Search Results (2,885)

Scandium (Sc) is well recognized as a potent grain refiner, yet optimizing its addition amount in the Al-Cu-Mg-Ni-Fe (A242) system remains a longstanding challenge, critically important for material performance in high-temperature automotive and aerospace applications. The present work, therefore, presents a study of low-Sc modified A242 alloys, demonstrating that 0.2 wt.% Sc microalloying of the system has a pronounced effect on its solidification-driven microstructural evolution, improving the high-temperature formability of the alloy over a 20–200 °C temperature range. The study demonstrates that this addition triggers a dramatic columnar-to-equiaxed grain transition, reducing the average grain size by 90.8% (from 400 ± 100 μm to 37 ± 10 μm) and fragmenting the brittle, continuous intermetallic network into a highly uniform architecture. Uniaxial compression testing revealed that, while the as-cast solid-solution alloy slightly reduces room-temperature strength due to solute trapping, it delivers an exceptional 142% increase in strain-to-failure at 200 °C (exceeding 0.8 mm) compared to the base alloy. This significant enhancement in ductility is driven by thermally stable Al₃Sc dispersoids that exert Zener pinning pressure, halting thermal grain coarsening and activating superplastic deformation mechanisms. These findings support the development of advanced thermoforming applications, with the finite element (FE) model predicting process improvements that enhance manufacturing efficiency. This work presents a validation and simulation-ready material framework that substantiates the viability of low-Sc-modified A242 alloys for such operations. Full article

Heavy-duty industrial robots (HIRs) are essential for high-payload operations in the automotive, aerospace, and nuclear industries. However, existing reviews are often limited to specific domains or control methods. This paper provides a concise review of recent advances in HIRs from two perspectives: structural innovation and intelligent control. The review shows that structural design is evolving toward lightweight, robust, and maintainable architectures, while control strategies are increasingly shifting from conventional PID methods to adaptive, robust, and learning-based approaches to handle high inertia, nonlinear dynamics, and uncertainty. Representative applications, including friction stir welding and nuclear operations, are also summarized. Based on the reviewed literature, we identify several key challenges for future research, including structure–control co-design, energy-aware motion planning, robust autonomy in hazardous environments, safe human–robot collaboration, digital-twin-enabled lifecycle optimization, and interpretable fault diagnosis. These findings outline the research agenda for the next generation of HIRs. Full article

Federated data spaces, established through initiatives such as IDSA and GAIA-X, enable organizations to share and monetize datasets under contractual terms. However, enforcing these contracts—particularly detecting unauthorized reuse or modification of datasets—remains an open challenge. We present the Off-Platform Contract Inspector, a component of the PISTIS framework, that implements a modular similarity-detection pipeline combining path-value Jaccard similarity, field-aware type-specific comparisons, and sentence-embedding-based semantic analysis across structured, semi-structured, and unstructured datasets. This contributes as follows: (i) an Inverse Document Frequency (IDF)-weighted structural similarity mechanism that discounts common domain vocabulary via Inverse Document Frequency weighting over the data space catalog, combined with a schema-evidence-gated fusion that reduces false positives from domain vocabulary overlap; (ii) an adaptive threshold optimization mechanism that learns modality-specific fusion weights and decision thresholds via cross-validated grid search; and (iii) a privacy-preserving similarity layer based on MinHash Locality-Sensitive Hashing signatures, Bloom filters with OR folding alignment, and Laplace noise for differential privacy, enabling cross-organizational dataset comparison without exposing raw data. Further, we contribute a threat taxonomy of seven dataset modification types ordered by detection difficulty, and evaluate the system on dataset pairs derived from real-world datasets across three smart-city application domains (Mobility, Energy, Automotive), with controlled augmentations applied to model adversarial behaviors. The IDF-weighted pipeline achieves high precision on intra-domain hard negatives—pairs of different tables from the same data space that share domain vocabulary—where text-similarity baselines produce false positives. The adaptive scheme learns per-modality fusion weights via cross-validated grid search. The privacy-preserving mode operates without accessing raw data and runs noticeably faster than the full pipeline, enabling screening while preserving data confidentiality. Full article

This paper presents a compact modified monopole antenna tailored for 5G NR on-glass automotive applications operating in the n78 band. The design overcomes 3D radiation pattern limitations inherent in conventional monopole and inverted-F antennas (IFAs). Unlike traditional structures where auxiliary branches serve impedance matching or grounding, this design integrates open- and short-circuited stubs with a coplanar waveguide (CPW) feed to eliminate discrete components. By utilizing a resonant mechanism distinct from IFAs, it enables precise control over the current distribution and phase on the radiator to achieve passive 3D beam shaping without active switches or arrays. This suppresses the inherent elevation null, enhancing upper-hemisphere radiation. A prototype operating from 3.3 to 3.6 GHz was fabricated on a flexible printed circuit (FPC) and verified on a glass substrate. This study focuses strictly on radiation characteristics at the antenna element level; to ensure a focused investigation on dielectric-antenna interactions, large-scale vehicle body scattering and full-scale vehicle integration are excluded from this scope. The results, including S-parameters, gain, total efficiency, and 3D patterns, demonstrate superior elevation coverage and comparable impedance performance under on-glass boundary conditions. The proposed methodology offers a high-feasibility, low-complexity, and cost-effective solution for passive 3D radiation control in on-glass 5G wireless links. Full article

The South African aluminium industry faces technical challenges related to cladded ingots used in automotive heat exchangers, creating a need for offline processing methods that can replicate rolling processes like roll bonding, as large-scale industrial trials are costly and difficult to control. To address this, Mintek established a comprehensive offline manufacturing facility for process and product development of rolled metal products, focusing on the thermomechanical processing of aluminium alloys. In this study, stacked AA4045/AA3003mod coupons were processed under controlled conditions by varying thickness reduction, temperature, and reheating, aiming to investigate the effect of isothermal soaking time on microstructure and mechanical properties. Tensile tests were performed on clad sheets before and after brazing heat treatment, and fracture surfaces were examined via scanning electron microscopy. Samples heated at 505 °C for ≥38 h, followed by cold rolling and annealing, fell at the lower end of the 9031-H24 specification for yield strength, which is important for this application (i.e., the minimum tensile yield strength of 145 MPa and the ultimate tensile strength (UTS) range of 190 to 230 MPa). Fracture surface analysis revealed a dimple-dominated structure in cold-rolled and annealed samples, indicating ductile fracture. The study concludes that the offline roll-bonding method successfully replicates industrial cladding processes, and that isothermal soaking duration significantly influences mechanical performance, though careful control of thermal exposure is necessary to meet the specified mechanical properties. Full article

Grey cast iron brake discs remain standard in automotive braking systems due to their favourable thermal conductivity and mechanical strength. However, increasingly stringent environmental regulations, including Euro 7, necessitate enhanced surface durability to reduce particulate emissions and mitigate corrosion-related degradation. In this context, laser metal deposition (LMD) offers a promising route to engineer wear-resistant coating systems with tailored microstructures. This study investigates phase formation and microstructural evolution in a 316L/430L-WC multilayer coating deposited on grey cast iron (GJL) brake discs and subjected to brake-shock testing to replicate thermomechanical load cycles representative of real braking conditions. X-ray diffraction (XRD) performed on the interlayer region between the 316L and 430L-WC layers revealed clear evidence of σ-phase formation, indicating intermetallic transformations facilitated by thermal cycling. Microstructural characterization using scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) identified localized enrichment of Cr- and Fe-rich regions that support the XRD-based interpretation of σ-phase development. These results provide insights into phase transformations and elemental diffusion in LMD-fabricated brake-disc coatings. The findings advance the understanding of thermally induced transformations in multilayer steel systems and support the optimization of LMD coatings for high-temperature and wear-intensive applications through advanced analytical evaluation. Full article

This study investigates the thermo-mechanical performance of fused filament fabrication (FFF)-printed polyphthalamide reinforced with 15 wt.% short carbon fibers (PPA CF15) for engineering applications under elevated temperature and cyclic loading conditions. The material was characterized by quasi-static tensile testing, fatigue testing, dynamic mechanical analysis (DMA), scanning electron microscopy (SEM), and finite element analysis (FEA). Tensile tests performed from 20 to 180 °C revealed a strong temperature-dependent reduction in mechanical properties: the elastic modulus decreased from 2.437 to 0.401 GPa, while the ultimate tensile strength decreased from 64.537 to 9.190 MPa. In contrast, elongation at break generally increased with temperature, indicating a transition toward more ductile deformation governed by thermal softening of the polymer matrix. Fatigue tests showed reduced fatigue resistance at higher temperatures and stress levels; however, stable cyclic performance was achieved when the applied stress remained below approximately 60–70% of the ultimate tensile strength, with several specimens reaching 10⁶ cycles. DMA confirmed the viscoelastic nature of PPA CF15 and enabled the construction of frequency–temperature superposition master curves for numerical modelling. SEM observations revealed increased matrix deformation and fiber pull-out at elevated temperatures. FEA of an automotive intake manifold (IM) case study demonstrated that experimentally derived material data can be used to predict deformation, stress redistribution, and viscoelastic stabilization under combined thermal and mechanical loading. The results indicate that FFF-printed PPA CF15 is a promising lightweight composite for thermally and mechanically demanding automotive applications. Full article

Joining dissimilar polymers such as polypropylene (PP) and high-density polyethylene (HDPE) remains a challenge in modern manufacturing due to their incompatible thermal properties and poor interfacial bonding. In this study, a novel hybrid structure was fabricated by laser welding of PP to an HDPE matrix reinforced with 3 wt% carbon nanotubes (CNTs). The CNTs were incorporated via fused filament fabrication (FFF) 3D printing to raise the melting temperature and thermal stability of HDPE, thereby minimizing the thermal mismatch with PP. A pulsed CO₂ laser was used to perform butt welding, and the influences of pulse frequency, welding speed, and laser power on the elastic modulus and tensile properties of the weld samples were thoroughly studied. A response surface design was employed to build predictive models and perform multi-objective optimization. The addition of CNTs, as evidenced by differential scanning calorimetry (DSC), elevated the crystallinity level of HDPE from 48.3% to 53.1% and the melting point from 137.8 to 140.8 °C, making its thermal properties more comparable to those of PP. Observations via scanning electron microscopy (SEM) indicated that when the optimal parameters were applied (pulse frequency: 35 Hz, welding speed: 21 mm/s, and laser power: 49 W), the joint line was defect-free, fully fused, and contained very few voids. At these settings, the model estimated an elastic modulus of 793 MPa and a tensile strength of 49.6 MPa, while confirmation experiments yielded 47.2 MPa and 764.5 MPa, respectively, with relative errors below 5%. The results demonstrate that the combination of CNT-assisted laser welding and RSM-driven optimization effectively resolves the thermal incompatibility of HDPE and PP, thereby facilitating high-quality joining of dissimilar polymers for applications in packaging and automotive fields. Full article

Lightweight thin-walled energy-absorbing structures play a critical role in passive safety systems for automotive and aerospace engineering applications, yet simultaneously achieving high specific energy absorption and stable crushing behavior remains a persistent challenge. Inspired by the topology of natural honeycombs, this study proposes a novel modular assembled multi-cell carbon fiber reinforced polymer (CFRP) structure (MAMCS), fabricated via a cost-effective modular assembly strategy based on a wrapping process. Quasi-static axial crushing experiments combined with validated finite element simulations were employed to systematically investigate the effects of inner layup configurations ([0°/90°], [30°/−60°], [45°/−45°]), cell number, and inner sub-cell size on crushing behavior. Among the investigated layup configurations, the [0°/90°] inner layup exhibited superior mean crushing force (MCF) and specific energy absorption (SEA). Multi-cell architectures significantly enhanced load-bearing capacity and crushing stability through mechanical interactions among internal sub-cells. Parametric analyses further revealed that enlarging the inner sub-cell size elevates both MCF and SEA, although at the expense of a higher peak crushing force (PCF). A TOPSIS-based multi-criteria decision-making framework was applied to identify a preferred configuration that achieves a favorable balance between peak load mitigation and energy absorption efficiency. The proposed MAMCS, characterized by its simple modular assembly, cost-effective fabrication, and superior crashworthiness performance, offers a promising bio-inspired design strategy for developing high-performance lightweight energy-absorbing structures in axial impact applications. Full article

Aluminum alloy A356 (Al-7Si-0.3Mg) is widely employed in automotive structural components due to its favorable strength-to-weight ratio, yet its mechanical performance is highly sensitive to T6 heat-treatment processes. Conventional heat-treatment schedules are typically based on uniform, empirically derived parameters and fail to consider variations in component size, geometry, or thermal mass. Consequently, applying a single schedule across all component sizes often leads to inconsistent microstructural development, energy inefficiency, and elevated scrap rates. Smaller components tend to be over-processed, while larger components may be under-processed, both resulting in suboptimal mechanical properties and increased production costs. To overcome these limitations, this study presents a scalable heat-treatment optimization framework that integrates physics-based thermal simulations with machine learning techniques. The framework combines a transient thermal simulator with Long Short-Term Memory (LSTM) networks to predict sample temperature evolution, Random Forest regressors to estimate mechanical properties such as yield strength, hardness, and modulus of toughness, and Bayesian optimization to generate size-dependent, property-compliant heat-treatment schedules. Unlike traditional methods, this approach dynamically adjusts furnace parameters to individual component characteristics, optimizing both processing time and energy consumption while minimizing scrap. Application of the framework to components ranging from 0.5 to 10 kg demonstrates internally consistent simulation-based predictions of temperature profiles, phase-fraction evolution, and mechanical-property trends within the assumed modelling framework. Optimized schedules achieved 15–25% reductions in cycle time while maintaining properties within T6 specifications. These findings underscore the potential of AI-assisted heat-treatment optimization to enhance energy efficiency, reduce material waste, and improve the consistency of mechanical performance in automotive casting operations. Full article

This study investigates the role of symmetric probabilistic models in predicting financial distress in the automotive industry, with a focus on companies operating in the Slovak Republic. Financial distress prediction represents a binary classification problem characterized by an inherent symmetry between healthy and distressed firms. To capture this structure, two widely used symmetric models—logit and probit—are applied and systematically compared. The modeling framework incorporates LASSO regression for variable selection, enabling dimensionality reduction while preserving the most informative financial indicators. The empirical analysis is conducted on a dataset of 351 manufacturing enterprises. The results indicate that both models achieve comparable predictive performance, with the logit model reaching an accuracy of 78.9% and the probit model 77.8%. The area under the ROC curve further confirms the strong discriminatory power of both approaches. The findings highlight that the symmetric nature of the applied link functions contributes to model stability, interpretability, and balanced classification behavior. This study extends existing research by explicitly linking symmetry concepts with financial distress prediction in a sector-specific context. The proposed approach provides a transparent and practically applicable framework for early risk identification in industrial enterprises. Full article

With the increasing demands on energy efficiency and dynamic stability of modern combustion engines (e.g., TDI systems), conventional powder metallurgy materials are reaching their limits in terms of fatigue life and surface integrity. This scientific problem has led to the need to develop hybrid metal matrix (MMC) systems that use in situ hard phase formation. This study presents a comparative analysis of two real industrial components representing hybrid systems with a uniquely high content of titanium and vanadium (>1% by weight). The Ni-Mo-Ti system and the high-carbon C-Cu-Ti system were compared. The samples were processed by steam oxidation and plasma nitriding at 200 °C after sintering. The experimental methodology included chemical analysis on the Bruker Q2 ION 2 instrument, 10-point EDX analysis (Phenom), measurement of the apparent hardness of HV10 and dynamic ball-on-disc tribological tests at a load of 5.00 N supplemented by 3D profilometry. The results showed that the Ni-Mo-Ti system achieves higher hardness at functional edges (256 HV10) and three times higher resistance to deep penetration (11.46 μm vs. 34.67 μm) compared to the C-Cu-Ti system. Topographic analysis confirmed the positive role of porosity as a micro-reservoir for abrasion particles (negative Ssk). The study confirms that the nickel–molybdenum matrix ensures more efficient fixation of in situ generated TiC carbides, thus providing higher functional stability for automotive applications, which was verified by the non-destructive vibroacoustic diagnostics of Polytec PSV-500. Full article

This paper presents an FPGA-based hardware-in-the-loop (HIL) platform for real-time simulation testing of anti-lock braking system (ABS) electronic control units (ECUs). The proposed system integrates a Temporal Convolutional Network (TCN) model implemented on FPGA hardware to provide real-time predictions of wheel speed sensors under complex braking scenarios. The FPGA acceleration achieves low-latency processing with a total end-to-end latency of 10.61 µs per prediction cycle, corresponding to approximately 94.3 Ksamples/s, which is suitable for closed-loop automotive testing. Experimental results show that the TCN model provides accurate prediction based on mean squared errors below 0.001043 for key parameters such as wheel speed sensors and lateral acceleration. The modular architecture of the simulator allows extensibility to other automotive ECUs and provides a scalable solution for real-time system validation in safety-critical applications. Full article

Al₂O₃ single beads were deposited on a Ti-6Al-4V (Ti64) substrate by laser-directed energy deposition (DED-LB) to establish baseline process conditions for ceramic protective layers and future Ti64/Al₂O₃ functionally graded materials (FGMs). These ceramic-containing surface layers are applicable to titanium components requiring improved oxidation, wear, and thermal resistance in aerospace, automotive, and high-temperature structural applications. Laser power (300–700 W) and scan speed (300–700 mm/min) were varied, and bead geometry was quantified from cross-sectional observations; energy density and dilution ratio were calculated. Melt pool depth increased with higher power and lower speed, indicating increased heat input and substrate melting. Crack formation in the melt zone was more sensitive to laser power than to scan speed. In contrast, bead height showed a non-monotonic response to energy density, which may be associated with possible coupled effects such as recoil pressure-driven melt pool disturbance, powder scattering, and insufficient powder melting at high scan speeds. Dilution-based optimization identified 300 W laser power and 400 mm/min scan speed, with a powder feed rate of 3 g/min, as the most suitable condition within the investigated process window, giving the lowest practical dilution ratio of approximately 40.27%. SEM–EDS and XRD analyses were conducted to examine the interfacial microstructure and phase characteristics under the selected condition. Overall, this study provides fundamental process guidelines and mechanistic insight into bead formation, dilution behavior, and interface formation, supporting the future application of DED-LB-based ceramic protective or graded layers on Ti64 surfaces. Full article

This paper introduces the Normative Lean Performance Score (NLPS) model designed to evaluate lean operational performance using publicly available financial and accounting metrics, without requiring advanced analytics for practical implementation. The study applies an empirical research design based on a longitudinal dataset, where firms are first classified into lean-oriented groups, followed by logistic regression to identify significant indicators and Random Forest models to estimate their relative importance. The resulting index provides an objective, interpretable, and easily implementable performance measure suitable for cross-firm benchmarking and managerial decision support. Empirical testing using automotive manufacturers demonstrates strong alignment with lean classification and efficiency outcomes, providing evidence for the model’s relevance as an accounting-based benchmarking tool. In addition to its practical applicability, the framework contributes to lean performance measurement by translating machine learning insights into a reproducible index that can be applied in data-constrained environments. This approach ensures that the resulting index remains both empirically grounded and practically interpretable, while avoiding reliance on arbitrary or expert-assigned weighting schemes and qualitative assessment-based approaches. The model therefore offers a scalable and transparent alternative for practitioners, analysts, and researchers seeking robust lean performance evaluation when advanced modelling resources are unavailable. The study contributes a transparent, accounting-based normative index that reframes lean performance as a financial configuration rather than an operational maturity construct. The empirical analysis uses quarterly financial data from 17 publicly listed automotive manufacturers over the period 1994Q1–2024Q4. Full article