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Due to the tool-less fabrication of parts and the high degree of geometric design freedom, additive manufacturing is experiencing increasing relevance for various industrial applications. In particular, the powder bed fusion of metals using a laser beam (PBF-LB/M) process allows for the metal-based manufacturing of complex parts with high mechanical properties. However, residual stresses form during PBF-LB/M due to high thermal gradients and a non-uniform cooling. These lead to a distortion of the parts, which reduces the dimensional accuracy and increases the amount of post-processing necessary to meet the defined requirements. To predict the resulting residual stress state and distortion prior to the actual PBF-LB/M process, this paper presents the finite-element-based simulation tool AscentAM with its core module and several sub-modules. The tool is based on open-source programs and utilizes a sequentially coupled thermo-mechanical simulation, in which the significant influences of the manufacturing process are considered by their physical relations. The simulation entirely emulates the PBF-LB/M process chain including the heat treatment. In addition, algorithms for the part pre-deformation and the export of a machine-specific file format were implemented. The simulation results were verified, and an experimental validation was performed for two benchmark geometries with regard to their distortion. The application of the optimization sub-module significantly minimized the form deviation from the nominal geometry. A high level of accuracy was observed for the prediction of the distortion at different manufacturing states. The process simulation provides an important contribution to the first-time-right manufacturing of parts fabricated by the PBF-LB/M process.

The powder bed fusion of metals using a laser beam enables the tool-free fabrication of complex part geometries with merging areas and rapid cross-sectional changes. Together, these geometry features represent a structural transition leading to the formation of shrink lines. These notches on the surface of the part reduce the dimensional accuracy and the fatigue resistance. Shrink lines arise in various materials, with the dimensions of the shrink line depending on the geometric design. The formation mechanisms and influencing parameters of shrink lines have not been investigated yet. This paper demonstrates the extent of influence of the part geometry on the shrink line formation, which was quantified by varying the design of a representative structural transition. In addition, the positions of the specimens on the build platform and the scanning strategy were varied for deriving a cause-effect relationship using process monitoring. The results demonstrated that the shrink line formation was mainly caused by a local overheating at the structural transition and the global cooling behavior. The radius at the structural transition indicated the most significant impact among the investigated geometric parameters. The shrink line dimensions depended significantly on the orientation of the specimens on the build platform and the local scanning strategy applied at the height of the structural transition. The results can be used to reduce shrink lines by re-designing the part and to adjust the manufacturing strategy for structural transitions.

Manufacturing companies face a volatile environment with multiple sustainability challenges threatening their competitiveness. To meet these challenges, they need to adapt quickly to the changing demands and environment to improve their sustainability in production. Therefore, this contribution presents a holistic and multidimensional model for sustainable production systems based on three levels: elements, overarching elements, and dimensions. First, a systematic literature review (SLR) was conducted to capture the state of research. Second, two studies were conducted to identify and cluster the relevant sustainability elements. One of the studies is based on the SLR findings, which were verified by an expert survey. The other is based on a document analysis of sustainability reports. Finally, both studies are compared, and the results are presented. In total, 40 elements, such as energy efficiency, and eight overarching elements, such as efficiency, can be identified in addition to the three dimensions of economic, social, and environmental sustainability.

Categorised as a directed energy deposition process, wire arc additive manufacturing (WAAM) is a promising technology for fabricating large-scale structures and components across various industries. However, the quality of WAAM parts depends on the tool trajectory, as it affects the temperature distribution. Ideally, temperature-related issues, such as overheating, are minimised by choosing an optimal deposition sequence. Selecting the deposition sequence solely on intuition is an unreliable approach, particularly when dealing with complex and irregular parts. This paper introduces a simplified WAAM simulation (SWS) model, which was embedded into an optimisation procedure for determining the optimal deposition sequence of thin-walled WAAM parts. The proposed method is based on a semi-analytical function, which was calibrated and validated using thermal histories obtained from finite element simulations. The findings showed that the proposed SWS model can reproduce the average thermal history at predefined positions within a sample part with minimal computational effort. Moreover, the optimisation successfully identified optimal deposition sequences for producing the sample part regarding a given optimisation criterion. The outcomes of this research contribute to the tool trajectory planning process for WAAM parts. An optimal deposition sequence improves WAAM part quality, increasing the usability of WAAM technology in the production industry.

ABSTRACTValue stream mapping is a well-established tool for analyzing and optimizing value streams in production. In its conventional form, it requires a high level of manual effort and is often inefficient in volatile and high-variance environments. The idea of digitizing value stream mapping to increase efficiency has thus been put forward. A common means suggested for digitization is Process Mining, a field related to Data Science and Process Management. Furthermore, adding sustainability aspects to value stream mapping has also been subject to research. Regarding the ongoing climate crisis and companies’ endeavors to improve overall sustainability, integrating sustainability into value stream mapping must be deemed equally relevant. This research paper provides an overview of the state of the art of Process Mining-based and sustainability-integrated value stream mapping, proposes a framework for a combined approach, and presents technical details for the implementation of such an approach, including a validation from practice.

During the powder bed fusion of metals using a laser beam (PBF-LB/M), an inert atmosphere is maintained in the build chamber to avoid reactions of the liquid metal with ambient air leading to the creation of oxides or nitrides, which alter the mechanical properties of the processed part. A continuous gas flow is guided over the process zone to remove spatters and fumes. This flow induces a convective heat transfer from the molten metal to the gas, which, depending on the level of the heat flow, may alter the melt pool dimensions by influencing the cooling rate. The present work investigated these phenomena with single-line trials, both experimentally and numerically. For this reason, a smoothed-particle hydrodynamics model was utilized to investigate the temperatures of the melt pool, cooling rates, and the integral heat balance with various gas atmospheres. In parallel, an on-axis pyrometer was set up on an experimental PBF-LB/M machine to capture the surface emissions of the melt pool. The atmosphere in the simulations and experiments was varied between argon, helium, and two mixtures thereof. The results showed a slight increase in the cooling rates with an increasing fraction of helium in the process gas. Consistently, a slight decrease in the melt pool temperatures and dimensions was found.

The role of the inert processing gas during the powder bed fusion of metals using a laser beam (PBF-LB/M) is to prevent oxidation and remove process by-products, such as metal vapor and spatter particles. The present study aims to unveil additional impacts of using argon (Ar), helium (He), and two mixtures thereof as the processing gas on the material properties of a high-strength Al–Cu–Li–Ti alloy fabricated by PBF-LB/M. The part density, microstructure, static tensile properties, and volatile element evaporation were characterized as functions of the processing gas. Decreased porosity levels and increased melt penetration depths were found across a range of processing parameters when increasing the fraction of He in Ar indicating a more stable process and melt pool dynamics. A trend towards increasing yield and ultimate tensile strength was also observed and was attributed to a slightly refined grain size when processing under He-containing gases. The process gas had no significant influence on the evaporation of alloying constituents in the material. Overall, several advantages of using He-containing process gases over pure Ar in PBF-LB/M are demonstrated and discussed.© 2017 Elsevier Inc. All rights reserved.

Additive manufacturing is becoming increasingly important in the construction industry. Wire arc additive manufacturing (WAAM) can be integrated into the selective paste intrusion (SPI) to enable the simultaneous printing of reinforced concrete. The bond behavior of a WAAM reinforcement was investigated with pull-out tests and compared to alternative reinforcement types to analyze the stress transfer between the different components. In the first step, the surface of all the reinforcement types was recorded using a laser-based line scan measuring system. This permits the evaluation of the surface parameters, such as the surface roughness Rq, or the related rib area fR. The WAAM reinforcement showed a bond behavior in the pull-out tests that was comparable to a reinforcing steel bar. Both the bond stresses achieved, and the occurring scatter of the measurement results at the characteristic slip values were almost the same. Even without existing transverse ribs, the WAAM reinforcement reached maximum bond stresses similar to the reinforcing steel. An evaluation of the surface roughness revealed a linear relationship with the maximum bond stress achieved with a logarithmic scaling of Rq. The bond work Wτ, which is a measure of the system stiffness, showed that WAAM reinforcements and reinforcing steel bars have approximately similar behavior.

Lithium metal is a favored anode material in various post-lithium-ion battery types. Developing processing routines for lithium anodes is necessary to pave the way for large-format lithium metal batteries. Laser cutting is a feasible production process to create the required electrode contours. In the scope of this work, model calculations were used to derive implications of the cell design on the relevant range of lithium layer thicknesses. Furthermore, nanosecond-pulsed laser cutting was evaluated for separating 50 μm-thick lithium foils. Cause-effect relationships between process parameters and quality criteria were analyzed through empirical investigations. The ablation thresholds for various pulse durations were determined experimentally. Different process regimes were identified using scanning electron microscopy with explosive boiling at high fluences as the most efficient ablation mechanism enabling cutting speeds of up to 6.6 m s−1. The influence of the peak pulse fluence, the pulse frequency, the pulse duration, and the pulse overlap on the formation of melt superelevations at the cut edge was studied using laser scanning microscopy. The presented results contribute to a better understanding of the nanosecond-pulsed laser process and provide a basis for developing tailored process strategies for laser cutting of lithium metal within industrial-scale battery production.

This study presents a method to analyze the electrical resistance of planar contacts. The method can determine whether the contact resistance of the joint exhibits linear or non-linear behavior. By analyzing the current distribution over a planar contact, it can be determined whether an area-based contact resistance is justified or if other parameters define the contact resistance. Additionally, a quantitative evaluation of the factors that affect the measurement accuracy, including the positioning, the measurement equipment used, and the influence of the current injection on the sense pin was conducted. Based on these findings, the electrical contact resistance and the mechanical ultimate tensile force of a silver-filled epoxy-based adhesive are analyzed and discussed. The layer thickness and the lap joint length were varied. Overall, the investigated adhesive shows a low contact resistance and high mechanical strength of the same magnitude as that of well-established joining techniques, such as welding, press connections, and soldering. In addition to evaluating the mechanical and electrical properties, the electric conductive adhesive underwent an economic assessment. This analysis revealed that the material costs of the adhesive significantly contribute to the overall connection costs. Consequently, the effective costs in mass production are higher than those associated with laser beam welding.

During strategic network design, not only strategic but also operational decisions must be made long before a production network is put into operation. These include determining the location and size of inventories within the production network and setting operational parameters for production lines, such as the shift model. However, the large solution space comprising a high number of highly uncertain design parameters makes these decisions challenging without decision support. Therefore, data farming offers a potential solution, as synthetic data can be generated via the execution of multiple simulation experiments spanning the solution space and then analyzed using data mining techniques to provide data-based decision support. However, data farming has not yet been applied to strategic network design due to the lack of adequate solution space management. To address this shortcoming, this paper presents a structured solution space management approach that integrates production network-specific requirements and Design of Experiment (DoE) methods. The approach enables converting the solution space in strategic network design into individual input data sets for simulation experiments, generating a comprehensive database that can be mined for data-based decision support. The applicability and validity of the comprehensive approach are ensured via a case study in the automotive industry.

Dynamic machine tool simulation models can be used for various applications such as process simulations, design optimization, and condition monitoring. However, all these applications require that the model replicates the real system’s behavior as accurately as possible. Next to carefully building the model, the parameterization of the model, that is, determining the parameter values the model is based upon, is the most crucial step. This paper describes the application of both sensitivity-based and black-box parameter identification to a machine tool. It further provides a comparison between these two methods and the method of sequential assembly. It is shown that both methods can increase the mode shape conformity by more than 25% and significantly reduce damping deviations. However, sensitivity-based parameter identification is the most economical method, offering the chance to update a dynamic machine tool model within minutes.

Abstract Laser powder bed fusion (L-PBF) is increasingly used to fabricate functional parts used in safety-relevant applications. To ensure that the sophisticated part specifications are achieved, 100% quality inspections are performed subsequent to the buildup process. However, knowledge about the detectability of defects in L-PBF parts using NDT methods is limited. This paper analyzes the suitability of NDT techniques in an ex situ environment, in particular active infrared thermography, neutron grating interferometry (nGI), X-ray computed tomography, and ultrasonic testing for the examination of L-PBF parts made from Inconel 718. Based on a test specimen with artificially inserted defects with varying dimensions and depths, these NDT techniques were compared in terms of their attainable resolution and thus defect detection capability. The empirical studies revealed that nGI shows the highest resolution capability. It was possible to detect defects with a diameter of 100–200 m at a depth of 60–80 $${\upmu } \hbox {m}$$ μ m . The results are discussed with regard to their relevance for the examination of L-PBF parts and thus not only contribute to a better understanding of the potential of the NDT techniques in comparison but also assist stakeholders in additive manufacturing in evaluating the suitability of the NDT techniques investigated.

The residual stress state of the machined sub-surface influences the service quality indicators of a component, such as fatigue life, tribological properties, and distortion. During machining, the radius of the cutting edge changes due to tool wear. The cutting-edge rounding significantly affects the residual stress state in the part and the occurring process forces. This paper presents a tool wear prediction model based on in-process measured cutting forces. The effects of the cutting-edge geometry on the force behavior and the machining-induced residual stresses were examined experimentally. The resulting database was used to realize a Machine Learning algorithm to calculate the hidden value of tool wear. The predictions were validated by milling experiments using rounded cutting edges for different process parameters. The microgeometry of the cutting edge could be determined with a Root Mean Square Error of 8.94 μm.

Laser metal deposition with coaxial wire feeding is a directed energy deposition process in which a metal wire is fed to a laser-induced melt pool. Oxidation occurring during the process is a major challenge as it significantly influences the mechanical properties of the produced part. Therefore, an inert gas atmosphere is required in the high temperature process zone, whereby local shielding offers significant cost advantages over an inert gas chamber. In this work, a novel local shielding gas nozzle was developed based on basic methods of fluid mechanics. A gas flow-optimized prototype incorporating internal cooling channels was additively manufactured by laser-powder bed fusion and tested for its effectiveness via deposition experiments. Using the developed local shielding gas concept, an unwanted mixing with the atmosphere due to turbulence was avoided and an oxide-free deposition was achieved when processing a stainless steel ER316LSi wire. Furthermore, the effects of the shielding gas flow rate were investigated, where a negative correlation with the melt pool temperature as well as the weld bead width was demonstrated. Finally, a solid cuboid was successfully built up without oxide inclusions. Overheating of the nozzle due to reflected laser radiation could be avoided by the internal cooling system. The concept, which can be applied to most commercially available coaxial wire deposition heads, represents an important step for the economical application of laser metal deposition.

The task of remaining useful life (RUL) estimation is a major challenge within the field of prognostics and health management (PHM). The quality of the RUL estimates determines the economical feasibility of the application of predictive maintenance strategies, that rely on accurate predictions. Hence, many effective methods for RUL estimation have been developed in the recent years. Especially deep learning methods have been among the best performing ones setting new record accuracies on bench mark data sets. However, those approaches often rely on numerous and representative run-to-failure sequences of the components under investigation. In real-world use cases, this kind of data (i.e. run-to-failure sequences and RUL labels) is hardly ever present. Therefore, this paper proposes a new, data-efficient method, which is based on Gaussian process classification to derive abstract health indicator (HI) values in a first step, and warped, monotonic Gaussian process regression for indirect RUL estimation in a second step. The proposed approach does neither rely on entire run-to-failure sequences nor on any RUL labels and was tested on the benchmark C-MAPSS turbo fan and FEMTO bearing data sets, achieving comparable results to the state-of-the art whilst using only a small fraction of the available training data. Hence, the proposed approach allows RUL estimation in use cases, in which gathering enough failure data for the application of deep learning models is infeasible.

The dynamic characterization of structures by means of modal parameters offers many valuable insights into the vibrational behavior of these structures. However, modal parameter estimation has traditionally required expert knowledge and cumbersome manual effort such as, for example, the selection of poles from a stabilization diagram. Automated approaches which replace the user inputs with a set of rules depending on the input data set have been developed to address this shortcoming. This paper presents an alternative approach based on Bayesian optimization. This way, the possible solution space for the modal parameter estimation is kept as widely open as possible while ensuring a high accuracy of the final modal model. The proposed approach was validated on both a synthetic test data set and experimental modal analysis data of a machine tool. Furthermore, it was benchmarked against a similar tool from a well-known numerical computation software application.

The Wire and Arc Additive Manufacturing (WAAM) process has a high potential for industrial applications in aviation. The interlayer temperatures influence the dimensions and geometric deviations of the part. Monitoring the absolute interlayer temperature values is necessary for quantifying these influences. This paper presents an approach for determining the absolute values of the interlayer temperatures during the process using Ti-6Al-4V. The emissivity and transmittance are determined and calibrated, enabling precise thermographic measuring during the WAAM process. The recorded thermographic data are then compared to signals of thermocouples so that the absolute temperature values can be aligned. The methodology is validated by its transfer to measure the interlayer temperature at different regions of interest. The effect of a heat accumulation using Ti-6Al-4V in WAAM was determined. The methodology enables a reproducible and non-tactile measurement of the interlayer temperature during the WAAM process. The results show that with an interlayer temperature of 200 °C, a heat accumulation occurs within a layer. The heat accumulates in the center of the layer because the free ends of the layer cool down faster than the center of the layer.

The use of particle loaded inks in binder jetting offers several advantages, such as the improved sintering ability or the adaption of material properties in a printed part. Various aspects have to be considered for the preparation and the printability. A high stability is necessary, therefore agglomeration and sedimentation of the particles must be avoided. It is also important that long filaments and thus satellite droplets are not formed during printing. In this study, inks loaded with graphite nanoparticles were investigated regarding their stability and drop formation behavior to qualify them for reliable processing in binder jetting. Polyvinylpyrrolidone (PVP) was used as a binder for the binder jetting process and simultaneously as a dispersing agent for the nanoparticles. The PVP mass fraction was investigated for its dispersion effect. To meet the print head requirements, the particle size distribution had to be below 1 μm. To destroy agglomerates with a diameter above this limit, ultrasonic treatment was used. The duration of the ultrasonic treatment was examined to identify the minimum time required to obtain a well dispersed ink. Particle loaded inks were then prepared and the fluid mechanical properties were determined for inks with different particle mass fractions. Monitoring of the drop formation provided information about a stable drop generation, the filament lengths, and the velocity of the drops. It was possible to qualify inks with a maximum particle mass fraction of 10 m% for the binder jetting process.

The reliable production of high-quality lithium-ion battery components still poses a challenge, which must be met to cope with their rising demand. One key step in the production sequence is the process of cell-internal contacting, during which the electrode carrier foils of the anode and the cathode are joined with the arrester. This is usually done with ultrasonic or laser beam welding. Both joining processes, however, show limitations concerning the quality of the weld. This paper presents a new approach for cell-internal contacting by using micro-friction stir spot welding. Welding experiments were conducted in which joints with high mechanical strengths were produced. It was also shown that large stacks with foil numbers of 100 can be joined in only a few tenths of a second. The process is therefore especially of interest for the fast production of large-scale battery cells or other new types of high-energy-dense battery cells.

In binder jetting (BJ), an ink is inserted layerwise into a powder bed to selectively bond the particles in the cross-section of a part. By predicting the penetration depth of the ink, the ideal layer thickness for BJ can be set. Each layer should be penetrated with ink. Insufficient penetration will result in a poor layer bond and a low strength of the part; over-penetration will impede a dimensionally accurate production, as the ink will leak from the sides of the part and unintentionally solidify the powder in these areas. The Washburn equation has been used for the calculation of the penetration depth in various fields, such as hydrology or with loose powders. However, a transfer to the BJ process is difficult due to the preferably compact powder bed and the fine particles. In more compact powder beds, the small radii with their greater capillary pressure and their distribution in the layer have a high influence on the penetration depth. This work shows an adaptation of the Washburn equation for powder beds in BJ and a new approach to determine the effective pore radius for calculating the penetration depth. A weighted pore radius was introduced, which accounts for the spatial distribution of the pores in the powder bed and the acting capillary pressure. The validation was performed with two different powders by experimentally simulating the BJ process through the infiltration of a drop into a powder bed. The weighted radius was used in the Washburn equation to calculate the penetration depth. The results were compared with those models from the literature and experimental data, and a good agreement between the calculation and the experiment was found.

High-fidelity machine tool models are needed for condition monitoring, machine tool development, and process simulation. To accurately predict the dynamic behavior of their real counterparts, these models have to be identified, meaning that the values for the involved physical model parameters have to be found by comparing the model with measured data from its real counterpart. As of now, this can only be performed automatically for comparably simple models, which are only valid under limiting assumptions. In contrast, parameter identification for predictive high-fidelity models requires cumbersome manual effort in many intermediate steps. The present work addresses this problem by showing how to automatically identify the parameters of a complex structural dynamic machine tool model using global sensitivity analysis. The capability of the proposed approach is demonstrated in two steps for simulated reference data: first, with a model being able to perfectly replicate the reference data, and second, with a disturbed model, which can only approximate the reference because modeling is present. It is shown that, in both cases, globally valid model parameters, which lead to high conformity with the reference data, can be found, paving the way for calibrating models based on experimental reference data in future work.

Coaxial Laser Metal Deposition with wire (LMD-w) is a valuable complement to the already established Additive Manufacturing processes in production because it allows a direction-independent process with high deposition rates and high deposition accuracy. However, there is a lack of knowledge regarding the adjustment of the process parameters during process development to build defect-free parts. Therefore, in this work, a process development for coaxial LMD-w was conducted using an aluminum wire AlMg4,5MnZr and a stainless steel wire AISI 316L. At first, the boundaries for parameter combinations that led to a defect-free process were identified. The proportion between the process parameters energy per unit length and speed ratio proved crucial for a defect-free process. Then, the influence of the process parameters on the height and width of single beads for both materials was analyzed using a regression analysis. It was shown that linear models are suitable for describing the correlation between the process parameters and the dimensions of the beads. Lastly, a material-independent formula is presented to calculate the height increment per layer needed for an additive process. For future studies, the results of this work will be an aid for process development with different materials.

Robotic machining is a promising technology for post-processing large additively manufactured parts. However, the applicability and efficiency of robot-based machining processes are restricted by dynamic instabilities (e.g., due to external excitation or regenerative chatter). To prevent such instabilities, the pose-dependent structural dynamics of the robot must be accurately modeled. To do so, a novel data-driven information fusion approach is proposed: the spatial behavior of the robot’s modal parameters is modeled in a horizontal plane using probabilistic machine learning techniques. A probabilistic formulation allows an estimation of the model uncertainties as well, which increases the model reliability and robustness. To increase the predictive performance, an information fusion scheme is leveraged: information from a rigid body model of the fundamental behavior of the robot’s structural dynamics is fused with a limited number of estimated modal properties from experimental modal analysis. The results indicate that such an approach enables a user-friendly and efficient modeling method and provides reliable predictions of the directional robot dynamics within a large modeling domain.

Powder Bed Fusion of Metals using a Laser Beam (PBF-LB/M) is increasingly utilized for the fabrication of complex parts in various industrial sectors. Enabling a robust and reproducible manufacturing process is one of the main goals in view of the future success of PBF-LB/M. To meet these challenges, alloys that are specifically adapted to the process are required. This paper demonstrates the successful interplay of simulation studies with experimental data to analyze the basic phenomena of in situ alloying. The meshless Smoothed-Particle Hydrodynamics (SPH) method was employed for the numerical simulation of two-component powder systems considering both thermodynamics and fluid mechanics in the solid and the melt phase. The simulation results for the in situ alloying of stainless steel 316L blended with the aluminum alloy AlSi10Mg were enriched and validated with the data from a novel experimental test bench. The combination of both approaches can enhance the understanding of the process for in situ alloying. Therefore, future investigations of the PBF-LB/M process with multi-component powder systems can benefit from detailed numerical studies using SPH.

Reactive particles consisting of nickel and aluminum represent an adaptable heat source for joining applications, since each individual particle is capable of undergoing a self-sustaining exothermic reaction. Of particular interest are particles with intrinsic lamellar microstructures, as they provide large contact areas between the reactants nickel and aluminum. In this work, the exothermic reaction as well as the microstructure of such lamellar reactive particles produced by high energy planetary ball milling were investigated. Based on statistically designed experiments regarding the milling parameters, the heat of reaction was examined by means of differential scanning calorimetry (DSC). A statistical model was derived from the results to predict the heat of reaction as a function of the milling parameters used. This model can be applied to adjust the heat of reaction of the reactive particles depending on the thermal properties of the joining partners. The fabricated microstructures were evaluated by means of scanning electron microscopy (SEM). Through the development of a dedicated SEM image evaluation algorithm, a computational quantification of the contact area between nickel and aluminum was enabled for the first time. A weak correlation between the contact area and the heat of reaction could be demonstrated. It is assumed that the quantification of the contact areas can be further improved by a higher number of SEM images per sample. The findings obtained provide an essential contribution to enable reactive particles as a tailored heat source for joining applications.

Friction press joining (FPJ) is an innovative joining process for bonding plastic components and metal sheets without additives in an overlap configuration. This paper focuses on the resulting bond strength. Tensile tests showed that the direct bonds produced by FPJ have either an equivalent or a higher bond strength compared to adhesive bonds. For the material combination of HD-PE and EN AW-6082-T6, an equivalent bond strength was achieved. In contrast, for the material combinations PA6-GF30 with EN AW-6082-T6 and PPS-CF with EN AW-2024-T3, higher tensile shear strengths were achieved via the FPJ technology. In addition to the technical considerations, this paper presents an evaluation of the technological maturity of FPJ. It was found that the basics of the technology are already well developed, and prototypes for showing the applicability have already been manufactured. The last part of this paper deals with the classification of FPJ into the standard for manufacturing processes, according to DIN 8593. The authors suggest a categorization into Activation bonding (item 4.8.1.3). These investigations show the high technical potential of FPJ for joining plastic components with metals.

Water-based processing of electrodes is associated with an enhanced environmental footprint for lithium-ion battery (LIB) production in conjunction with reduced costs. This trend is accompanied by an increasing demand for electrode dispersion processing in inkjet printing. However, most of the dispersion components show a low inherent dispersibility with poor stability in aqueous formulations. This is particularly important when it comes to qualifying electrode dispersions for use in inkjet printing, since the effect of agglomeration and sedimentation effects must be effectively prevented. Therefore, additives are needed to improve the dispersive behavior. This paper analyzes the suitability of dispersants for organic electrode components, in particular graphite and carbon black. An empirical approach was devised on the basis of comprehensive theoretical considerations. Empirical investigations revealed that the utilization of polyvinylpyrrolidone (PVP) favored the enhanced stabilization of graphite nanoparticles. The addition of Triton X-100 (TX-100) resulted in an improved stabilization of carbon black. Based on these empirical studies, a methodology was derived, which supports the application of suitable dispersants in printable dispersions.

Friction press joining is an innovative joining process for bonding plastics and metals without additives in an overlap configuration. This paper presents for the first time a model-based approach for designing a multi-variable model predictive control (MPC) for friction press joining. For system modeling, a differential equation based on the heat flows was proposed and modeled as a torque-dependent function. With this model, it is possible to consider cross-effects between the axial force and the friction zone temperature. With this theoretical approach, adaptive model-predictive process control was implemented and validated for different material combinations (EN AW-6082-T6; EN AW-2024-T3; PE-HD; PA6-GF30; PPS-CF). It could be shown that the MPC has excellent control accuracy even when model uncertainties are introduced. Based on these findings, a 1D Finite Differential Method multi-layer model was developed to calculate the temperature in the plastic component, which is not measurable in situ (r = 0.93). These investigations demonstrate the high potential of the multi-variable MPC for plastic-metal direct joining.

Preliminary studies have shown the superiority of convolutional neural networks (CNNs) compared to other network architectures for determining the surface quality of friction stir welds. In this paper, CNNs were employed to detect cavities inside friction stir welds by evaluating inline measured process data. The aim was to determine whether CNNs are suitable for identifying surface defects exclusively, or if the approach is transferable to internal weld defects. For this purpose, 120 welds were produced and examined by ultrasonic testing, which was the basis for labeling the data as “good” or “defective.” Different types of artificial neural network were tested for predicting the placement of the welds into the defined classes. It was found that the way of labeling the data is significant for the accuracy achievable. When the complete welds were uniformly labeled as “good” or “defective,” an accuracy of 98.5% was achieved by a CNN, which was a significant improvement compared to the state of the art. When the welds were labeled segment-wise, an accuracy of 79.2% was obtained by using a CNN, showing that a segment-wise prediction of the cavities is also possible. The results confirm that CNNs are well suited for process monitoring in friction stir welding and their application enables the identification of various defect types.

In the work described here, Gaussian process regression was applied to predict the ultimate tensile strength of friction stir welds through data evaluation and to therefore avoid destructive testing. For data generation, a total of 54 welding experiments were conducted in the butt joint configuration using the aluminum alloy EN AW-6082-T6. Four tensile samples were taken from each of the 54 experiments and the resulting ultimate tensile strength of the weld seam segment was modeled as a function of the weld’s surface topography. Further models were created for comparison, which received either the process variables or the process parameters to predict the ultimate tensile strength. It was shown that the ultimate tensile strength can be accurately predicted based on the weld’s surface topography. Especially for low welding speeds, the correlation coefficients between the true and the predicted ultimate tensile strength were high. However, overall, even higher correlation coefficients could be achieved when providing the process variables or the process parameters to the model. In conclusion, it was shown that the developed Gaussian process regression model is a powerful approach for replacing destructive testing and for predicting ultimate tensile strength based solely on data that can be collected non-destructively.

Friction stir welding (FSW) is an innovative solid-state welding technology that produces high quality joints and is widely used in the aerospace industry. Previous studies have revealed welding temperature to be a decisive factor for joint quality. Consequently, several temperature control systems for FSW have been developed. These output feedback control systems usually require delicate and expensive temperature measuring equipment, which reduces their suitability for industrial practice. This paper presents a novel state feedback system of the welding temperature to remedy this shortcoming. The system uses a physical model of the FSW process (digital twin) for the determination of the welding temperature signal from the process torque signal. The digital twin is based on a multi-input nonlinear time invariant model, which is fed with the torque signal from the spindle motor. A model-based ℒ1 adaptive controller was employed for its robustness with respect to model inaccuracies and fast adaption to fluctuations in the controlled system. The experimental validation of the feedback control system showed improved weld quality compared to welded joints produced without temperature control. The achieved control accuracies depended on the results of the temperature calculation. Control deviations of less than 10 K could be achieved for certain welding parameters, and even for a work piece geometry, which deliberately caused heat accumulation.

Friction stir welding is a modern pressure welding process, which is particularly suitable for aluminum alloys. Several studies have been conducted to investigate the interrelations between the process parameters, such as the welding speed and the tool rotational speed, and the resulting mechanical properties of the joint. This study explored the connections between the surface topography of the welds, such as the flash height and the seam underfill, and their mechanical properties (ultimate tensile strength; elongation at break; and Vickers hardness). For this purpose, a total of 54 welding experiments at three different welding speeds were conducted using the aluminum alloy EN AW-6082-T6. The welded specimens were examined using visual inspection, topographic analysis, metallography, hardness measurements, and uniaxial tensile tests. Afterward, a statistical analysis was performed in order to determine the correlation coefficients between the surface topography and the mechanical properties of the welds. The strongest correlations were between the surface topography and the ultimate tensile strength. Thereby, the most pronounced relations were found between the seam underfill as well as the arc texture formation of the weld and its ultimate tensile strength. The interrelations between the surface topography and the elongation at break, as well as the hardness of the welds, were less pronounced. The higher the welding speed became, the less pronounced the interrelations were. The results show the potential of a non-destructive monitoring system based on the topography to support the prediction of the acceptability of welded parts.

Lightweight design is gaining in importance throughout the engineering sector, and with it, workpieces are becoming increasingly complex. Particularly, thin-walled parts require highly accurate and efficient machining strategies. Such low-rigidity structures usually undergo significant deformations during peripheral milling operations, thus suffering surface errors and a violation of tolerance specifications. This article introduces a general approach to mitigating surface errors during the peripheral milling of thin-walled aluminum workpieces. It incorporates an analytical approach to predicting surface-error characteristics based on geometrical quantities and process parameters, which is presented in detail. Milling experiments, including geometrical measurements of the samples, have been performed to verify the approach. The approach allows for a pre-selection of parameter sets that result in surface errors that can be compensated with minimal effort. Additionally, the introduced model offers a simple criterion to assess potential error mitigation by applying the respective tool-path adjustments. In doing so, the amount of costly numerical simulations or experiments is significantly reduced.