Efficient management of end-of-life lithium-ion batteries (LIBs) is critical to advancing circular economy goals. To support informed decision-making for circular strategies (ReX) such as reuse, remanufacturing, repair, or recycling, this study proposes a systematic and adaptive framework D-BReX (Decision-Making Framework for Circularity and Assessment of ReX in Battery Life Cycles). The framework is designed to leverage structured data from the Digital Battery Passport (DBP) and integrates it with fuzzy Failure Mode and Effects Analysis (FMEA) and State of Health (SOH) assessments. This hybrid approach allows for risk- and condition-based evaluation at the pack, module, and cell levels, supporting scalable, safety-conscious ReX decisions. The framework includes cost modelling to quantify trade-offs between diagnostic thoroughness and operational efficiency. A case study on retired E-bike battery packs demonstrates how D-BReX adapts to varying failure profiles and supports optimal ReX strategies under different economic and safety constraints. The findings affirm that D-BReX is effective in guiding second-life battery decisions, offering a structured yet flexible approach aligned with circular economy (CE) goals. Its iterative structure also enables continuous enhancement of DBP data, improving traceability and long-term decision quality.
Electroplated zinc-nickel (Zn-Ni) coatings are commonly applied as corrosion protection for steel components in demanding industrial environments. However, due to high process complexity, coupled with a limited process transparency and irregular chemical monitoring, achieving resource efficiency and predictive control remains challenging. This paper presents a data-driven approach for monitoring and modelling of an alkaline Zn-Ni barrel plating process at the process and product levels using machine learning (ML) techniques. A methodology was developed within a cyber-physical production system framework for training the models based on a controlled laboratory environment (20 L bath volume) and knowledge transfer on an industrial electroplating plant (13,000 L bath volume). Different supervised algorithms were trained with a limited set of input features to predict metal concentrations of the bath, coating thickness, nickel ratio in the coating and aggregated coating quality indicator. Despite data scarcity and process variability, the selected best models demonstrate accurate prediction rates (>86%). The results show that experimentally generated data can improve product-level modelling in the industrial environment, while process-level transfer remains limited when relevant industrial interventions, such as zinc dosing, are not recorded.
Additive manufacturing (AM) of high-speed steel (HSS) is of great interest for the manufacture of optimized forming and cutting tools. This study aims to systematically develop the sinter-based Cold Metal Fusion process for M2 steel and enable manufacture of complex shaped part designs. Green parts were printed with surface roughness, R-a, of 21 mu m and density of 5.40 g/cm(3). A sintering temperature of 1248 degrees C yielded a sintered density of >99% theoretical density and significantly decreased surface roughness to 8-10 mu m. The microstructure consisted of martensite, and of grain boundary and finer intragranular MC and M6C carbides. As-sintered Vickers hardness was 576 + 10 HV5 and increased to 933+ 27 HV5 upon oil quenching. Compression tests after air quenching revealed superior compressive strength of >3360 MPa compared to the as-sintered and oil cooled HSS. The fracture mode changed from ductile transgranular fracture for the as-sintered material to brittle intergranular upon oil cooling. As-sintered tensile specimens showed low strength of 681 + 43 MPa and brittle intergranular fracture as opposed to the as-sintered compression specimens. Finally, the manufacturing capability for an injection molding tool insert was proven, as no defects were identified. This novel process will allow for the manufacture of complex high-performance parts with improved durability and efficiency.
Adhesion wear during blanking of aluminium is a well-known and industrially relevant problem, as adhesion formation is influenced by process-inherent thermoelectric currents arising from the Seebeck effect. Previous studies have shown that externally applied countercurrents can reduce adhesion by suppressing these thermoelectric currents. However, their application has been limited to time constant countercurrents whose magnitudes had to be calibrated through numerous experimental trials. In this work, a universally integratable compensation system enabling time-resolved countercurrents is introduced. Different process-driven countercurrent strategies are systematically compared, allowing the most effective strategy to be isolated. Based on this strategy, a linear calculation formula for the required countercurrent magnitude is established. Throughout the study, non-lubricated blanking of AA5754 (2.5 mm and 4.0 mm) was investigated. The calculation formula is experimentally validated by varying material thickness, cutting speed, and punch material. The proposed approach achieves a reduction of the mean adhesion height on the punch by 28% to 67%, while reducing the experimental effort by more than 85%.
Mechanical engineering, as a leading industry, offers the potential to increase resource and energy efficiency for the targeted implementation of sustainable value chains. The application of laser-based processes, especially laser-based additive manufacturing, enables the production of thin-walled, complex, and functionally integrated structures with a high stiffness-to-weight ratio, making them ideal for lightweight applications. However, a disadvantage of laser-based processes is the significantly higher energy demand compared to conventional methods. This suggests that manufacturing lightweight components while keeping the amount of additive manufacturing at a minimum should be environmentally favorable. The subject of the investigation is to test this hypothesis on an aluminum-based component from the automotive sector and its topology optimized counterpart. For that purpose, a hybrid manufacturing route—including laser cutting, laser welding, and laser-based additive manufacturing—is compared to two reference routes depicting conventional manufacturing and purely additive manufacturing by means of a life cycle assessment (LCA). The assessment is partly based on primary data from experiments, covers production and use phase and also considers future energy conditions through the incorporation of integrated assessment models in form of prospective LCA. The results show that minimizing the share of additive manufacturing within a topology-optimized component can reduce environmental impacts drastically. But compared to conventional manufacturing impact savings are still only achieved under future energy conditions.
Bobbin tool friction stir welding (BT-FSW) is a solid-state joining technique that produces high-strength welds without the need for backing plates. This study introduces a new variant, termed hybrid bobbin tool friction stir welding (HBT-FSW), which combines the advantages of conventional BT-FSW and semi-stationary bobbin tool friction stir welding (SSBT-FSW). The HBT-FSW configuration features a probe with a rotating section positioned at the same level as the upper stationary shoulder. This design enhances weld surface quality, while preserving the mechanical performance required for demanding aerospace applications. The process was applied to aluminium alloy AA2219, a common material used in space structures, such as pressurised modules and propellant tanks for orbital systems. A comprehensive microstructural and mechanical characterisation was performed to assess the effectiveness of HBT-FSW. Electron backscatter diffraction (EBSD), hardness mapping, and synchrotron wide-angle X-ray scattering (WAXS) were used to analyse the weld microstructure. Deformation and failure behaviour were investigated through tensile testing with digital image correlation (DIC) and fractographic analysis using scanning electron microscopy (SEM). The results show that HBT-FSW produced a microstructure similar to BT-FSW, achieving comparable tensile strength and ductility while maintaining the superior surface quality typical of SSBT-FSW and enabling welding speeds approximately 30% higher than SSBT-FSW. HBT-FSW therefore represents a promising advancement for the reliable and efficient joining of aluminium alloys in high-performance aerospace structures.
The joining of dissimilar metals offers considerable potential for weight reduction in aerospace structures, especially through the substitution of mechanical fasteners such as screws and rivets with spot-welded joints. Friction Melt Bonding (FMB) is a lap welding technique developed for joining dissimilar metals with large differences in melting temperature. However, achieving sound joints between titanium and aluminum alloys remains challenging due to intermetallic compound formation and uneven material flow. In this study, a modified spot FMB process was proposed to join titanium and aluminum alloys. Particular attention was given to the introduction of a counter-pressure step, aimed at improving the quality of Ti-Al weld joints. Microscopic characterization and mechanical testing were performed on welds produced with and without counter-pressure to assess its role in the joining process. The formation of nano scale Ti-Al intermetallic compounds at the Ti-Al interface assists the joint formation. Fracture surface analysis further confirmed the beneficial effect of counter-pressure in promoting stronger interfacial bonding. These results highlight the critical role of counter-pressure in facilitating interfacial diffusion and consequently, in producing high quality Ti-Al joints through the spot FMB process.
The work addresses the problem of filler wire deviation from the tool center point in Wire Arc Additive Manufacturing based on Gas Metal Arc Welding. The primary causes identified for this deviation are inherent wire curvature and the progressive wear of the welding torch's contact tip, leading to an offset between the actual material deposition location and the programmed trajectory. This study analyzes and identifies a series of parameters that most significantly influence the magnitude of the filler wire deviation. It was determined that the wire-tip contact interaction is characterized by the wire's stressstrain state, which governs the dynamic changes in contact force throughout the tip wear process. To establish the relationship between the deviation and the key parameters of this contact interaction, effective flexural modulus, microhardness, diameter, and radius of curvature were determined for Inconel 718, Inconel 625, and low-carbon steel G3Si1 welding wires. Based on measured properties, a simulation of the wire-tip contact interaction was developed to determine contact forces, subsequently validated by laboratory experiments. Using the experimental data set and applying artificial intelligence methods, a system of three multi-layer perceptron neural network models was developed. The use of neural networks enabled the description of the non-linear relationship between the selected parameters and allowed for the prediction of wire deviation with an experimentally measured mean absolute error of 0.088 mm. The resulting models generalize and comprehensively describe the dependency of the wire deviation magnitude relative to the tool center point, utilizing a minimally necessary set of input parameters.
The rapid growth of the fast fashion industry has necessitated business models prioritizing agile responsiveness to shifting market trends. This demand drives the need for accelerated garment design, production, and distribution cycles. To this end, the Hanging Transportation (HT) system has emerged as a critical component of infrastructure in labor-intensive apparel manufacturing, facilitating efficient product flow and allocation processing. Crucially, scheduling within HT systems presents a distinct challenge compared to traditional flow shop scheduling: the optimal allocation of workers, rather than machine assignment, determines production efficiency. To address the Flow Shop Worker Assignment Problem (FSWAP) in HT systems, this paper proposes a novel aDaptive wOrker aSsignment (DOS) approach. DOS explicitly incorporates workers’ proficiency heterogeneity and dynamic availability while optimizing for completion time efficiency and solution stability. Furthermore, we introduce a Local Optimum Breaking Strategy (LOBS) integrated within DOS to mitigate the local optimum issue commonly associated with evolutionary algorithms. A comprehensive evaluation through theoretical analysis, simulations, and real-world case studies demonstrates that DOS significantly outperforms existing popular methods in terms of completion time, computational efficiency, and solution stability.
Precise miniature tooling is critical for the production of micro-electromechanical systems, microfluidics and micro-optics. Post-treating mould tools is critical for ensuring defect-free production in micro-injection moulding applications. In this study, large-scale electropolishing (EP) was implemented successfully to shape and polish 4-inch scale Ni mould tools, introducing a positive draft angle and increasing the fillet radius of the micro features. These geometric modifications reduced demoulding defects and facilitated smoother demoulding of polymer parts from Ni moulds. The upscaling of EP was investigated systematically, focusing on its impact on reducing friction and demoulding forces, using both polished and unpolished moulds. Polished moulds achieved a 9.22±2.56% reduction in demoulding force and a decrease in the coefficient of friction from 0.3 to 0.2 compared to unpolished moulds. A mould tool life test, conducted over 1000 cycles, demonstrated the superior durability of electropolished moulds with high-quality cyclic olefin copolymer (COC) chips being produced. Furthermore, a microfluidic mould for a high-performance drug delivery system was fabricated via UV-lithography and electroforming. This mould was used in both polished and unpolished forms to produce microfluidic chips, which were tested subsequently for lipid nanoparticle synthesis and transfection efficiency. The polished moulds resulted in improved transfection efficiency and throughput. Overall, this study demonstrates that electropolished moulds can manufacture superior quality polymer components by reducing friction, demoulding forces and defects while also improving tool life. These findings provide valuable insights into the scalability of EP for enhancing mould performance and throughput in the fabrication of microfluidic devices.
Path planning is a critical challenge in additive manufacturing, as it directly affects part quality and process reliability. Conventional algorithms often struggle with complex geometries, leading to voids, overlaps, and discontinuities that compromise structural integrity. This paper presents an adaptive path-planning framework that combines convex decomposition with a partial-contour-guided hatching (PCGH) strategy to generate continuous, space-filling, and geometry-conforming toolpaths. The proposed approach segments complex interior regions into fillable sub-polygons using a collision-aware concavity analysis and tree-search procedure, while PCGH constructs efficient hatch paths by selectively leveraging boundary segments as guidance. This integration ensures geometric conformity, minimizes defects such as overlaps and voids, and maintains path continuity across intricate features. The framework is demonstrated and validated in wire arc additive manufacturing (WAAM), which is a directed energy deposition (DED) process where continuous deposition, collision avoidance, and stable layer formation are essential. Tests on diverse geometries, from simple polygons to curved and multi-featured structures, confirm state-of-the-art performance in both simulations and physical experiments. By addressing a long-standing limitation in path planning, this work enables more reliable and defect-resistant additive manufacturing of complex parts.
Friction extrusion is an emerging solid-state manufacturing technique for producing rods and tubes, but ensuring uniform microstructural properties along the extrudate length remains a major challenge. This work introduces, for the first time, a feedback-controlled friction extrusion framework in which high-fidelity smoothed particle hydrodynamics (SPH) simulations are coupled with a proportional–integral–derivative (PID) controller to regulate the force-controlled process numerically. Unlike existing numerical approaches that rely on experimentally prescribed die displacement or velocity profiles, the proposed PID–SPH framework allows die displacement to evolve naturally in response to material resistance while maintaining a prescribed extrusion force. The framework is implemented in a GPU-accelerated SPH solver, enabling fast and efficient simulation of the nonlinear thermomechanical behavior inherent to friction extrusion. The PID-SPH framework is validated against experiments on the extrusion of 14 mm rods from high-strength AA7075-T6 billets using a flat die, with comparisons in extrusion force, die plunging displacement, and temperature evolution. Thirteen thermocouples positioned at different radii and depths provided detailed thermal data at the die–billet interface and within the billet during the process. The results demonstrate that the PID–SPH framework accurately captures the force-controlled thermomechanical response of friction extrusion. Beyond predictive accuracy, the framework provides insight into the origin of microstructural nonuniformity along the extrudate length by resolving the coupled evolution of strain rate, material flow, and thermal fields under applied force. These insights support the identification of key factors governing grain size variation and the evaluation of mitigation strategies, such as controlled thermal management, to promote more uniform microstructural development.
High-speed blanking (HSB) is an advanced cutting process particularly suitable for high-strength steels. Accurate and physically realistic simulation of this process can help to obtain basic understanding of the process mechanisms, but it is challenging due to the very high strain rates, the required thermo-mechanical coupling, and the necessity of representing the elastic characteristics and dynamic behaviour of the tools in the numerical model. Model validation is an added challenge due to the difficulty in accessing the key parameters from the highly dynamic process. In this study, we demonstrate the key aspects of the deforming material and the tool components to be accounted for in the numerical model, as well as the measurement of relevant process variables that can be used to build and validate a realistic process simulation. The validated simulations support interpretation of the experimental results with respect to measured cutting-force curves for two different tools. To describe the deformation behaviour of 22MnB5 under high-speed blanking conditions, the constitutive parameters of the Modified Johnson–Cook model are determined by inverse optimization in LS-OPT for strain rates of up to 3500 s−1. Tensile tests and Split Hopkinson Pressure Bar tests provided the experimental reference for model calibration. For the subsequent HSB process simulation, a three-dimensional LS-DYNA model was built, considering all tool components relevant to the load transmission path. All of these components were modelled as elastic bodies to account for the actual dynamic compliance of the system, which is essential for reproducing the measured time-dependent force. Good agreement between simulated and measured force–time histories demonstrates that accounting for elastic tool deformation in the 3D model is essential for realistic force prediction.
Charge welds in ‘continuous’ billet-to-billet extrusion processes are commonly associated with substandard material properties, leading to conservative industrial scrap practices. Despite the prevalence of this problem, limited understanding exists regarding governing mechanisms in terms of microstructural and mechanical evolution across the charge-weld zone under real-world processing conditions. This study presents a comprehensive multiscale investigation of charge-weld integrity in hollow AA6082 profiles using a combined experimental and numerical approach. A large number of full-scale industrial extrusion trials were carefully conducted at varying ram speeds, followed by systematic microstructural and mechanical characterizations of the weld zone. The experimental findings reveal a gradual increase in charge-weld strength and ductility along the extruded profile. This is primarily attributed to a reduction in the density and size of oxide-induced grooves and pits at the weld interface between two succeeding billets dispersed into the extruded profile. To understand the governing mechanisms at continuum scale, a finite element model was developed using QForm-Extrusion software. The Kolpak's semi-empirical film theory-based model was incorporated to investigate how thermo-mechanical history and ‘local’ conditions influence charge-weld integrity. The numerical model effectively captured important trends of charge-weld integrity, identifying regions where weld properties progressively recovered to the levels of parent material. Our findings show that a substantial portion of the charge-weld zone provides adequate material integrity, which may call for less conservative scrap standards in industry. Overall, this work advances the fundamental understanding of charge-weld formation, offering a pathway to optimizing material yield towards more efficient manufacturing of aluminum products.
The increasing use of ultra-high-strength steels (UHSS) in automotive safety components is driven by stricter crash safety requirements, vehicle weight reduction, and ecological goals in production and service. The application of UHSS requires adaptations in the manufacturing process chain, as conventional slow-speed blanking (SSB) used in mass production is challenging due to tool wear. Another aspect is crashworthiness: The interaction between material properties and blanking-induced defects-such as surface irregularities, microvoids, and microcracks-promotes crack initiation at free edges and limits edge formability. Local plastic deformation without breakage is a precondition for a stable break load of safety components, wherefore edge stretchability serves as an indicator for crashworthiness. High-speed blanking (HSB) of three steels with ultimate tensile strengths in the range of 1500 MPa-martensitic Docol 1500M, press-hardened (PH) 22MnB5, and carbon steel C60-is examined. Blanking trials are followed by central-hole tensile tests (CHTT) to assess edge stretchability. HSB produces edges with high geometric accuracy and homogeneous fracture surfaces, exhibiting roughness values comparable to wire-eroded surfaces. The shear-affected zone is confined to a narrow band of less than 2% of the sheet thickness, which is four times smaller than those observed in SSB. CHTT results show that HSB edges retain the same load-bearing capacity and edge fracture strain as wire-eroded edges, showing that edge integrity has not been compromised by HSB. In contrast, SSB triggers premature crack initiation reducing the achievable fracture strain by nearly half.
Characterisation of the strain hardening behaviour for metal forming simulations at elevated temperatures still poses a challenge for state of the art characterisation strategies, where often microstructural changes shall be investigated simultaneously. To do so, GLEEBLE thermo-mechanical simulators are commonly used to conduct physical testing of materials at thermal and mechanical loading at a wide range of loading rates. Such testing systems introduce a known challenge of non-uniform temperature into the specimen which leads to inhomogeneous strain distributions. This heterogeneous strain distribution consequently leads to an uncontrolled forming area together with an undesirable increase of strain rate. To address that challenge, this contribution presents and applied a novel approach to conduct locally controlled tensile tests by utilising a 3D-DIC system for a closed loop strain rate control. The titanium alloy Ti6Al4V with a thickness of 1.5 mm was investigated at temperatures between 600 degrees C and 900 degrees C and strain rates between 0.01 s- 1 and 0.1 s-1. The novel testing method leads to a change in the analysed flow stress at 600 degrees C of up to 80 MPa and at 900 degrees C of up to 65 MPa.
Optimising injection dynamics in micro-injection moulding (μIM) enhances efficiency, reduces defects, and improves repeatability. The current study examines the injection dynamics of μIM using both rapid-tooling, fabricated via material jetting, and conventional aluminium metal tooling. A 20 mg micro-moulding cavity was used to assess injection behaviour, injection pressure profiles, and process variation through in-line process monitoring and computational modelling. Results reveal significant differences between rapid and metal tooling in terms of drag, pressure build-up during injection and mechanical properties of the final products. The low thermal conductivity of rapid-tooling has led to prolonged low melt viscosity retention, resulting in significantly reduced peak injection pressures and dampened pressure overshoots, improving process repeatability. Metal tooling in contrast showed increased pressure fluctuations, making the injection dynamics more complex and affecting process repeatability. Computational modelling captured the major trends and exhibited deviations in pressure profiles, particularly for rapid-tooling, where accurate heat transfer coefficient estimation remains a challenge. Mechanical property correlations with injection dynamics further highlight the data-rich nature of μIM, with shear stress effects at higher injection rates influencing part performance. This study provides new insights into μIM process dynamics, emphasising the role of thermal properties and the challenges in modelling heat transfer effects in rapid-tooling. The findings support the optimisation of μIM for improved process control, predictive modelling, and data-driven quality monitoring for both industrial and rapid prototyping settings.
Joining-by-hydroforming is a process in which components are joined through expansion under internal pressure. Depending on the required fluid pressure and application rate, this process can be technically demanding and challenging to implement on industrial equipment. To address this, a simplified experimental setup was developed to investigate the fundamental joining mechanisms. In this setup, aluminum 6061-T6 (AA6061-T6) and poly(ether ether ketone) (PEEK) rings are force-fitted using a conical punch and segmented conical expansion elements, enabling controlled radial expansion. The resulting assemblies are subsequently separated in a dedicated push-out test. Experimental results show that the required separation force increases with rising elastic strain in the polymer, attributable to an increase in contact pressure according to Coulomb’s friction law. This effect diminishes once plastic deformation of the thermoplastic initiates. Furthermore, stress relaxation in PEEK causes a time-dependent decrease in joint strength, reaching a quasi-equilibrium after approximately 104 s, as confirmed by relaxation experiments on PEEK coupons. To analyze thermal effects, the entire ring assembly is preheated to defined temperatures in a laboratory furnace. An inverse correlation between joining temperature and joint strength is observed, consistent with the trend identified in the dynamic-mechanical-thermal analysis (DMTA) of PEEK.The proposed experimental method enables rapid identification of the most influential parameters for joining-by-hydroforming, without requiring dedicated hydroforming equipment or production machine time.
Additive manufacturing (AM) has advanced rapidly, expanding its applications across various fields. A key challenge in AM is fabricating fine, high-precision parts, which requires uniform and densely packed powder layers. While fine particles (<20 μm) hold promise for these components, their poor flowability and compactability present significant obstacles. This paper presents an experimental and numerical study on overcoming these limitations through high artificial gravity. Fine Inconel 625 particles immersed in epoxy adhesive were compacted using a lab-scale centrifuge at 1010G, 2030G, and 2810G, and a customized large-scale centrifuge at 71.7G, 101.6G, and 123G. The packing fraction of the green body increased up to 0.52 in the lab-scale centrifuge and up to 0.35 in the customized centrifuge. A validated Discrete Element Method (DEM) model simulated compaction of a fine metal powder bed without epoxy adhesive at various gravitational levels, confirming an 82.8% improvement in packing fraction, reaching 0.53. Cross-sectional analysis of materials produced by laser melting of fine-particle powder beds without epoxy adhesive revealed substantial voids in samples fabricated under normal gravity. In contrast, samples produced under high artificial gravity (71.7G) exhibited significantly reduced void formation.
Accurate characterization of the steady-state deformation resistance of materials is a prerequisite for high-precision forming simulations. However, controlling the temperature and strain rate in high-temperature tensile experiments is difficult and can hinder data acquisition under ideal isothermal and constant strain-rate conditions. Although the inverse finite element method (iFEM) has been conventionally employed for this purpose, it suffers from limitations such as dependence on constitutive equations, non-uniqueness of solutions, and requirement for expert-level implementation. This study introduces a data-driven framework for extracting steady-state deformation resistance from transient experimental data without using iFEM. The proposed framework acquires transient experimental data, performs regression-based interpolation and optimal model selection, and extracts steady-state responses using regression techniques, which includes a pre-defined material model, artificial neural network, and Gaussian process regression. Two case studies with aluminum and magnesium alloys, each under distinct variable conditions, are conducted to assess validity and scalability. ANN and GPR enhance the prediction accuracy of interpolated values compared to that of the pre-defined material model. This finding validates the feasibility of accurately estimating steady-state deformation resistance through flexible model selection. The plausibility of the predicted responses is supported by visualizing the corresponding response surfaces. The proposed framework provides a flexible, scalable, and practical alternative to conventional iFEM-based approaches with promising future applicability to higher-dimensional scenarios.
