Production at the leading edge of technology

The near-surface layer of forging tools is repeatedly exposed to high thermal and mechanical loading during industrial use. For the assessment of wear resistance of tool steels, in previous work thermal cyclic loading tests were carried out to investigate changes in hardness. However, actual results of time-temperature-austenitisation (TTA) tests with mechanical stress superposition demonstrated a distinct reduction of the austenitisation start temperature indicating a change in the occurence of tempering and martensitc re-hardening effects during forging. Therefore, the superposition of a mechanical compression stress to the thermal cyclic loading experiments is of high interest. Tests are carried out in this study to analyse hardness evolution of the tool steel H11 (1.2343) under consideration of forging process conditions. The results show that the application of compression stresses on the specimen during the temperature cycles is able to restrict tempering effects while increasing the amount of martensitic re-hardening.

The current trend of mass customization pushes conventional production techniques to their limits. In the case of forming technology, limitations in terms of adaptability and flexibility emerge, while additive manufacturing lacks in the manufacturing of large, geometrically simple components. Combining both processes has potential to use the strengths of each process and thus realize time and cost efficient mass customization. As the interactions between the processes have not been fully investigated yet, in this work a distinct modelling approach in LS-DYNA is used to examine the influence of the additively manufactured elements on the formability. Namely, varying geometric properties and number of pins created with additive manufacturing are in the focus of this research. The used material is the alloy Ti-6Al-4V, which requires processing at elevated temperatures due to its low formability at room temperature. The results show a clear influence of the additively manufactured elements on the formability.

In the scope of project A7 of the collaborative research center TCRC73 investigations of superimposed oscillation forming have been conducted. Positive effects regarding the reduction of plastic work, surface quality and mold filling in an ironing process could be demonstrated. Basic investigations also showed these effects for a compression process. By means of this research, the findings are transferred to the production of a functional component with an external gearing. For this purpose, a hydraulically working oscillating device is installed in an existing tool system in order to produce a demonstrator component. Forming experiments with and without superimposed oscillation are conducted. The components are examined concerning the plastic work required for forming as well as metallographic properties and surface quality. For this purpose, micrographs of the structure are taken and the flow lines and grain structure are analyzed. Hardness measurements are conducted and conclusions about the influence of superimposed oscillation on forming behavior are drawn.

Up to 250 fine-blanked components per luxury-class vehicle make a considerable contribution to the automotive production supply chain. High scrap rates in an industrial setting imply an inefficient use of the resources and process design and cause avoidable CO2-emissions. This contribution focusses on a data-driven production cycle optimization of workpiece quality features and enabling efficient usage of resources in a production chain for the life cycle of fine blanked components. As one of the quality features in fine blanking, the die roll reduces the geometric accuracy and the functional surface area and thus needs to be removed in a secondary finishing process, typically grinding. An experimental setup is deployed using an industrial fine blanking and surface grinding machine on a shopfloor interconnected by an Edge-/Cloud-Architecture. This approach enables quality feature prediction and process adaption to enhance workpiece quality and efficient usage of resources, eventually leading to increasingly sustainable process chains.

The increasing use of high-strength materials in sheet metal processing results in challenges for cutting presses. The required cutting force increases with the strength of the material. After material separation, a sudden release of the energy stored in the machine leads to intense system vibrations and noise emission. Noise protection and reduction solutions such as machine cabins or active vibration damping systems are expensive, require much space or frequent maintenance. Due to this, forming machines must be acoustically optimized. In this article, selected structure measures for the machine-side noise reduction are derived and evaluated in the context of simulation studies using an acoustic machine model.

Demands of the aging society shift the focus of production engineering towards challenges in medical technology. An increasing number of people are equipped with implants, but incompatibilities can lead to implant loosening. These incompatibilities can be avoided by implants based on fine-grained titanium which stimulate interactions between implant and bone tissue and thus activate bone healing.The continuous production process Equal-Channel-Angular-Swaging (ECAS) is applied to achieve grain refinement in titanium rods. Shear strains during forming are therefore numerically investigated. Processing of titanium with conventional ECAS leads to unfilled die cavity. Thus undesired bending occurs, both in simulation and experiment. The bending stresses result in material failure. Additionally, the achieved shear strains are inhomogeneously distributed in the cross-section.It is shown that a hydrostatic stress state created by counter pressure improves the failure limit, yields in more homogenous shear strain distributions and enables a continuous production of fine-grained titanium.

In order to reduce the weight of vehicles and their CO2-emissions and to increase driving range, new ways of producing hybrid material compounds must be developed to tailor the properties of the part exactly to the intended use. Lateral angular co-extrusion (LACE) offers the possibility to produce hybrid profiles. In the past, this process was used to extrude flat magnesium-titanium profiles and after further development, co-axial aluminium steel profiles.In this study, the numerical development of a tooling system for the production of asymmetric hybrid semi-finished products extruded using LACE is presented. The co-extruded profiles, consisting of an L-profile made of steel (AISI 5120) that is filled with aluminium alloy EN AW-6082 on one side, will subsequently be formed to hybrid transverse control arms by die forging. The tool design is initially carried out for a laboratory scale extrusion in order to gain basic knowledge about the process and to quantify the influence of the different process variables like ram velocity or extrusion ratio.

Hot stamping of boron manganese steel is a state of the art process for manufacturing safety-relevant automotive components with regard to lightweight design. In terms of the mechanical properties, the microstructural evolution is of particular interest. Besides conventional hot stamping processes, this is especially apparent for the manufacture of hot stamped parts with tailored properties. To reduce costs and scrap parts, numerical process design requires appropriate material models considering transformation kinetics during in-die quenching. The phase transformation behavior can be characterized through experiments on various testing facilities, which can lead to discrepancies in the results and therefore in the predicted mechanical properties. In this paper, the results from two testing facilities are compared to each other through dilatometry experiments on the boron-manganese steel 22MnB5. For further characterization of the differences, secondary samples are taken to analyze the hardness as well as the microstructure by optical microscopy.

Very thin foil material is usually fed by roll-feeding devices. However, these devices can limit the feeding speed and cause damage to the foil surface finish. A promising alternative are electromagnetic systems. This technology is based on the working principle of a linear induction machine.In this paper, a study is presented which examines the potential and limitations of this approach for foils. For this purpose, a parametric finite element analysis (FEA) model was developed using different ANSYS element types. With this model, the physical limits of the system, e.g. heating by the excitation of the windings and induction of eddy currents in the foil, the model is also used to optimise the ratio between feeding force and material heating.The feeding technology presented here provides for a more efficient production and thus significantly contributes to shaping the change in the production industry.

Bidirectional deep drawing is a process for sheet metal forming on servo screw presses in order to influence part properties in a targeted manner and to extend process limits. With servo drives, it is possible to freely change the motion and force during the forming process. Experimental and numerical investigations show the extension of the process limits. In deep drawing, the process forces are indirectly transmitted from the flange via the part frame to the bottom area. In this process, a crack may occur near to the bottom of the part due to excessive thinning. In bidirectional deep drawing, the weak point can be strengthened with a suitable motion profile. Thus, the drawing depth can be deeper, because higher process forces can be applied. The design of the motion profile in bidirectional deep drawing is supported by a variance-based sensitivity analysis. This allows the potential of servo screw presses for sheet metal forming to be better exploited.

A superimposed oscillation in the main force flow of a forming process is investigated. Therefore a tool system was developed. The oscillation in this system is generated by pulsating oil streams. As part of this research, the oscillation device is equipped with additional storage tanks, to generate a higher oil volume flow. The upgraded oscillation system is installed in a forming process. In this research, the influence of the superimposed oscillation on the necessary plastic work to form a complex gear geometry is examined. An analysis of the influence of higher forming forces, higher operating pressures and longer process times on the operating behavior of the oscillating device also takes place. The identified optimal process parameters are used in a model process based on the industrial forming process of the company Felss Systems GmbH. Furthermore, an experimental test setup to carry out these investigations is developed in this research. Forming experiments to iron an external gear geometry oscillation-free and superimposed oscillated are conducted with the new test stand.

Clinching is a cost-effective mechanical joining process used for metallic as well as nonmetallic materials. Destructive testing methods such as peel and tensile tests can be used for strength investigations of such joints. Additionally, by measuring the geometrical properties such as undercut, neck thickness, and final bottom thickness, the joint quality can be estimated. These methods are cumbersome and do not meet the time and cost efficiency requirements of industrial production. The harmonic analysis benefits from changes in vibration characteristics. In this paper, the structural response of a clinched joint, subjected to a dynamic displacement in order to introduce an acoustic wave testing method is studied. This is accomplished by using simulated joining processes. The approach of this paper is based on a transient dynamic finite element analysis, followed by a fast Fourier transform. The results are presented, together with an analysis of sensitivity to different process parameters.

From a metal forming perspective, warm forming technologies can be used to reclaim the shape and functionality of worn engineering steel parts such as case-hardened geared components. Since forming temperatures are beneath the material-transformation temperature, no loss in hardness is expected and a new heat treatment can be omitted. In order to determine a process window for reclaiming geared components, upsetting tests were carried out on case-hardened workpieces made of AISI 5115.The workpieces were heated to between 700 °C and 900 °C by induction or furnace heating and upset to a true strain between 0.1 and 0.4. The upset workpieces were cooled in two media, air and water, and metallographically investigated. The combination of 900 °C furnace heating, upsetting (φh = 0.4) and quenching led to an increase of 60% in case-hardening depth. This procedure can be used for reclaiming geared components by means of forming technology.

The load specific design of structural components and their associated mechanical joints is challenging due to the large number of interdependencies between different joining parameters. The definition of a transferable knowledge base is also difficult and in most cases the design phase has to be accompanied by numerous experimental tests. Therefore, in most cases, the components and/or the clinched joints are manufactured with a significant safety factor, generally leading to oversizing and corresponding reduced economic and environmental efficiency. This paper describes the basics of a new approach for the evaluation of component and joint loads based on load path analysis by using a simple specimen geometry. The new method is independent of the orientation of the component and the force input in relation to the coordinate system used for evaluation. It is presented using simple plate with a square hole and compared to the classical approach. The primary goal of this methodical approach is an even load distribution over the joining point and component, thereby developing a basis for future design approaches aiming at the reduction of oversizing in joint structures.

Thermomechanically treated (TMT) materials are characterized by a fine-grained microstructure, which leads to extraordinary mechanical properties. In this study, the alloyed tempering steel 42CrMo4 (AISI 4137) is used to set up a two-step TMT upsetting process with intermediate cooling. A water-air based cooling system was used to adjust different phase configurations between the two steps by varying the target temperature and cooling rate. Standardized test specimens for Charpy impact tests and tensile tests as well as for metallographic analyses are cut out of the formed parts. Tensile tests showed that the yield strength can be enhanced up to 1188 MPa while the elongation at break is about 12% without any additional heat treatment by forming the material after rapid cooling. This fulfills the demands of the standard in quenched and tempered state. This process route allows for local-load tailored part design and manufacturing by adjusting the forming conditions conveniently.

Hybrid components are commonly produced in two or more separate manufacturing steps. In order to apply fiber-reinforced thermoplastics (FRTP) adhesively to a hot stamped blank, it has to be reheated. Through the integration of production processes of processing hot stamped metal blank and FRTP, it is possible to form and join the two materials simultaneously without reheating the metal component. By setting up an automated experiment to interrupt the cooling step during the hot stamping process at an elevated temperature level, the residual temperature of the metal blank in the quenching phase can be used to enable an adhesive bond of the FRTP to the formed steel. Within the scope of this investigation, a process window suitable for the interruption of the hot stamping process and the subsequent application of the FRTP is determined. Finally, hybrid u-shaped profiles are examined under dynamic loads, demonstrating an increase in the mechanical performance.

The application of solid lubricants offers high potential in terms of resource-efficiency in contacts under relative motion. The disadvantage of this lubrication method is the high risk of interface failure after the coating has been worn. In order to predict the service life of coated surfaces, it is necessary to determine the microscopic wear behaviour by analysing the coating hardness and coefficient of friction (CoF). Due to layer thicknesses under 3 µm, these parameters can only be determined using nanoindents and nano-scratch-tests. These tests require a transfer of material properties and tribological parameters from the nano to the micro level. The transfer method to the micro level and validity of the values determined at the nano level is presented while also describing challenges due to different influences of the respective size level. Additionally, oscillating sliding tests are carried out to determine the friction properties during sliding, which correspond to reference tests performed on the micro level.

Lithium-ion battery cells will dominate the market in the next 10 years. However, the use of certain materials as cobalt is a critical issue today and is constantly being reduced. Sodium-ion batteries are an alternative, which has already been researched on a laboratory scale. Increasing of the individual production steps are serious bottlenecks for bringing basic cell concepts into application. Within this paper a systematic investigation of parameters influencing the producibility for sodium-ion battery cells will be taken into account. For this purpose, the characteristic process variables and challenges along the production chain are presented along the process chain of lithium-ion battery cells. The influence of various process-machine interactions on the properties of the electrode is illustrated using the anode of sodium-ion batteries as an example. First conclusions whether the production technology can be adapted to the cell chemistry of the future at an early stage will be made.

Components used in high temperature applications require special material properties to be able to withstand these external conditions. However, oftentimes only individual component areas are exposed to such requirements. Multi-material solutions facilitate the use of the right material at the right place, thus saving resources and costs. One approach, considered in the CRC “Tailored Forming”, is the use of pre-joined hybrid semi-finished products to manufacture high-performance multi material components. Within the scope of this study, the forming process for the production of a hybrid shaft made of the nickel-based alloys AISI alloy 625 and AISI 304 is numerically investigated. A material characterisation was carried out to analyse the different thermomechanical properties of the materials and to define a suitable process window in which the flow properties are adjusted. Furthermore, the influence of various die angles on the joining zone was investigated. A tool load analysis was finally carried out.

A new process is presented in which semi-finished products (AA6060) with tubular internal reinforcements of higher strength (AA7075) are produced by composite hot extrusion. Flange-shaped parts are manufactured from the extrudate by subsequent lateral extrusion and upsetting. The aim is to develop a robust process route for flange-shaped components by composite hot extrusion and subsequent cold forging. For this purpose, numerical investigations were carried out. The process can be conducted without errors if certain limits are observed, which can be shown numerically. The probability of solid state bonding can be increased by choosing the proper tube geometry, so that subsequent lateral extrusion can be carried out. The knowledge generated is to be used to expand the spectrum of components in the future to include higher strength tubular reinforcements.

Today’s omnipresent environmental problems have led to an increased demand for electrical machines and its subcomponents – i.e. stators, rotors and control units - that show highest quality/price ratios. The flatpack technology is a multiple stage method of bending flat, comb-like stator cores with inserted copper windings into rounded rings. Despite the complexity caused by simultaneously acting bending effects, that stator manufacturing technology provides potentials regarding achievable stator performance due to its relatively high copper fill factor. In previous investigations, numerical models of one single bending operation have been validated. In contrast, this investigation focusses on predicting the stator geometry after multiple bending operations. Therefore, simulation models were extended to calculate such bending processes within one single model. Finally, results gained from experiments and simulation runs with same settings are presented. Discussion of results discloses a conformity between numerical and experimental geometries after multiple bending stages beyond 94%.

The compensation of straightness deviation in BTA deep hole drilling represents a major challenge caused by the inaccessibility of the cutting zone and the multitude of influencing factors. It is required for a variety of drilling applications with length-to-diameter-ratios larger than l/D > 10 and high quality features such as roundness, diameter accuracy and surface quality. In addition to these classical requirements, the straightness deviations represents one of the most important quality features. This paper presents the conceptual idea of an innovative tool system for in process compensation of straightness deviation. It can be retrofitted for existing deep drilling machines and carries out the straightness deviation correction without process interruption. Fundamental idea is the process parallel measurement of the wall thickness with an ultrasonic measurement device, which is connected to an actuator system by a control loop and allows the drill head to be slightly tilted in a targeted manner.

This report addresses both the empirical and theoretical analysis of a two-cut strategy for gear hobbing with conventional hobs. Tool life and wear behavior of PM-HSS S390 tools are analyzed in fly-cutting trials when machining 20MnCr5 case-hardening steel. By varying the axial feed rate in the first and the second cut, the influence of the interaction between both cuts on the tool wear is investigated. The results are compared to wear measurements from trials conducted with a one-cut strategy. It was shown that at the underlying test conditions, the average proportion of wear in the second cut was about 4%. For a better understanding of the interaction between the two cuts, load collectives were formed based on characteristic chip values calculated with SPARTApro. The collectives were evaluated with regard to the tool life.

Temperature measurement close to the point of its origin is of great importance during chip removal. Therefore, two different temperature measurement methods are used and compared with each other for turning of the aluminum alloy EN AW-2017. On the one hand, cutting temperatures are measured by three thermocouples embedded in the indexable insert. On the other hand, the temperature in the contact area of the tool and the workpiece is detected by a tool-workpiece thermocouple measuring the thermoelectric voltage resulting from the Seebeck effect. For the experiments the cutting speed remains constant while the depth of cut and the feed are varied.The results show a rise of the cutting temperature with increasing cross-section of the undeformed chip. In comparison, the tool-workpiece thermocouple offers a higher sensitivity while the embedded thermocouples measure higher temperatures for large depths of cut. Hence, the suitability of the methods is affected by the cross-section of the undeformed chip.

Grinding is an established process for gear manufacturing, as good geometric and surface quality can be achieved. For bevel gears, grinding is used in case of high demands on accuracy and reproducibility. In industry, design of bevel gear grinding processes is usually based on experience. An efficient design of grinding processes can be performed based on the cutting force. Knowledge of the cutting force is necessary to predict the process influence on the workpiece and the wear of the grinding tools. For bevel gear grinding, no cutting force models exist. To model the cutting force in grinding processes, the contact conditions must be known. In this report, a model of the geometric contact conditions in bevel gear grinding is presented. The model is validated by comparing the simulated bevel gear flank with the ideal flank. Finally, the relation between simulation and measured process loads is analyzed.

The process principle of electrochemical machining (ECM) is based on anodic material dissolution at the interface between the workpiece surface and an electrically conductive solution without any mechanical or significant thermal impact on the workpiece surface. As the material removal mechanism is contact- and force-free it is independent of mechanical properties of the workpiece material such as strength or hardness.In this paper, a methodology to perform the process design of pulsed electrochemical machining (PECM) for manufacturing a three-dimensional geometry based on a sequence of standardized material characterization experiments, data processing and multiphysics simulation is shown. As a part of parameter studies, the machining parameters cathode feed rate and process voltage were varied to determine their influence on the resulting workpiece geometry. Resulting process conditions as for example the working gaps and electric current density distributions were analyzed and compared with experimental results.

To improve the wear resistance of cutting tools, PVD (physical vapor deposition) coatings are well established in manufacturing. Therefore, different PVD hard coating systems have been developed until now. Nevertheless, the demands on the functionality of PVD hard coatings have become more specific. Reasons for this include increased cutting speeds, the machining of high strength materials with low heat conductivity as well as the development towards minimum quantity lubrication and dry machining. In summary, all these leads to increasing temperatures at the cutting edge. As a result, the wear resistance to thermally induced failure mechanisms and self-lubricating PVD hard coatings getting more important. In this work, important requirements for PVD coatings for high temperature cutting processes are presented. Furthermore, an overview of self- lubricating oxidic coating systems based on aluminum oxide (Al2O3) and Magnéli-phases as well as their wear behavior in cutting application is given.

One approach to overcome the difficulties in machining titanium can be the selection of a suitable cutting material. However, influences of the chemical tool wear under machining atmosphere has not been considered jet. In this work the tribochemical wear resistance of uncoated carbide tools, polycrystalline cubic boron nitride (PCBN) tools polycrystalline binderless CBN tools, and polycrystalline diamond (PCD) tools is investigated under different atmospheric conditions cutting Ti-6Al-4V. Air, pure argon and silane-doped argon are used to determine the influence of oxygen contents on tool wear. It was found that oxidation and adhesion behaviour were influenced by oxygen content. Up to 33% increase in tool live were obtained with uncoated carbide- and the PCBN tools with high CBN content. The reduced oxidation also affects PCBN tool behaviour. During machining under oxygen-free conditions increased adhesion of Ti-6Al-4V occurs. The increased wear behaviour for binderless PCBN tools lead to significantly reduced tool life.

Ti-6Al-4V was machined by varying the nozzle position during external cryogenic CO2 cooling. The thermomechanical load was measured and the resulting surface morphology was characterized. The results show a significant influence of the nozzle position on the temperatures in the surface layer. Furthermore, a correlation between the temperatures and the microhardness inside the surface layer of the workpiece was found. This relationship was used to specify an optimal positioning of the nozzle in order to minimize the occurring temperatures.

The “rolling contact related life calculation” (RCRL) of profiled rail guide systems takes the survival probability of every rolling contact into account, that allows a calculation of up to 4 times higher lifespans for pitch and yaw moments on a single guide. New calculation methods allow a user-friendly calculation of the RCRL, which differ from each other by the level of simplification and the computational effort. In this paper, they are summarized and compared to each other.With the temporary results of the experimental lifespan investigations, the applicability of the RCRL and the mathematical calculation methods can be confirmed.The mathematical models are based on an iterative calculation of the displacement between the wagon and the guide rail. Based on that, a concept for an indirect measurement of loads on the wagon is presented. Thereby a lifespan calculation at real operating conditions of the profiled rail guide system can be implemented.

Internal traverse grinding (ITG) with electroplated cBN tools is a highly efficient process for machining of precision bores. Profiled tools allow for high stock removal and good surface quality in a single axial stroke. However, process control is difficult. Especially shape deviations of machined workpieces are influenced by machining system compliances. Simulation-based solutions can be used to predict and compensate shape errors.To model the system compliance, deformations have been measured using eddy current sensors in combination with piezoelectric force measurement components. A simplified substitute model has been developed, which represents the deflections of the entire machining system as a function of the process normal force, and grinding investigations with in-process force measurements have been performed. By incorporating the measured forces and the compliance model into an existing simulation system, the influence on the resulting shape of the bore has been predicted in good accordance with the real grinding process.

During the machining of wood and fibre composites, large quantities of fine dust and chips are produced which are harmful to health. In addition, a chip accumulation can occur at the cutting position, which reduces the production quality and promotes double cutting. In woodworking, chip fans are often used to remove these dusts and chips. These are located directly on the tool holder and generate an air flow directed towards the spindle by the spindle rotation. The additional airflow improves the removal of the dust and chips directly after the chip formation in the direction of the suction hood. As a side product, the chip fans generate high aeroacoustic emissions with increasing rotational speeds. In this paper, the acoustic behaviour of chip fans is characterised by measuring the sound pressure levels at different speeds when running idle. In addition, the aeroacoustic and flow behaviour of chip turbines is numerically modelled with a Computational Fluid Dynamic (CFD) simulation. Thus, in the future, design measures can be analysed for their sound-reducing effectiveness in time-saving simulation studies.

Thermo-elastic errors in machine tools have a significant influence on the machining accuracy. The error at the tool center point can be determined with the structure model based correction, which uses physical based models like finite element models. The output of the structure model is the volumetric thermo-elastic error in the workspace of the machine tool. The correction values of the machine axes are determined based on the volumetric error with the help of a kinematic model. Therefore, three variants are presented in this article and their influence on the correction accuracy is estimated. The evaluation is based on typical thermo-elastic errors in the workspace of machine tools. The influence of the different variants on the correction accuracy is estimated with the Monte Carlo method based on randomly generated errors in the workspace of the machine.

Abrasive flow machining is widely used for finishing applications. For every workpiece material, chips are formed by the relative movement between abrasive grains and workpiece surface. Thus, the friction in between is an important parameter to describe these mechanisms. A method for inline measuring of process forces in abrasive flow machining is presented. A sealed, technical device for measuring tangential forces and a device for measuring the static pressure were successfully developed. The results show a strong dependence on processing parameters. The data is used for developing a friction model that takes processing parameters into account. Moreover, the friction model is utilized to implement the local slip of the abrasive media on the workpiece surface in flow simulations. Consequently, process designs of machining tasks will be improved and will contribute to an enhanced quality and process stability. By process simulations, time consuming experiments are reduced in order to reduce costs.

In this paper, we present a measuring approach to assessing the volumetric accuracy of machine tools or kinematics with more than 3 interpolating feed axes. It is based on an inexpensive Double Ballbar and suitable for fast performance-tests in an operational state of the machine. The novelty is the recording of large amounts of data in a very short period while continuously moving along measuring paths with up to 6DoF. In order to facilitate this measuring approach, we developed a Double Ballbar with extended measuring range based on an optical sensor and a series of systematic methods, like kinematic error modeling, optimal design of 6D measuring paths as well as post-processing and evaluation of the captured data. We demonstrate that our approach is universally applicable to different types of kinematic structures such as 5-axis machine tools, industrial robots or even parallel kinematics. By experiments, we will also show that the measured data are essentially more informative than those of standardized circular DBB tests are. Moreover, the measuring procedure is automatable and to be carried out under production conditions in the workshop, what makes it practically applicable in various evaluation and decision scenarios.

Additive manufacturing [AM] is often claimed as an environmentally friendly technology that also offers great potential for the industry. However, material and energy efficiency depend on a large number of influencing factors. Recent studies have focused on the quantitative evaluation of the environmental impact and energy demand of AM processes as well as the investigation of their impact factors. For powder production as well as post-processing there are only few studies available so far. This paper introduces an evaluation model to quantify and analyze the cumulative energy demand [CED] from cradle to gate using direct energy deposition [DED]. During the analysis, the process steps that have a significant impact on the CED are identified. It is observed that the proposed evaluation model is a powerful tool to analyze the energy performance of DED technology

As applications of Additive Manufacturing (AM) extend from rapid prototyping into series production, new challenges of digital information integration arise. In order to obtain transparency of overall costs and part quality, the relevant information of each process step needs to be gathered from different sources and combined in a meaningful way. Thus, a digitally integrated process chain can provide added value for manufacturers and their clients.This paper proposes building blocks for digitally integrated process chains in the context of powder bed fusion (PBF) based AM to form a basis for transparency of cost and quality. Solving requirements for the end-to-end digital chain leads into challenges of acquiring, storing, processing and routing information. Related technologies comprise domain-specific software, enterprise integration patterns, database and semantic technologies. The mapping of these enablers onto the AM chain leads to a target architecture that forms the fundament for future investigations.

The results of spatter measurements within laser-based powder bed fusion of metals are presented. A stereoscopic imaging setup and corresponding reconstruction algorithm are used to determine three-dimensional measures of the spatter characteristics from experiments with 1.4404 stainless steel. The spatter characteristics are correlated to the process zone morphology and evaporation behavior. The evolution of the spatter count over consecutive tracks is investigated and shows a decrease and convergence towards a constant value. Experiments with the process gases argon, helium and nitrogen reveal that the spatter count decreases with the gas density, whereas the spatter speed stays unaffected. This confirms the key role of the process gas in the entrainment of powder particles and the associated spatter generation. The results indicate that macroscopic spatter characteristics contain relevant information about microscopic process behavior. This makes spatter characteristics a designated process feature for new sensing approaches for the observation of industrial applications.

We present a disruptive method for manufacturing structure-integrated force sensors based on the LPBF process by inserting a strain sensing steel plate in the additive manufacturing process of the deformation element. In order to investigate the strain transmission from the printed part to the integrated steel plate, we built two prototypes which differ in terms of geometry. Both of them were investigated focusing on strain transmission and differences in the thermal stress applied. Measurements revealed a high impact of induced thermal stress from the manufacturing process. It can be reduced by having a lower exposed area in the built-up process. A good linearity (about 1.5%FS) for the first prototypes with a nominal load of 50 N is achieved, due to a good strain transmission from the printed part to the integrated steel plate. Thus, the suitability of the LPBF method for the structural integration of force sensors can be confirmed.

Combining fiber-reinforced composites with classic construction materials has tremendous potential for lightweight design but requires expensive equipment such as dies for injection molding. Additive manufacturing is cost-efficient for small volumes from fiber-reinforced materials. The MM3D (multi-material 3D printing) project analyses what technologies regarding interfaces, printer technology and identification of process parameters make economic fusion of 3D printing and established production processes possible.Results from pull-off tests suggest that adhesion between surfaces doubles with optimal substrate temperature and quadruples with appropriate plasma treatment. Because hybrid structures require printing on free forms, the authors employ a hexapod machine for rotational motion of an extruder head. The paper presents solutions for resulting challenges such as referencing pre-existing structures in the workspace, generating the printing path and coupling extrusion rate to printing speed. Finally, the identification of process parameters is addressed.Currently the technology is tested for manufacturing a nature inspired bike saddle.

The trend towards individualized products and the increasing demand for a greater variety of variants create new challenges for existing production environments and require a re-thinking of production. Established manufacturing systems that provide the desired flexibility are associated with significant productivity restrictions and are therefore unable to compete economically with production from rigid production lines. They are therefore often limited to serving niche markets. Consequently, an approach is needed that combines high productivity with high flexibility. For this purpose, this paper presents a new approach to manufacturing with an equally high productivity and flexibility, so-called value stream kinematics. The basic idea of value stream kinematics is to combine the advantages of specialized machines with the versatility of industrial robots. The vision behind this is to be able to realize entire value streams with uniform robot-like kinematics and no need for special machines.

Increasing flexibility in production systems is driving the use of robotic solutions. During their planning, robots must be placed according to their future operations. Thereby, influences such as space limitation, mechanical reach or cycle time must be taken into account. This paper introduces a concept based on the data farming methodology aiming at the optimal robot positioning for a given set of constraints. By simulating a defined sequence of robot operations with changing robot placement in a definable investigation area, each result data set is stored and analyzed. The simulation run with the best fitting robot position according to the defined key performance indicators is shown. For further evaluation, a clustering algorithm is used to evaluate the simulation results. The usage of the proposed method enables production planners to conveniently place robots in the optimal position according to their later application.

Due to disturbances or a lack of excitation during the measurements, conventional identification methods offer solutions with limited precision for the inertial parameters of industrial robots (IR). This paper introduces an approach to increase the rank of the identification matrix through additional equations from the frequency domain. In areas of lower frequencies, the total inertia that is affecting an axis is related to the amplitude of the frequency response of the rotational speed controlled system (RSCS). Another advantage of the presented method is the possible correction of friction effects via the phase information, which enables a higher identification accuracy. The frequency responses are measured during exciting trajectories, which stimulate low frequencies. Thereby, the approach generates additional equations, which enables the identification of more inertial parameters with a higher accuracy. In this paper, the measurement method and the identification algorithm are outlined.

Increasing complexity in production and factory automation represents a significant challenge in context of maintenance. One approach to address complexity is the implementation of automated industrial service bundles, which resemble complex business processes. To enable efficient usage of service bundles, their individual components are designed as functional modules in the form of independent micro services and are compatible with the paradigm of service-oriented architectures. For ubiquitous communication and data exchange between service entities, message-oriented middleware provides an adequate solution. This paper presents an approach for increasing efficiency in maintenance using a service bundle. The use case demonstrates the automated creation of service tickets enriched with necessary information from various sources.

A main prerequisite to applications in the Internet of Production is the integration of sensor data into an interconnected infrastructure, which in turn requires expert knowledge of sensor implementation as well as of network architecture design and communication protocols. To reduce complexity in this concern, the authors propose the SensOr Interfacing Language (SOIL), a domain-specific programming language for sensor interface definition and exchange of metrological data. Based on a meta-model, the functional interface can be designed without prior knowledge of the underlying communication details. It is composed of instances of components, parameters, functions and measurements as core elements of SOIL. Subsequently, the interface is automatically defined on protocol level and its software implementation is generated, leaving only the hardware-specific implementation to the developer. The domain-specific language is prototyped and evaluated by implementing and integrating interfaces for a virtual laser tracker and a distributed temperature measurement system, confirming the envisaged benefits.

Flexible intralogistics solutions are basic components to enable classic manufacturing companies to compete in dynamic and global production networks. Developments in the field of autonomous road traffic technology and the increasing availability of cost-efficient sensors allow the economic use of autonomous systems in various industrial sectors. One of the subareas of autonomous systems with economic and technological potential is the intralogistics material supply with driverless transport systems. While the usage between buildings of such systems is not yet economically feasible, development of cost-efficient sensors from other markets are opening up new approaches in this area of operation. Localization and navigation, which must work seamlessly in mixed indoor and outdoor usage scenarios, are particularly critical to the success of autonomous systems.In this paper, a solution for the localization and navigation of autonomous driverless transport systems in a mixed indoor and outdoor scenario is proposed. It focuses on the use of cost-efficient sensors to enable a mostly infrastructure-independent localization of each system. The proposed solution is validated and a first approach for a dynamic fusion of localization data is done.

In order to meet the high demand for aircrafts and the strict requirements regarding CO2 emissions in the future, new designs and technologies are necessary. Existing aircraft layouts and production processes are not designed for production rates of more than 75 aircrafts per month. One promising approach to reach significant improvements regarding weight and process cycle time is the combination of thermoplastic carbon fiber reinforced plastic aircraft structural elements, lining parts and cabin system elements to one integrated structure module. By this, improvements in productivity and environmental sustainability are accomplished likewise. This paper provides an overview of the Clean Sky 2 Multifunctional Fuselage Demonstrator, which comprises these topics. The focus of the scientific discourse lies on the concept of the full-scale assembly demonstrator especially discussing handling technology for position and shape adjustment of fuselage shells. Moreover, a systematic evaluation of thermoplastic welding technologies and a discussion regarding welding joint design is presented.

Industrial workers still face work-related musculoskeletal disorders daily and therefore physical support systems like exoskeletons are being developed. Making these wearable robots adaptable to different tasks and users in terms of its support characteristics is expected to generate greater performance and broader acceptance. By analyzing relevant elements of joint tasks in groups of humans and the environment exoskeletons are typically being used in, this paper derives the need for a framework allowing for adaption of the exoskeleton to the task, but also predictability for the user of the exoskeleton. A situation aware gain-scheduling controller with internal state feedback to the user is proposed as a means for adaption and predictability.

Machine learning models trained to predict certain outcomes bear great potential in a variety of applications. This research takes a step to elevate the hot forming technology of radial-axial ring rolling towards a fully digitalized and even more efficient forming technology. For successful machine learning the preprocessing step is essential. This paper presents current research regarding the most promising preprocessing approaches of time series data for the specific use case of classifying form errors of the radial-axial ring rolling process. By predicting form errors (in-situ), scrap and rework rates can be lowered due to an alert by the model for form errors in advance of a potential error, thus contributing to a more efficient industry. The data used exists in form of time series from log-data of an industrial used, single ring rolling machine. Concluding, the proposed preprocessing approaches are evaluated by comparing different model performances, trained on actual production data.

The majority of aircraft rivet holes are drilled with semi-automatic and manually controlled, pneumatically driven machines as full automation is often unsuitable due to workspace restrictions. Lightweight materials of difficult machinability complicate drilling. This is particularly relevant when drilling stack materials, where the machining parameters are determined by the most difficult to machine material layer. To provide reliable rivet connections, drilling in multiple steps, use of minimum quantity lubrication as well as subsequent manual deburring and cleaning are indispensable. Newly developed electrically driven semi-automatic advanced drilling units (ADUs) enable intelligent process layouts and online condition monitoring by evaluating integrated sensor data. Additionally, process parameters can be adapted to suit each material in the stack.In this paper, machine learning is applied to ADU sensor data to predict cutting forces and process conditions based on the internally measured currents of the ADU’s electric motors. The application of machine learning to ADU data is beneficial as drilling in the aerospace industry shows high repeatability and many produced holes, providing a large dataset. The machine learning methods linear regression, artificial neural network and decision tree are applied to force prediction. Furthermore, the k-nearest neighbour method is used to predict material, feed rate and lubrication state. Process monitoring based on the presented results minimizes manual control and rework by the identification of process deviations, resulting in a comprehensive quality assurance as well as optimal tool life exploitation. This leads to a step change in semi-automatic drilling of aircraft structures by overcoming a major productivity limitation.

AI will increasingly take over complex cognitive tasks and support human thinking and thus change the system of production management over decades to a cyber production management system. It has to be considered that AI can behave proactively, unexpectedly and incomprehensibly for humans. Here the human factor trust is essential and even becomes more relevant to determine sustainable relationship between humans and AI.This leads to the research question at the edge of production research: What does human trust in an AI assistant depend, on in production management decisions? To answer this question this article statistically examines a set of previously identified influencing factors on human trust. From these results an explanatory model is derived, which serves as a first design guideline for a socially sustainable human-AI interaction in production management.

This paper presents a method for machine component supervision with little to none prior knowledge of the machine, operating conditions and wear behavior. A hybrid approach based on unsupervised learning methods, consisting of an autoencoder network and clustering, to identify machine states and possible failure preceding anomalies is proposed. In order to cope with information sparsity, the model parameters of the unsupervised methods are derived automatically based on data distribution and a physical motivation. The approach was validated on a dataset of artificially introduced bearing faults. The gained clustering results show a general usability of the approach for condition monitoring with vibration data.

Machine learning offers a high potential for the prediction of manufacturing lead times. In practical operations the lack of defined processes and high-quality input data are a major obstacle for the use of machine learning. The method of process mining creates a better transparency of such workflows and enriches related data. This paper develops a method, which combines the benefits of machine learning and process mining with the goal of high accuracy lead time prediction. The method is focused on high variance processes and verified with a case study containing real industrial data from heavy engine assembly processes.

One central aspect of future high speed machining is the knowledge of the thermal behaviour of the machine tool and its motor spindle. A new temperature control for motor spindles with energy efficient synchronous reluctance drive is developed. In the first instance a finite element method model (FEM) is set up. This FEM aims to analyse the use case of nearly constant bearing temperatures within a defined range throughout the machining operation. By means of design of experiments (DOE), selected operating points of the speed-torque-characteristics are simulated with FEM considering different cooling parameters such as volume flow and inlet temperature. Machine learning algorithms are used to model the input-output-relationship in order to reduce the complex thermal motor spindle FEM. The applied algorithms are multiple linear regression and artificial neural network. The concept of temperature strategy, the FEM simulation results and the thermal models using machine learning algorithms are presented.

Vibrations have a significant influence on quality and costs in metal cutting processes. Existing methods for predicting vibrations in machine tools enable an informed choice of process settings, however they rely on costly equipment and specialised staff. Therefore, this contribution proposes to reduce the modelling effort required by using machine learning based on data gathered during production. The approach relies on two sub-models, representing the machine structure and machining process respectively. A method is proposed for initialising and updating the models in production.

In order to offer energy flexibility in energy markets in short time slots a fast and efficient processing and analysis of data from shop floor to production planning and control is necessary. To this end and to gain more knowledge, different datasets and sources have to be integrated. This paper proposes a conceptual architecture and a method for profiling energy data of manufacturing systems. This includes datasets from information systems as well as physical sources such as sensors, actuators or machine data. Real-life data often come with quality problems like missing and invalid values, outliers or duplicates. The key concept is to automatically identify the necessary metadata for including the dataset in an environment where further analysis and integration of datasets can take place. Moreover, a web service for profiling and visualizing data is implemented.

Especially in the context of Artificial Intelligence (AI) applications and increasing Overall Equipment Effectiveness (OEE) requirements, the use of data in production is gaining in importance. Applications in the field of process or condition monitoring use, for example, machine component parameters such as motor currents, travel speeds and position information. However, as the data is usually only accessible in the machine control systems in non-standard structures and semantics, while having a large number of potential variables, the identification and use of these parameters and data sources represents a significant challenge. This paper therefore presents an approach to automatically identify and assign machine parameters on the basis of time series data. For the identification, feature- and deep learning-based classification approaches are used and compared. Classification results show a general usability of the approaches for the identification of machine parameters.

In times of competitive market environments, offering customized products becomes a crucial success factor for manufacturing companies. High variance in the product portfolio leads to expanding costs along the value chain. These costs are not allocated cause-related, which results in cross-subsidization and competitive disadvantage. Process-oriented approaches proved high potential to improve cost allocation. The determination effort often is uneconomically high and results are arbitrarily imprecise. That might result in wrong decisions according the product portfolio and production system. The increasing execution of activities in information systems allows reductions in costs and time as well as increasing quality in process-oriented cost accounting. The aim of this paper is to develop a methodology that uses event data gathered in information systems to determine variant-specific process costs using Process Data Mining.

A Circulation Factory is a concept for a future closed-loop production system that combines product manufacturing with remanufacturing and recycling into one hybrid system for increased environmental and economic performance. So far, Circulation Factories have remained a vision. In the present paper, the initial framework for Circulation Factories is revisited and refined by analyzing real world production systems, which are already closing the loop partially. This way, a classification scheme for closed-loop production integration, and a list of product and factory features, which support closed-loop integration, are derived. The feature list is used to analyze if under current conditions the high impact case of Li-Ion batteries is a suitable scope of application for Circulation Factories.

In product development projects, companies try to meet more individual customer requirements by increasing the number of product variants. The product variants are no longer solely created physically, but also through different configurations of software, digital services and platforms. This extension of a physical system is called cyber-physical system (CPS). The development towards ‘System of Systems’ creates products, which fulfil required functions through the interaction of systems. The combination of physical and virtual objects makes cyber-physical systems significantly more complex than purely mechatronic products. The overall goal is to improve the cost-benefit ratio of cyber-physical systems. Therefore, system complexity is examined, in order to improve the design of CPS. In this paper a first step towards this goal is presented, the analysis and synthesis of description features of cyber-physical systems, which are capable to describe any CPS in a standardized logic and include all relevant dimensions that cause complexity.

The paper concentrates on the external elimination of bottlenecks by using congruent shared capacities within a non-hierarchical organized network. The study assumes that the capacity of assembly can be adapted by increasing the resources of the company. Conversely, the capacity of production is restricted by technological requirements and regulated by machine resources.To relieve capacity by external measures within a non-hierarchical organized network, an adapted process model is required. For this purpose, critical issues for the process flow within a network are identified and necessary regulations suggested.The paper presents the possibility of integrating the adapted approach into the operations management process as well as into systems of production planning and control and deduces the necessary information that is required to ensure the economic viability of bottleneck removal.

The shortening of innovation and product life cycles induces a development environment that is characterized by high dynamics and uncertainty. Furthermore, the increasing integration of functions from the domains of mechanics, electronics as well as software into complex systems leads to the need for extensive competencies and resources. Traditional development and organization patterns are increasingly unsuitable for successfully meeting these challenges. A solution approach that addresses the described challenges is the interorganizational integration of economically autonomous companies into industrial development networks. Development networks offer fast and cost-effective access to required competencies and technologies. This paper presents a methodology for the systematic configuration of the organizational structure of a development network according to the respective situation of relevant requirements in order to increase organizational agility. Therefore, design recommendations for the organizational structure of the development network are derived based on a typology of requirements.

Demographic change is leading to a decline in population in Germany. As a result, fewer skilled workers are available and many jobs remain vacant in various sectors, including the production industry. The turnover of employees favors the problem. In order to reduce employee turnover and to retain skilled workers, different general retention strategies are available for companies. However, a strategy adapted to the needs and requirements of workers at value-adding work stations is missing.This paper will present a research plan for the development of an employee-specific retention strategy as well as a first draft applicable to value-adding workplaces.

To fulfil the strategic importance of product development, manufacturing companies need to deal with the underlying processes. As plan-driven approaches have proven to be too inflexible for today’s market dynamics, development projects are alternatively carried out by using agile process models. However, while a too high degree of agility leads to efficiency problems, a too low degree brings major challenges in market competition. As a result, a multitude of hybrid approaches as an integration of agile and plan-driven processes arise. When defining those hybrid processes, the project context should be taken into account. The consideration of the context is decisive for an optimal process performance. Therefore, the focus of this research lies on the identification of relevant context factors. The knowledge of those factors enables the characterisation of each project individually and subsequently supports determining the appropriate level of agility as a prerequisite for adjusting the optimal hybrid development approach.

In order to ensure long-term competitiveness, production networks need to be designed in a continuous way that allows a quick adaptation to changing conditions. Today, due to high complexity in production networks adaptation needs are identified too late and the selection of the appropriate adaptation reaction is a time-intensive process. This turns resilience into one of the main challenges in creating robust global production networks. Therefore, in order to reduce the complexity in the design of production networks, this paper presents a systematization of adaptation needs in network configuration. The systematization can be used to facilitate the identification of adaptation needs by structuring adaptation options and reducing them to a finite number of options. The structured approach to derive the adaptation cases in response to changes serves as a first step to identify adaptation needs at an early stage.

The value stream method is widely used in the manufacturing industry to analyze and redesign value streams. However, with the increasing complexity of modern production systems, conducting a value stream analysis (VSA) and extracting reliable information for an accurate value stream design (VSD) becomes a challenging task for practitioners. Utilizing data from production-related IT systems offers the potential to support the value stream method with target-oriented analyses. Process mining (PM) supports the VSA by deriving process flows from production data as well as by analyzing process performances. Focused analyses of master data and transactional data enable reliable VSD activities without having to assume an oversimplified current state. This paper provides a framework for a continuously integrated data assistance within the value stream method, presenting a team structure, best practice procedures, and requirements for the application of the data assisted value stream method supported by examples from industry projects.

Todays’ increasing market volatility and product variety result in growing business processes complexity. To master the arisen challenges of high process complexity, the process performance description of the end-to-end order processing process (ETEOPP) is crucial. With the trend of digitalization in manufacturing companies, an increasing availability of data is created that can be used to master process complexity by data-based methods. One suitable method is process mining (PM), which offers a continuous analysis of event data from business information systems. This paper aims to describe the minimum viable dataset to thoroughly evaluate the process performance in ETEOPP by PM. Therefore, process performance indicators (PPI) are first scientifically derived by a systematic literature review and afterward defined across the ETEOPP as quantifiable parameters based on processes and data. By doing so, the required event log attributes, as well as corresponding data requirements, are presented and information systems for data extraction are pointed out.

Manufacturing companies are confronted with increasing market and innovation dynamics resulting from the demand for differentiated products. This leads to increasing importance of flexibility and versatility as objectives of production, especially in final assembly, since the highest variant-induced complexity occurs there. For this reason and to avoid investment costs for product-dedicated assembly lines, companies strive to assemble several products on one assembly line in a so-called model mix. Highly iterative product development is increasing in order to take the mentioned volatile market environment into account. However, this agile development methodology contradicts the sequential processes of production development and the solution space already defined by the existing production system. In order to address this conflict of objectives, this paper proposes a procedure for a holistic and cross-domain development cooperation for the integration of a new product on an existing assembly line.

Distributed ledger technologies such as the blockchain technology offer an innovative solution to increase visibility and security to reduce supply chain risks. This paper proposes a solution to increase the transparency and auditability of manufactured products in collaborative networks by adopting smart contract-based virtual identities. Compared with existing approaches, this extended smart contract-based solution offers manufacturing networks the possibility of involving privacy, content updating, and portability approaches to smart contracts. As a result, the solution is suitable for the dynamic administration of complex supply chains.

Global manufacturing companies are operating their manufacturing networks in an increasingly uncertain and complex environment. Consequently, they have to adapt their networks much faster to changing conditions. Thus, agile manufacturing networks that are able to react and adapt quickly at reasonable cost to those conditions are required. One of the main challenges in this context is the balancing of cost- and investment-intensive measures to increase flexibility and transformability on the one hand and the impediment of the overall changeability/agility by the associated increased capital tie-up on the other. Hence, this paper presents an approach to quantify and evaluate the benefits and costs of changeability measures. By systematically discussing a reasonable trade-off between costs and benefits, a dedicated, cost-effective agility for a manufacturing network is determined, enabling companies to improve their network design in terms of cost-effective changeability and thus ensure their competitiveness in the long term.

Analysis of data is currently being discussed in research and practice across a wide range of areas, but mainly with focus on production. Besides production, this is also relevant in areas such as complexity management. A major challenge in complexity management is the trade-off between standardization and customer-specific solutions. This is strengthened by increasing cost pressure, shortened product life cycles and a rising speed of innovation. Therefore, companies need transparency with regard to product-induced complexity in order to respond with an appropriate product variety and complexity management. In this paper a methodology is presented, which defines design guidelines for a targeted and event-based visualization of stakeholder-specific information requirements. To achieve this, a requirement morphology is developed to describe general as well as complexity-specific visualization requirements. On that basis, a dashboard structure is derived and a stakeholder-specific assignment of visualization types is considered.

Machinery and equipment manufacturers especially from high wage countries are increasingly under pressure to extend their competitive differentiation. Studies show that innovation in business models can create stronger differentiation than innovation in products and processes. Especially in the software industry, subscription-based business models have recently formed new champions who dominate the markets. Unfortunately, it is so far unclear what “subscription” means in the machinery and equipment industry, what offers it can contain and how the offers can be delivered. This work supports machinery and equipment manufacturers by answering these three fundamental questions. Firstly, an adequate definition of subscription business models in the machinery and equipment industry is introduced. Secondly, a holistic understanding of potential offers within a subscription business model is provided. Thirdly, supporting success factors and requirements for the creation of the offers are identified.