavior during planning and allow testing it in the verification stage.

The continuous development in the tool and mould making and aero-space industry requires advanced materials which are processed specifically by electrical discharge machining (EDM). The removal of debris from the working gap is of utmost importance to prevent arcing and short circuits, causing higher tool wear and lower material removal rates. Besides passive flushing, manually aligned side flushing nozzles are used in practice to ensure sufficient flushing in deep or large-scale cavities. This paper presents an approach of combining numerical simulations and statistical methods to reduce the number of required computer experiments and calculate optimized side flushing configurations for maximized volume flows. As a result, the number of simulations required to investigate a large range of parameters was reduced by a factor of 106, allowing 108 more combinations to be investigated. The results showed good conformity in terms of increased experimental material removal rate at configurations with increased simulated volume flows.

The tool heating associated with machining processes causes thermal expansions of the tool structure, which reduces the machining accuracy. Due to increasing demands on the resource-efficiency of production, the use of cooling lubricants to compensate for thermally caused tool structure deformations is to be reduced or even completely avoided. To maintain the required manufacturing tolerances the development of a correction method on the control-side was carried out, which calculates a correction value for thermally induced expansion using the temperature curve of the tool structure. Therefor a sensor system, attached to a customary milling chuck, which is capable to measure several chuck-temperatures, was developed in this paper. To enable in-process measurement, the sensory chuck was equipped with wireless communication and energy transmission. The recorded temperatures can be used to calculate a correction value for the thermal linear expansion of the tool structure, which is used to increase the machining accuracy.

Due to its stochastic nature and complex interdependencies maintaining overall process stability and efficiency are challenges in electrical discharge machining (EDM). Extensive research is necessary to further understand the underlying physical phenomena. A signal analysis of gap voltage and discharge current enables the detection of certain gap phenomena and offers a deeper insight into the EDM process. However, resulting from discharge frequencies in the upper kilohertz range the sampling of the signals results in large amounts of data. To reduce this data, this paper presents signal analysis methods implemented on a single-board-computer to offer a cost effective and efficient way of characterising sinking EDM processes. With these analysis methods EDM processes are characterised regarding discharge energies and occurrence of different discharge phenomena. The findings presented in this paper give a deeper insight into the sinking EDM process.

Predictive maintenance of machines promises their operators the possibility of avoiding unexpected machine defects and thus, of increasing the availability of their plants. High expectations of the potential of predictive maintenance have drawn both users and suppliers of machines and systems to deal intensively with the subject. However, only a few component suppliers have established development efforts or a marketable offering of predictive maintenance solutions in their own product portfolio.Protective covers of machine tools are used to shield vulnerable machine components from harmful influences from the machining area. Currently, they are either replaced after damages are evident and the machine itself may already be damaged or periodically, which means wasted wear reserve. The use of predictive maintenance promises to solve this existing conflict of objectives while reducing unexpected downtime.This paper examines the development and implementation of an ML-based predictive maintenance solution for an existing protective cover. The implementation and testbed-operation show the validity of the presented approach.

The efficient execution of demanding deep hole drilling operations represents a major challenge for manufacturing companies. The ejector method makes the advantages of deep hole drilling, such as high material removal rate and bore quality, usable for the industry on conventional machining centers. Thus, no expensive deep hole drilling machine with complex sealing system is necessary. The ejector effect is mainly responsible for a stable deep hole drilling process by supplying the working zone with cooling lubricant. In order to realize an enhanced fundamental understanding of the fluid flow in the tool with its process-typical peculiarities, a demanding experimental setup for in-process fluid pressure and volume flow measurement is developed. Based on the results a simulation model is developed with the help of the mesh-free Smoothed Particle Hydrodynamics (SPH) method. The measurements done in the experimental investigations are used as input data for the model generation and the adjustment of the SPH adaptivity.

Advances in digitisation bring new potentials for process analytics and optimisation in machining. New edge-level data acquisition systems provide high-frequent data streams from machine tools. This raw data needs to be preprocessed, structured, and enriched with domain-specific context to become usable information and ready for analyses. Generally, this means challenges for data processing and data management. Due to missing established technologies in machining production, leading to high effort for data analysis, most of the information from the production processes is lost today instead of being stored in a structured way. The special focus within this paper is on the context-specific preparation and processing of the data streams. A concept has been implemented, which allows the fusion and storage of internal machine tool data with additional external sensor data streams and metadata of the production process. Additional data from up- or downstream processes can be joined automatically into the presented data structure.

Conventional turning results in surfaces with a twisted surface structure. However, for some applications of machine parts, twist-free surfaces are recommended. These surfaces can be obtained by grinding or turning with special one-purpose machine tools. Alternatively, a proprietary start-stop turning method can be used and applied on multi-purpose turning machine tools. In order to manufacture twist-free surfaces with start-stop turning, a conventional lathe is equipped with an additional short-stroke air bearing drive system. A series of experiments with specimens of hardened steel is performed and the effects of the process parameters $${n}_{v}$$ n v (number of workpiece revolutions in the stop-phase) and $${v}_{Hub,v}$$ v H u b , v (short-stroke velocity in the positive direction of the machine tool feed motion) on the surface properties are investigated. The results show that the surface structure and the so called transition area lengths can be influenced by the settings of process parameters $${n}_{v}$$ n v and $${v}_{Hub,v}$$ v H u b , v . The findings can be used to predetermine surface structures for machine parts and applications such as surfaces with demanding requirements regarding tribology.

To improve the wear resistance of cutting tools, PVD (physical vapor deposition) coatings are used. Nevertheless, the demands on the coatings are challenging. One reason for this is a higher request on economical machining of Ti6Al4V. Ti6Al4V is a difficult to machine material. Its material properties lead to high mechanical loadings and early tool failure during machining. Usage of self-lubricating PVD coatings leads to a decrease of thermomechanical loadings in the contact between tool and workpiece. Part of this study is the analysis of the diffusion and oxidation behavior of CrAlVN coatings with varying Cr / Al ratio. Therefore, heat treatments at ϑ = 600 °C, ϑ = 700 °C and ϑ = 800 °C in ambient atmosphere with Ti6Al4V counterpart were conducted. vanadium oxides were detected by Raman-spectroscopy. The formation might be dependend on the chemical composition. Additionally, tribological tests against Ti6Al4V were carried out for one coating system.

BTA deep hole drilling is used to produce bore holes with high bore hole qualities and high length-to-diameter ratios. The tool system consists of a drill head and a drill tube. However, the length of the drill tube required to achieve high drilling depths leads to an increased tendency of the tool system to vibrate. The strong torsional vibrations increase tool wear at the cutting edges and the guide pads and influence the drilling quality. The dynamic behaviour of the tool system and thus the process reliability can be improved through the vibration damping effect resulting from the inhomogeneous structure of fiber-reinforced plastics (FRP). In this paper, a wound FRP drill tube is designed based on the experimental determination of mechanical and thermal loads of the drilling process. Compression and twisting of the FRP tube are evaluated simulatively and com-pared to a conventional steel drill tube. The vibration damping effect of the FRP drill tube is analysed in experimental investigations for the material 42CrMo4 + QT. In addition to the feed force and the drilling torque, chip shape, surface quality and noise exposure during the process are evaluated.

The high energy demand and the need for rare resources such as tungsten or cobalt in the production of cutting materials create reasons to provide ecological and economical alternatives. Natural rocks could be such an alternative due to their high global availability and the low energy input required to make them usable as cutting tools. So far, however, no systematic investigation of the usability and suitability of natural rocks as cutting materials has been carried out. In this work, the usability of different flints as cutting materials is investigated. For this purpose an analysis of the rocks is carried out to characterize their properties. Subsequently, indexable inserts are ground from the rocks and applied in the turning of aluminum. It was demonstrated that it is possible to manufacture inserts made of rocks usable for the turning of aluminum and to link grinding result and operational behavior to their properties.

Pay-per-use models for machine tools based on the operating hours neither include the load nor the actual wear of components, although the machining process has a strong impact on the wear of components and consequently on the residual value of the machine tool. This requires a characteristic indicator for the implementation of stress-based data driven payment models for machine tools, which correlates with the degradation process during operation. Also, the indicator has to be responsive from the beginning of the lifecycle of a component and not from the point where a significant change of the condition or potential failure is detected. Therefore, this paper presents an approach to determine the degradation of a machine tool based on the calculation of the wear reserve for most relevant components. Since data driven modeling of the degradation process demands an enormous amount of data, this approach considers the fatigue theory and the calculated life including the actual load during the machining process. This concept provides transparency in the usage of machine tools and allows to implement a stress-based payment model.

Diamond impregnated segments are used for the machining of concrete. In current projects, the diamonds are no longer inserted statistically but deterministically. The positioning of the diamonds has advantages in terms of increased performance, lower raw material requirements or improved wear. This paper aims towards the lifetime and the wear of diamonds during the machining of concrete. Because of the large number of parameters while machining inhomogeneous materials, this paper deals with a reduced number of influencing variables. Hence, tools with four positioned diamonds on the surface are used. The analysis is based on single segment tests performed with different diamond positions. In order to analyze the lifetime and the wear of the diamond tools, the wear phenomena are evaluated by light microscopy. Furthermore, micro cracks by visual 3D-analysis with an X-ray microscope are taken. It can be observed, that the tools with the smallest distances have the shortest lifetime.

Automotive companies in highly industrialized regions are currently facing a stronger competitive situation than ever before. With the onset of Industry 4.0, digitalization of the production systems has been a trendsetter. Due to fast changing markets and push for implementing novel materials, the competitiveness relies increasingly on economical and short planning cycles of machining processes. This has increased the application of simulation tools such as the Finite Element Method (FEM) in manufacturing technologies, including machining. In the last decade, various researchers applied FEM to simulate the macro- and micro- geometry of tools. Furthermore, FEM has been implemented to analyze the effect of coating thickness but has been widely limited to 2D-simulations. In future, a 3D-FEM simulation analyzing the effects of coating thickness in machining can significantly shorten the development cycle, as the simulation can be directly implemented for optimization of mass-production processes. This work presents a 3D-finite element model of a turning operation using AlTiN coated tools with two different coating thicknesses of 4 µm and 8 µm. The Finite Element Method (FEM) simulations are conducted in AdvantEdge software. The influence of coating thicknesses on temperature, and forces are simulated in coating and substrate for a hard to machine casted steel alloy, 1.4837D. The experimental temperature is measured with a two-color pyrometer and the forces are measured with a 3-component dynamometer. In the end, simulated results such as forces and temperature are compared to the experimental measurements and the comparison demonstrates a good agreement between them.

Plasma electrolytic polishing (PeP) is an innovative finishing technique for producing smooth surfaces of electrically conductive materials. The setup for PeP is comparable to the setup of electrochemical polishing, but unlike conventional electrochemical polishing, higher voltages are required and low concentrated, aqueous salt solutions are used as electrolytes. PeP offers advantages similar to electrochemical polishing, such as the absence of mechanical and significant thermal impact on polished parts.This paper presents the results of PeP of steel AISI 304, aluminum EN AW 6082 and cemented carbides WCCo7 and WCCo10. A significant reduction in surface roughness for all workpiece materials is achieved. The influence of the process parameters and the initial surface roughness on the polishing results are presented.

Methods for advanced manufacturing enabling high performance cutting contribute to sustainability in terms of reduced energy and resource consumption. Ultrasonic assistance is a known approach to improve cutting processes for example through reduced forces, tool wear and burr formation as well as enhanced chip breaking behavior. Regarding ultrasonic-assisted drilling, shaft geometry of the drilling tool affects the vibration characteristics and therefore the fundamental effects and process properties. This paper investigates the influence of tools with straight-flute and twisted shafts on the shape of ultrasonic vibration. A kinematic process analysis deduces the change of cutting mechanism. Experimental test series in EN AW-7075 are designed according to process scenarios with intermittent and continuous cutting conditions. The evaluation includes chip size and process forces to show the influence of process parameters and tool geometry as well as the potentials of ultrasonic-assisted drilling.

Centrifugal disc finishing is a free abrasive grinding process characterised by high material removal rates compared to the widely used vibratory finishing. The process is used for deburring and surface polishing of workpieces. In conventional unguided centrifugal disc finishing, the workpieces are machined freely movable within a bulk of flowing abrasive media. In this study, the robot-guided centrifugal disc finishing is presented in which the workpieces are guided by an industrial robot. Based on finishing experiments, it is found that the material removal rate of robot-guided centrifugal disc finishing is significant higher compared to unguided centrifugal disc finishing. Therefore, robot-guided centrifugal disc finishing can be used for time efficient deburring and surface finishing operations. Furthermore, a material removal model based on the discrete element method is presented. The model is valid for both, unguided and robot-guided centrifugal disc finishing. Thus, the model can be used as a universal tool to design centrifugal disc finishing processes without using a time and cost intensive trial and error approach.

The European aviation industry is facing a groundbreaking phase of upheaval, not just because of the global COVID-19 pandemic, but also as result of increasing efforts to reduce greenhouse gases. Carbon neutral fuels that might be used in the future are more expensive than currently used fossil-based jet fuel. Due to these rising operating costs for airlines, travel costs for passengers might increase significantly. To counteract on this, essential cost optimization of today's aircraft production and aircraft operation are of crucial importance. This paper presents a concept for and the experimental analysis of a superimposed quality assurance based on an automated process monitoring system for machining of large aircraft structures to reduce non-productive secondary times. Based on monitoring systems for evaluation of machined edge quality, process forces and machine path, only a superimposed analysis of all information gives a reliable, automated statement whether the machining has been carried out within tolerances. To achieve this, a data collector, data synchronization and analysis of all available monitoring systems as well as a generic data exchange via the communication protocol OPC UA have been developed.

During machining of steel, adhesive and abrasive wear appear as damage mechanisms of the tools. Thin hard coatings deposited by physical vapor deposition for wear protection of cutting tools are state of the art. For the present article, three different coating systems CrAlN+Mo:S, TiAlCrSiN and CrAlON are investigated using a planing test setup. For this purpose, a planing test rig was used, which is equipped with a simultaneous measuring system for the analysis of process forces and chip formation based on high-speed recordings. Coated cemented carbide tools were used for planing a 42CrMo4+QT workpiece. The oxynitride coating CrAlON showed a lower adhesion tendency against the steel workpiece material during planing compared to the nitride coatings CrAlN+Mo:S and TiAlCrSiN. This prevented adhesive wear and reduced friction of CrAlON. The reduced friction decreased the chip thickness during cutting. Moreover, CrAlON showed a reduced contact length.

Due to their long and slim geometry, single-lip deep hole drills have a high level of flexibility, especially in the radial and tangential direction. This leads to torsional and chatter vibrations, respectively, especially at high feed rates. In this paper, the conventionally brazed connection between the shaft and the adapter sleeve is replaced by an adhesively bonded joint that allows slight rotational movements. This is intended to passively dampen the vibrations that occur. In addition to examining the obvious question of whether different types of adhesive can withstand the machining forces and torques, practical environmental conditions are also considered. These are, for example, long-term contact with cooling lubricants or the extreme temperature and ambient conditions during a PVD coating process of the tool.

Due to the emergence of standardized infrastructure for data acquisition and processing, the manufacturing industry collects time series data from machine sensors and numerical controls at large scale. While the necessity to enrich data with domain knowledge to derive tangible insights into the process is universally accepted, little effort has been spent on mining the data at hand. Domain agnostic methods developed by the data mining community often require setting many parameters or do not cover the essential characteristics of manufacturing data. In this paper, a novel approach is presented, which allows for an unsupervised, semantically meaningful segmentation of milling process data. Identifying such segments lays the foundation for cross-process comparisons and in-depth data analyses as the local differentiation of process features is facilitated. Thus, the algorithm aims to support time-consuming data-driven optimization methods uncovering productivity and quality potentials of milling processes.

During laser keyhole welding of metal sheets, a process regime has been observed where no stable welding process sets in. Instead, material is ejected at the keyhole bottom, leading to the formation of a cutting kerf. The forces driving the melt ejection originate from the laser material interaction itself opposite to conventional fusion cutting, where an assist gas has to be used to enforce the melt ejection. As there is no need for a local application of auxiliary materials or gases, this holds the potential for a remote fusion cutting process. So far, only initial research exists concerning its occurrence and the underlying physical mechanisms are barely understood. Therefore, we use high-speed imaging of the cutting front and the melt ejection to generate insights into the melt dynamics. An evaluation approach is presented which incorporates image processing to enable the automated processing of large datasets, allowing a quantification of the observed effects and the evaluation of large-scaled studies of the melt behaviour for different parameters. The presented methodology enables the investigation of the mechanisms leading up to stable remote fusion cutting.

In mechatronic assemblies, future developments require smaller installation spaces and lead therefore to increasing demands on Mechatronic Integrated Devices (MID), e.g., for aerospace industry. Since thermoplastic substrates and conventional production techniques like Injection Molding cannot fulfill these demands, additive manufacturing of ceramics seems to be a promising approach. However, processes to integrate electrical conductors into the ceramic bodies still have to be developed. This paper provides first steps for solving this problem. First, ideas are systematically searched, evaluated by means of a SWOT analysis and elaborated into detailed concepts considering possible process chains. The most promising concepts are investigated experimentally focusing on (i) pouring molten conductor material and (ii) inserting conductor wires into capillaries located in the ceramic body as well as (iii) manufacturing of aluminum oxide-copper-composites by active metal brazing. This paper is a starting point for detailed studies of the most promising concept “pouring molten conductor material”.

The most frequently used process for shaping metal powders is the two-sided pressing in dies. The properties of the green compact resulting from this pressing process, such as green density and strength, are decisive for the final density of the sintered component and thus for mechanical properties like tensile and fatigue strength. Green-compact properties, in turn, can strongly be influenced positively or negatively by the lubrication strategy applied during powder pressing.Friction-reducing tool coatings (e.g. diamond-like-carbon-based) offer the potential to improve green-compact properties by homogenizing density distribution, increasing green strength and reducing injection force. In addition, less admixed lubricant would be required resulting in resource savings.This paper presents first results of experimental investigations on the performance of friction-reducing coatings in die pressing. In particular, the influence of compaction pressure on the resulting density and the correlation between different lubrication strategies (conventional vs. coating) and green strength are studied. To interpret the results, force-displacement profiles were recorded during powder pressing and correlated with material properties. First findings indicate that the coatings alone cannot entirely replace the addition of lubricant. However, it was also found that a combination of both lubrication techniques improves wear behaviour and leads to a more homogeneous density distribution within the green compact. For future investigations, selective variations of lubricant content are planned in order to examine the behaviour of the coating in a more differentiated way.

Additive manufacturing (AM) processes offer significantly increased freedom of design regarding the production of innovative and function integrated components. Although some AM processes, such as laser powder bed fusion, are already used for prototyping, efficient series production is not possible yet. The limiting factor are the low build-up rates of these processes. The novel technology of high-speed laser directed energy deposition (HS L-DED) offers the potential to solve this problem by significantly reducing process times in additive manufacturing. This technology is characterized by very high feed rates (up to 200 m/min). Consequently, extremely increased build-up rates are achieved and thus the potential for economic additive mass production is increased. The high build-up rates are realized by shifting the laser and powder focus, whereby supplied powder particles are already melted before they reach the melt pool on the substrate. This research primarily focuses on the previously unexplored areas of extremely high feed rates and their influences on the process and its results. Hence, an initial process parameter combination is investigated with respect to the resulting material properties. Based on this, a test plan for the systematic investigation of further process parameter combinations is set up with the aid of suitable selected parameter ratios by means of extrapolation. The data obtained will be used to build a database that correlates the process parameters with the achieved process results, in particular the material properties (relative density, microhardness, melt path dimensions). Using this database, optimum process variables for specific applications can be chosen while ensuring high productivity.

The increased environmental awareness of politics and society is confronting the automotive and aviation industries with new challenges. This results in an increased need for research into new lightweight construction potentials. One of the most efficient lightweight materials is titanium. In particular, the high strength and corrosion resistance allows a wide range of applications. Nevertheless, titanium has a decisive disadvantage - its high material costs. One way of making the positive properties of titanium economically viable for industry is to combine it with less expensive materials such as steel or aluminum. For the present study, composite bodies of the steel S235 and titanium powder were produced by cold gas spraying.Compared to other additive processes, cold gas spraying offers the advantage that the powdery coating material is not melted. In addition, modern equipment can achieve very high build-up rates. However, cold gas spraying is a very sensitive process. For an optimal coating formation with respect to good bonding to the substrate material and low porosity, the process parameters must be adapted to the specific material combination.The aim of these investigations was the numerical modeling and experimental proof of a process combination of cold gas spraying with subsequent (inline) forming and heat treatment. The local forming of the sprayed coating is intended to increase its density and to introduce compressive stresses and also plastic strain. In addition, the defined plastic strain in combination with heat treatment by laser allows flexible adjustment of the mechanical properties.

A central element in the surgical treatment of bone fractures is the functional design of bone plates. A novel approach is the use of polyaxial locking screws, which allow the surgeon to respond to different surgical situations. Furthermore, additive manufacturing enables the production of patient-specific bone plates. By combining both technologies, the surgeon benefits from patient-specific implants and the flexibility to react to surgery events. This study evaluates the combination of the two approaches. For this purpose, test specimens that replicate the bolting of the locking mechanism were constructed. These test specimens were subjected to tensile tests in order to determine the maximum forces that the bolted connection can withstand. Conventional bar material and additively manufactured Ti-6Al-4V, which were processed through powder bed fusion using an electron beam (PBF-EB), were used as the base material for the test plates. The investigations showed that higher maximum forces could be achieved with the additively manufactured specimens.

Laser powder bed fusion (LPBF) of parts often requires support structures in order to fix overhangs, dissipate heat and reduce deflections due to thermal expansion and resulting residual stresses during laser sintering. The removal of support structures is a manually accomplished tedious work today. Moreover, slender part geometries are prone to be damaged during the support structure removal. In order to overcome these major cost efficiency limitations and achieving reproducible part quality an automated support removal technology has been developed: An oscillating chisel is moved by a robot along those part contours which are equipped with support structures and intermittently separates them. Fundamentals of the impact shear separation are outlined, which allows low impact forces and removal even of slender and intricately accessible supports. The technology is demonstrated on a curved part. Thus, the novel automated support structure removal technology enables a step change in industrial use of laser sintered parts.

The increasing demand for alternative drives in the mobility sector is continuously generating innovations in the development and production of electrical machines. The automotive industry relies more and more on traction machines with hairpin stators, as the process chain can be automated more easily and the copper filling factor can be increased. Enhancing the electrical copper filling factor in particular allows an improvement of the power density of the electrical machine. However, large conductor diameters lead to additional losses at high frequencies, as they occur in high-speed applications, due to eddy current effects. These operational disadvantages can be avoided by substituting the solid rectangular conductor by a litz-wire. Nevertheless, this semi-finished product requires a more complex manufacturing process. Within this paper the process chain for the production of a litz-wire hairpin-stator is illustrated and the challenges in the process chain of a stranded conductor compared to a solid conductor are presented. In this context, the manufacturing aspects for the production of the litz-wire hairpins are considered in detail.

In modern assembly systems, manufacturers expect a high level of flexibility and efficiency. As an interface between internal logistics and the actual assembly, part feeding technology plays a decisive role in the manufacturing process. Therefore, in this work, we propose a new way of flexible part feeding based on image processing and the proven principle of aerodynamic feeding technology. With a high-speed camera, we analyze the workpiece’s movement during the orientation process and automatically adjust the system parameters to ensure reliable and efficient feeding. Based on three parameters of the workpiece’s trajectory, we develop an algorithm that can systematically find suitable parameter combinations for efficient and reliable feeding. With the proposed concept, retooling for new workpieces can be achieved quickly, using only few components for the parameter setting. At the same time, no hardware changes are required for retooling when handling new components.

In this paper, we present a new framework for process optimizations, the Data Analytics Production Line Optimization Model (DAPLOM). Due to increasing efforts in the digitalization of production systems, an extensive amount of production data is available for analytics. This data can be used for the optimization of production lines and the prediction of their performance (e.g. drift of parameters or component quality) in order to achieve economic and technical improvements. The demand for systematical usage of data-driven methods involving technologies like Data Analytics and Machine Learning and the combination of engineering approaches is growing continuously.DAPLOM guides the implementation process of IT supported problem-solving solutions in production environments. It combines classical process- with data-driven approaches. Specific focus lies on achieving a holistic perspective with a macro- as well as a microscopic view on the given conditions. Here the macroscopic view covers the general material flow, whereas microscopic view considers process details. Additionally, DAPLOM provides useful methods in a step-by-step procedure structured in seven phases. The framework is validated in an industrial use case of an automated wire bending process. Thus, the effectiveness of the framework is demonstrated and further development potentials are identified.

To ensure material supply in production, the focus of research and development is on flexible, automated intralogistics solutions. The automation of material transport in mixed indoor and outdoor environments enabling transport processes between factory buildings is still challenging. The seamless localization of transport vehicles with the required accuracy is a core issue for navigation. While global navigation satellite systems can be used outdoors, other approaches like wireless networks or light detection and ranging technology are applied indoors. Additional sensors e.g. incremental encoders or inertial measurement units are used to create a multi-source localization system, which can adapt to the ideal mix of sensor sources, especially in transition areas. In this paper, a localization approach for driverless transport vehicles is presented in detail including applied methods for data fusion and implementation. The localization approach is validated using a tow truck equipped with sensors for autonomous transport.

Since assembly processes are continuously increasing in complexity due to the growing number of product variants, the importance of digital assistance systems for handling complexity is growing to support human work in a suitable manner. However, the configuration of the assistance system for the increasing number of product variants can be particularly complex and time consuming. This paper therefore presents a concept to support the implementation of new product variants in existing digital assistance systems in a data-based approach by facilitating the process. Thus, an approach for possible support options depending on the individual data management of companies is identified and presented. The developed approach is examined on a conceptual level which is generally applicable. Finally, to identify the most suitable support within the presented approach, a benefit-oriented evaluation is derived as an outlook.

Increasing fluctuations in demand and individualised products require changeability in manufacturing and assembly systems. Line-less mobile assembly systems (LMAS) represent a paradigm aimed at producing large products in small lot sizes. One enabler for LMAS is the automated planning and evaluation of spatial configurations of assembly resources regarding the executability of the assembly tasks. Criteria for executability are the reachability and manipulability measures of the assembly resource. To overcome extensive simulations and case-studies, faster solutions for executability evaluations are necessary. This paper surveys methods of base placement of robots, for utilisation in online assembly configuration planning. Based on the survey, research gaps are identified. A task clustering algorithm for preselection of spatially distributed task sets into task sequences executable from discrete base placements is presented. Clustering overcomes the often applied assumption that the set of given tasks has to be reachable from one discrete base placement.

Sequential precision placing and bonding of components is time-consuming and expensive. Electrostatic self-assembly is a process for the parallel alignment of flat parts. Fluid between the parts acts as bearing and dielectric and serves as an adhesive for the subsequent bonding process. After a rough pre-positioning, a voltage leads to the electrical attraction between electrodes on both components. This results in a force that precisely aligns the parts on the designated assembly position. This paper describes the basics of the electrostatic self-assembly process and presents a structure design for the alignment of large-scale parts (127 mm). A model would help to design necessary conductive structures and control the process. In order to build a suitable model, we investigate the correlation between the applied voltage, the positioning error and velocity during the alignment process. We present the temporal velocity distribution in the process and calculate the alignment force based on a simple theoretic model.

This paper shows that Petri net simulation models are a suitable basis for using Reinforcement Learning to generate a supervisory control unit in industrial production systems. By modelling control unit functions as transitions and state information as places and tokens, a timed coloured Petri net model describing a material flow system can be constructed. The transitions form the action space, while the places and tokens correspond to the observation space of the Reinforcement Learning agent. To confirm the suitability of the timed coloured Petri net model a case study of a simple production facility was conducted. Existing python packages were expanded to provide additional timed Petri net functionality. Then a Q-Learning agent was trained on the Petri net simulation model to perform a simple task. The case study showed that Petri nets provide a suitable model type for training a Reinforcement Learning algorithm and are capable of modelling all relevant components of a material flow system.

The contacting process for insulated copper wires is one of the most challenging operations in the field of electromechanical engineering. New technologies used by electric motors and inductive charging devices increases the complexity of this process. A major factor contributing to increased process complexity is the use of litz wires which consist of numerous individually insulated thin wires. During the contacting process, each of the litz wires must be reliably stripped as well as electrically and mechanically connected to each other or additional contact elements. Therefore, the purpose of this publication is to show and evaluate various contacting processes that exhibit high potential for use with different types of litz wires. In each process evaluation, both shaped wires for electric motor applications and high-frequency litz wires for inductive charging applications are considered. The results are compiled in a process matrix which shows the contacting processes suitable for each wire type as well as their corresponding advantages and disadvantages.

Monitoring process errors and tool condition in single item production is challenging, as a teach-in is not possible due to a missing reference process. An approach to this problem is anomaly detection, e.g. based on motor currents or axis position signals from metal cutting processes. However, with no references anomaly detection struggles to detect failures from signals, because failure patterns are often too similar to regular process dynamics. While single items inherently constitute an anomaly by themselves, they do contain repetitive elements, like boreholes or milled pockets. These elements are similar, what provides an anomaly detection with additional information on regular processes.Hierarchical K-Means clustering combined with Dynamic Time Warping (DTW) and Barycenter Averaging (DBA) enables the identification of similar process elements. The algorithm allows ordering similar process segments by similarity in a tree structure. The introduced method supports querying subsequences from any given cutting process, for which it returns the closest cluster in the tree. This allows to (a) improve the data basis for anomaly detection and (b) to transfer labels with errors between processes. The article demonstrates the transfer of labels (for errors) from a turning process, to a single item milling process.

The use of hybrid vehicle components can support the achievement of a more sustainable mobility through e.g. weight reduction and reduced environmental impacts. In order to exploit all potentials of hybrid components a holistic comprehension of the life cycle is necessary, which can be achieved by increasing the transparency through a digital representation of all life cycle stages. The digital representation contains data like process characteristics and material properties, which can be used for e.g. condition monitoring. Life Cycle Technologies (LCTs) like component- or tool-integrated sensors are an enabler to generate this data. For a value-adding use of LCTs their embedding in data-driven business models offers the potential for creating a sustainable business driven interconnection of relevant stakeholders along the life cycle. Within this paper, the definition of LCTs is enriched and a framework and procedure model for implementing LCTs is developed, including the embedding in a data-driven business model.

Condition monitoring is essential for the OEE of machine tools. Existing solutions are customized to specific settings. However, linear guidance systems commonly used in machine tools are exposed to varying process conditions. Thus, this contribution proposes a concept for a transferable condition monitoring system, which enables a static system to be applied to different settings. The solution is composed of a combination of data preparation methods, feature generation and an anomaly detection model. The system is demonstrated on two test beds with different linear guidance systems. The selected isolation forest for anomaly detection is trained on a series of experiments from one test bed before transferring the condition monitoring to the other test bed.

Increasing importance of X-Ray Computed Tomography (CT) for industrial applications demands suitable approaches for user support. These are intended to guide the choice of setting parameters to improve measurement quality and enable CT-technology access also to non-experts. In the past, several user support systems (USS) have been developed. One promising approach uses knowledge gained from historical measurements. However, this initially requires time-consuming measurements and thus limits generalization, and the potential of machine-learning (ML) techniques. Further, no statement about the achieved quality is possible. In this paper, a concept and the required workflow to build a USS using ML is introduced. Therefore, a suitable ML approach is identified by examining patterns between ML algorithms and their characteristic applications. To provide the required database and analyze the measurement quality, the use of a virtual CT is suggested. Based on the proposed concept, future work will focus on the implementation of the USS.

The modern industrial environment is characterized by short product lifecycles and high competitive pressure. Therefore, efficient production processes are highly important to stay competitive. Due to this, the interest in Business Process Management (BPM) reached more importance over the last years. The first step in BPM is process discovery. However, traditional workshop-, interview- or evidence-based methods (e.g. value stream mapping) are subjective and time-consuming. To overcome these disadvantages, Process Mining (PM) can be used to describe processes unbiased and fact-based. PM uses process feed-back data from existing enterprise software, which are stored in event logs, to describe production processes. The quality of the resulting process model strongly depends on the data quality from the systems. Because of the broad variety of historically grown IT solutions in production industries, the quality of data is often too heterogeneous to be used. There are also frequent problems in reported data, such as missing order identification or different time stamps for the same event. To address these problems, PM-specific data quality problems are identified in this paper and an approach to assess the quality of event log data in production is presented. Additionally, further research outlooks for a subsequent data enhancement are given, in order to apply PM in production successfully.

The use of Artificial Intelligence approaches like Machine Learning (ML) for process optimization promises significant benefits in modern production. One of the most important tasks while using ML methods is feature engineering. The common feature engineering methods are either very time-consuming or require a deep understanding of the process data, that is, knowledge of the basic causality relations in the process. Therefore, due to the increasing complexity of equipment and processes, as well as the necessity to reduce the time to market for new solutions, feature engineering is becoming a challenge to the successful application of ML. This article presents a holistic feature engineering approach showing how the use of integrated software for experimental process analysis can significantly reduce the time spent on feature engineering while improving its quality. The considered target application is the tool wear monitoring on an example of a drilling process based on minimal available measurements.

The Fourth Industrial Revolution accompanies exponentially growing data bases and decreasing costs for computing power and data storage and is focussed on digitisation and networking of the production environment. Thus, methods of machine learning (ML) are gaining popularity. Manufacturing was shaped by research regarding the mathematical and physical description (causality-based) of production technologies in the past. Data-driven approaches usually are not built on these causalities and only consider data. Combining these approaches, called Hybrid Modelling, helps overcoming the limitations of the single ones. The idea is to integrate the available knowledge and causalities into a data-driven model to obtain a better accuracy, with lesser training. Therefore, Hybrid Modelling can provide more accurate forecasts at an acceptable cost level, and has the potential to save resources, reduce schedules, and improve manufacturing quality. This article provides guidance and recommendations for the application of Hybrid Modelling in production processes.

Knowledge Graphs can be characterized as a set of semantically linked information artifacts. One of their possible applications is to enable the integration of heterogeneous data sources: A task becoming increasingly important within industry to obtain end-to-end transparency for complex process chains. There, they can further serve the data for automated machine learning algorithms to operate on. This paper at first elaborates on the definition and characteristics of Knowledge Graphs and data integration. Then, the underlying semantic architectures as well as the corresponding information technology standards are collected. Additionally, first industrial applications in the context of Knowledge Graph-based data integration are presented. The insights are compared with an exemplary custom implementation. Lastly, the findings are assessed for their potential to enable end-to-end data integration and furthermore to provide the basis for future machine learning applications to operate on.

The process design of the hot forming process Radial-Axial Ring Rolling directly influences the quality of the produced rings. Pre-production simulations can improve the quality by extracting the optimal process parameters for given ring geometry, material and properties of the rolling mill. The novel subsequent evaluation of ring rolling simulation data presented in this publication provides information about the development of the height deviation during the simulated rolling process. The required geometric data of the ring can therefore be extracted at any time step of the simulation. The method of least squares is then used to create a regression plane and calculate the real height-axis related distance of all data points. This data leads to a statement about the height deviation. The height deviation is a quality criterion especially for this industrial process and defined as the periodic curvature of both face sides of a ring. The additionally generated quality criterion of the progression on the height deviation over the rolling time thus strengthens the information value of simulations. The application is to be seen in the optimization of simulation processes and thus in the increase of the quality of rolled rings.

Increased market competitiveness compels manufacturing companies to enhance the efficiency of their business processes. Therefore, companies conduct process optimization programs, which typically contain a phase of process mapping (as-is-process) and a phase of process design (to-be-process). Methodically, conventional approaches for process optimization rely on workshops or interviews of employees involved in the business processes. This causes high efforts to analyze process models for weaknesses. Furthermore, improvement measures depend on the expert knowledge, while their impact on process efficiency is barely quantifiable at the time of process design. Hence, a databased identification of process weaknesses and a standardized derivation of measures is necessary to address the aforementioned challenges. Additionally, it needs to be clarified how databased approaches for process design can consider creative expert knowledge. This paper examines existing literature and presents a concept for applying process mining in process design by modelling expert knowledge into a databased decision support.

This paper aims to optimize data-based failure management in manual assembly through qualification for the application of data analytics. Nowadays, scientific approaches in data-based failure management focuses on automated and future-oriented data analysis. Data acquisition’s ability to create a required data structure and provide the necessary prerequisites for the application of data analytics is often neglected or assumed as a given. Due to a variety of influences in manual assembly, a structured acquisition of defect information is impaired. Consequently, the generated data structure and associated information content fluctuate enormously. This creates a high level of waste in companies’ knowledge and resources, which leads to competitive disadvantages in long-term action. Therefore, this paper analyzes existing requirements in terms of information relevance and data structure for relevant data analysis approaches in the context of failure management. Subsequently, an evaluation based on their requirements and manual assembly applicability is carried out. Hence, an advanced process model is developed to indicate necessary and optional data acquisition in manual assembly. Finally, the model is evaluated by using an example from a commercial vehicle manufacturer.

Manufacturing companies face the challenge of fulfilling individual customer requirements while maintaining a low internal variety at the same time. In order to manage both the variety of products and the variety of production processes, companies increasingly focus on process modularization. Nevertheless, the conception of modular processes is a relatively new approach and sets challenges for manufacturing companies. This paper presents a methodology to design modular process structures, which integrates a market view, a product view and a process view, by using communality matrices. Therefore, similarities between different variants are indicated by a market-product communality matrix. Generic production processes and process variant drivers are identified and a product-process communality matrix is derived. Finally, the modular process structure is deduced from the communality matrices. The methodology supports companies to reduce resulting process-related complexity and therefore enhances internal efficiency in development and manufacturing.

Technical Product-Service Systems (PSS) are a well-known concept in research and have been successfully adopted in various industries. For instance, the German manufacturing industry achieves about 20% of its revenue through services complementing physical products. Following the megatrends of sustainability, global responsibility and climate change, the demand for “green” products and services is rising. PSS bear the potential to provide a certain benefit with a lower use of resources, by performing certain tasks through services or improve the performance of products, which can lead to reduced environmental impacts. Nevertheless, this potential is not inherent in PSS and can only be ensured when PSS are designed purposefully. Hence, this article addresses this issue and proposes a new concept for eco-design of PSS with the use of a customer- and process-oriented design approach. In order to evaluate the environmental impacts of PSS, Life Cycle Assessment is used. With the proposed concept, PSS providers are able to evaluate, and optimize the environmental performance of PSS in the early stages of PSS design.

Besides shorter product life cycles, companies are facing increased market volatility and cost pressure. To remain competitive, customer requirements must be met as individually and quickly as possible. New products must be developed faster, at minimal cost, and in a customer-centric way. However, this cannot be realized with traditional, plan-driven development approaches. The sequential development of the product and production processes leads to long development times while simultaneously increasing the effort to include changes in customer requirements. The highly iterative and integrated product and production process development (HIP3D) approach presents a possibility to enable shorter development cycles, greater customer involvement in the development process, and a reduction of time-to-market. The application of the HIP3D approach, which implies an integrated and hypothesis-based development and validation of products and production processes, leads to an increased overall complexity as well as to an increased need for bidirectional information and coordination throughout the development. In addition, continuous operational work with incomplete and uncertain information must be taken into consideration. To ensure that these challenges do not outweigh the benefits of highly iterative and integrated development, a methodology to identify and prioritize activities based on their bidirectional information needs and maturity level is presented in this paper. In this way, uncertainties in the implementation of the hypothesis-based HIP3D process are systematically reduced and thus the shortening of time-to-market is proactively ensured.

Platform companies such as Amazon and Uber have changed the business-to-consumer (B2C) sector at a rapid pace. In the context of the digitization of products and processes, the importance of digital business-to-business (B2B) platforms is increasing. Emerging digital platforms form a value network between business entities and are characterized by mostly complex business relationships of different partners. Therefore, a clear representation of the respective value network on a business level is fundamental for the understanding and analysis of digital platforms and their underlying business models. In this publication, existing models and modeling languages for value networks were analyzed for the requirements in digital platform-based businesses. As a result, this publication provides a methodology that reveals the complex relationships of platform-based value networks based on defined roles and relationships for an improved and consistent understanding of platforms in the manufacturing industry.

Globalization has led to a major shift in economic power from the Western world to the emerging economies in Eastern Europe and Asia. Manufacturing companies from these countries are improving their technological and organizational capabilities at high speed. In addition, they benefit from the continuing advantage of significantly lower labor costs in global competition. The technology leadership and global competitiveness of Western industrialized nations is increasingly under threat. To survive in this asymmetric competition, the analytical evaluation of the actual performance of the relatively new competitors from emerging markets is crucial. It forms the basis for performance-based comparison and the derivation of new differentiation features. Currently, however, there is a lack of an approach to evaluate the performance of an industrial sector to enable an in-depth and global comparison. The objective of this paper is to develop a systematic model for data-based evaluation of the performance of an industrial sector. This model is exemplary applied to the key industrial sector of tooling. Finally, the proposition of new differentiation features in global competition results from the in-depth comparison of the performance of the Chinese and German tooling industry.

Towards greener production, manufacturing companies face several challenges, for example peak load shaving or flexible production planning as parts of demand-side management (DSM). DSM uses processes that can be shut down, shifted, or controlled. Advances in digitalization in the energy sector and manufacturing systems create transparency which in turn offers new opportunities to commercialize energy flexibility potentials as optimally and automatically as possible. The variety of flexibilities in manufacturing systems and various dependencies of different kinds of complex manufacturing processes complicate the modelling and aggregation of flexibility. To overcome this challenge, we developed a method for the aggregation of energy flexibilities that is based on a generic energy flexibility data model. The method proposes a two-step approach to aggregate flexibilities cost efficiently and considers manufacturing specific limitations. For cost-efficient aggregation, we use in the first step the merit-order model known from the energy industry and in the second step the bin-packing problem originating from combinatorial optimization, adapted according to the generic data model. The two-step approach allows energy flexibilities to be aggregated across industries, facilities, and systems, thus ensuring broad applicability.

This paper presents the concept of “Internet of Sustainability”, a framework covering which sustainability-related data, models and decisions are necessary along the product lifecycle.Irrespective of the pandemic slowing industrial output worldwide and reducing resource use, “earth overshoot day” occurred on August 22, 2020. Production industry as one of the main energy consumers has to realize a transition towards sustainable production. To create a palatable transition, it is essential for industrial companies to maintain a balance between compliance with increasing regulation and constant turnover. The progressing digitalization and the increasing availability of data in industrial production enhances overall productivity and offers increased sustainability.First, the main structure of the framework is introduced. Second, two use cases are discussed with respect to three application layers (Raw Data, Smart Data, Smart Expert Layer). Third, an outlook for further research in the “Internet of Sustainability” is given.

In recent years, the industry has been relying on the use of hybrid process models, i.e. the integration of agile and plan-driven approaches into a combined development approach. However, successful implementation has been hampered due to a lack of systematic combination patterns for the use of hybrid process models. Therefore, this research focusses on the identification of so-called basic types of hybrid development processes by means of typification. Unsystematic integration and the combination of arbitrary characteristics of agile and plan-driven approaches is neither meaningful nor purposeful. Instead, the arrangement of different characteristics must follow a certain logic to ensure the integration of a consistent process. Therefore, the infinite amount of random combination possibilities of plan-driven and agile model characteristics has to be reduced to a limited number of consistent combination patterns. Consequently, the following methodology determines basic types of hybrid product development processes by means of typification.

Companies that manufacture their own products and also provide after-market service are common and usually involve in-house manufacturing and assembly. The limited capacity of in-house resources and the requirement to meet delivery deadlines forces companies to deviate from the cost-optimal production strategy and to produce quantities according to demand instead of optimal lot sizes. A selective increase in the capacity of heavily utilized resources, e.g. by additional capacity of platforms or participation in peer-to-peer networks, enables companies to make savings on setup costs by producing selected articles in optimal load sizes. Such savings can be used to price third-party capacity. This paper presents a new method for pricing the procurement of capacity in a peer-to-peer network and demonstrates it on an industrial example.

The desire for individual customer solutions is rising in the manufacturing industry. The digitalization enables new forms of creating customer value, but simultaneously opens up the market for new player from the tech industry, so that traditional production companies have to challenge their business models. The development of existing business models into digital business models is therefore of high strategic relevance to maintain a company’s competitiveness. However, manufacturing companies are hesitant to adapt digital business models.For successfully building digital business models in manufacturing, their connection with the production strategy must be considered. Since there are no approaches that take these interactions into account, the goal of this paper is to develop an integrated approach to develop production strategy and digital business model simultaneously. For this purpose, two morphological boxes are developed to characterize production strategy and digital business model. Their interdependencies were assessed in an expert interview.

The intensity of international competition is increasing continuously due to the progressive globalization and digitalization. As a result, it becomes more difficult for companies in high-wage countries and high-tech industries to differentiate based on cost or quality leadership. Therefore, focusing on the actual customer benefit by offering product-service systems (PSS) to generate new revenue streams and to differentiate from competitors appears to be more successful. Consequently, complexity increases and shifts to the provider due to integration of more and more customer processes into the offered solution. In addition, the provision of PSS requires versatile knowledge that often cannot be managed by a single company. Thus, companies organize themselves in so-called corporate value networks (CVN) to deal with the increased complexity and required competencies. This paper presents a concept for a methodology to increase the profitability of the provision of PSS by a complexity-oriented structuring of CVN.