Abstract: The accuracy of dynamics parameters in the transmission system is essential for high-performance motion trajectory planning and stable operation of heavy-duty ser-vo presses. To mitigate the performance degradation and potential overload risks caused by deviations between theoretical and actual parameters, this paper proposes a dynamics model accuracy enhancement method that integrates multi-objective global sensitivity analysis and ant colony optimization-based calibration. First, a nonlinear dynamics model of the eight-bar mechanism was constructed based on Lagrange's equations, which systematically incorporates generalized external force models con-sistent with actual production, including gravity, friction, balance force, and stamping process load. Subsequently, six key sensitive parameters were identified from 28 sys-tem parameters using Sobol global sensitivity analysis, with response functions defined for torque prediction accuracy, transient overload risk, thermal load, and work done. Based on the sensitivity results, a parameter calibration model was formulated to minimize torque prediction error and transient overload risk, and solved by the ant colony algorithm. Experimental validation shows that, after calibration, the root mean square error between predicted and measured torque decreases significantly from 1366.9 N·m to 277.7 N·m (a reduction of 79.7%), the peak error drops by 72.7%, and the servo motor’s effective torque prediction error was reduced from 7.6% to 1.4%. In an automotive door panel stamping application on a 25,000 kN heavy-duty servo press, the production rate increases from 11.4 to 11.6 strokes per minute, demonstrating en-hanced performance without compromising operational safety. This study provides a theoretical foundation and an effective engineering solution for high-precision model-ing and performance optimization of heavy-duty servo presses.

Abstract: This study tests TiO₂ and SiO₂ nanolubricants in PAG oil using a Mini Traction Machine and an Ultra Shear Viscometer. The loads were 20 N and 40 N. The entrainment speeds ranged from 2.5 to 500 mm per second. The slide to roll ratio ranged from 25 to 150 percent. The nanoparticle concentrations were 0.01, 0.03, and 0.05 percent. The ball size was 19.05 mm, and the disk was 46 mm. All tests ran at 40°C. Only 0.05% of samples lowered traction compared with PAG at fixed SRR. TiO₂ at 0.05% showed the largest drop, up to 4.89 percent at 20 N and 2.99 percent at 40 N. However, lower concentrations increased traction. All nanolubricants reduced wear. TiO₂ at 0.03 percent gave the lowest wear, with a reduction of about 35 µm at 40 N. Nanolubricant samples stayed between 40.2 and 40.5°C while PAG reached about 41.0°C. TiO₂ produced slightly lower temperatures than SiO₂. Ultra shear tests from 40 to 100°C showed shear thinning. TiO₂ at 0.05% kept the highest viscosity at 40 and 60°C, up to 12 percent above PAG. SiO₂ showed smaller changes. TiO₂ delivered better friction, wear, temperature, and viscosity performance. Overall, both nanolubricants at 0.03% suits refrigerations applications while the 0.05% suits high load or high shear use.

Abstract: The design of aircraft components is a complex process that must simultaneously ac-count for environmental impact, manufacturability, cost and structural performance to meet modern regulatory requirements and sustainability objectives. When these factors are integrated from the early design stages, the approach transcends traditional eco-design and becomes a genuinely sustainability-oriented design methodology. This study proposes a sustainability-driven design framework for aircraft components and demonstrates its application to a fuselage panel consisting of a curved skin, four frames, seven stringers, and twenty-four clips. The design variables investigated in-clude the material selection, joining methods, and subcomponent thicknesses. The de-sign space is constructed through a combinatorial generation process coupled with compatibility and feasibility constraints. Sustainability criteria are evaluated using a combination of parametric Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) regression models, parametric Finite Element Analysis (FEA), and Random Forest surrogate modeling trained on a stratified set of simulation results. Two methodolog-ical pathways are introduced: 1. Cluster-based optimization, involving customized clustering followed by multi-criteria decision-making (MCDM) within each cluster. 2. Global optimization, performed across the full decision matrix using Pareto front analysis and MCDM techniques. A stability analysis of five objective-weighting methods and four normalization techniques is conducted to identify the most robust methodological configuration. The results—based on a full cradle-to-grave assessment that includes the use phase over a 30-year A319 aircraft operational lifetime—show that the thermoplastic CFRP panel joined by welding emerges as the most sustainable design alternative.

Abstract: Three different Gas Tungsten Arc Welding methods – DC single-electrode, DC double-electrode, and PC double-electrode – were analyzed using SS304 steel as the base material. Numerical models were developed to simulate the arc plasmas and calculate heat flux, current density, and wall shear stress on the surface of the workpiece. These data served as input for simulating the weld pools across all three configurations. Experimental validation showed a good agreement with the numerical results. In the double-electrode setup, electromagnetic interaction caused the arcs to deflect, which resulting an 8% reduction in the maximum heat flux and a 4% decrease in the maximum current density. Marangoni stress had a notable effect on the weld pool shape, creating a w-shaped stationary pool with the single-electrode setup, whereas the pool reached its greatest depth with the stationary double-electrode configuration. With the moving weld pool, the DC double-electrode created a pool that was 41% deeper and 25% wider compared to the DC single-electrode setup. The PC double-electrode created a pool that was 40% deeper and 22% wider than the DC single-electrode configuration. The findings of the research offer guidance for enhancing different arc settings and electrode arrangements to attain the intended welding quality and performance.

Abstract: The operational ability of the unit or a mechanism depends mainly on the quality of the working surfaces that are obtained mechanically. Many materials can be assigned to a group of hard-to-cut materials that includes titanium- and aluminum-based alloys, a new class of heat-resistant alloys, SiCp/Al composites, and other alloys. The difficulties in their machining are related not only to the high temperatures achieved on the contact pads under the mechanical load and the extreme cutting conditions, but also to the properties of those materials related to the adhesion of the chip to the faces of the tool that hampers chip flow. One of the possible solutions to reduce those effects and improve the operational life of the tool, and as a consequence the final quality of the working surface of the unit, is texturing the rake face of the tool with microgrooves or nanogrooves, microholes or nanoholes (pits, dimples), micronodes, cross-chevron textures, and other microtextures, the depth of which is in the range of 3.0–200.0 µm. The review is addressed to systematize the data obtained on micro- and nano-texturing of PCD tools for cutting hard-to-cut materials by different techniques (fiber laser graving, femto- and nano-second laser, electrical discharge machining, fused ion beam) additionally subjected to fluorination, dip- and drop-based coatings, and the effect created by the use of the textured PCD tool on the machined surface.

Abstract: This study proposes an analytical framework that converts operational records into structured research hypotheses, offering a systematic approach to understanding failure mechanisms. The framework combines descriptive statistics, time-series analysis, linear regression, and machine-learning techniques to identify patterns, irregularities, and residual model behavior. Within the scope of this research, the framework was implemented as executable Python code and tested on a representative downtime category observed in a real industrial machine. The application demonstrated how analytical outputs can be translated into inductive, deductive, and abductive hypotheses that support deeper exploration of failure dynamics. The findings indicate the need for a comprehensive, multifaceted analytical approach to interpreting and forecasting machine downtimes, emphasizing that combining quantitative insights with the development of reasoning-based hypotheses increases the explainability and methodological rigor of the results.

Abstract: The presented paper discusses and analyzes the methods for restoration of damaged stay rings and vanes of large Francis turbines. The focus is set on the crack damages that were also observed in stay vanes of Francis turbines in “Chaira” Pumped Hydro Energy Storage (PHES) plant, Hydro Unit 4 (HU4), where cracks occurred in all ten stay vanes. Additionally significant cavitation patches where noticed overall the front surface of the turbines stay vanes. Two approaches are examined in the study. The first approach for repairing adds material by welding over the damaged places, which are then polished. Although welding of damaged areas is a widespread method, some adverse effects are observed, mainly because of changes in the characteristics of the base metal and the impact of the high temperatures. The second approach is based on the removal of the damaged material till elimination of the cracks. Virtual models for computer simulation of both methods are prepared. These models are examined by structural mechanics and CFD analyses and compared. Final recommendations are proposed.

Abstract: Ultrasound applications are currently a very intense field of research, always in expansion, constantly finding new uses for them in all scientific fields. The basic process investigated in this research is that of obtaining wires by wire-drawing technology, which consists of successively passing a semi-finished wire through a sequence of dies until the desired final diameter is obtained. This plastic deformation process usually takes place at room temperature. The phenomena that can still be studied in this process and improved is that of reducing the friction between two parts that are in relative motion named also “ultrasonic lubrication”. Based on this phenomenon, technology for obtaining metal wires by wire drawing can be improved by increasing the wiredrawing speed. The article presents the design and calculation of the ultrasonic transducer used to activate the drawing die, the FEM to determine the optimal vibration frequencies and vibration modes used in activation of the die. To demonstrate the effect of reducing the coefficient of friction during wire drawing, a test stand was used in which the pulling force is achieved by a pneumatic system and the die is one of those used in a wire production factory. By ultrasonic activation of the die, an increase in the working speed was obtained. The percentage increase in the drawing speed is different, depending on the initial traction speed.

Abstract: Electric vehicle drivetrains reveal tonal gearbox noise once masked by combustion engines. Among the most persistent are ghost orders narrowband spectral components not aligned with nominal mesh harmonics linked to periodic gear tooth surface waviness. This review synthesizes research on the w aviness to transmission error to ghost order pathway focusing on detection modeling and validation methods relevant to EVs. Measurement techniques range from single flank transmission error tests and torsional order tracking to high speed full flank metrology with advanced waviness analysis enabling earlier identification of tonal risk. Modeling approaches include quasi static loaded tooth contact analysis with sinusoidal superposition multi body dynamics with micro geometry driven transmission error modulation and hybrid finite element multi body dynamics workflows that integrate measured topography. Findings show that circumferentially coherent waviness even at sub micron amplitudes can produce audible ghost tones in low damping EV drivetrains especially when coinciding with structural resonances. However predictive accuracy remains limited by inconsistent waviness terminology incomplete data transfer from metrology to simulation and scarce EV representative validation under varying loads and speeds. The emerging trend is toward integrated design manufacturing simulations where measured flank surface data directly informs noise prediction models. Standardized waviness metrics routine use of measured topographies in contact models and psychoacoustic or machine learning aided tonal assessment are identified as priorities for improving ghost order prediction and mitigation in EV gearbox design and production flow.

Abstract:

This work investigates a smart manufacturing approach to monitor gear noise, vibration, and harshness (NVH) in high-speed electric drivetrain gears. We focus on how micro-geometry errors introduced by the honing process can imprint waviness on gear teeth that causes persistent gear whine (including non-integer “ghost” noise orders). Compounding this challenge, vibrations can propagate through factory structures, making source identification difficult when multiple machines operate in proximity. We propose a cloud-based Industrial IoT architecture: a dense network of low-cost accelerometers synchronized via Precision Time Protocol (PTP IEEE 1588) collects vibration data across the plant. Each measurement is tagged via Data Matrix Code (DMC) and work-order integration to link it to the specific gear and process. Big Data infrastructure (time-series database, object storage) combined with real-time stream processing enables anomaly detection (using models like Isolation Forest and XGBoost) and root-cause analysis with explainable AI (SHAP values). A feasibility study outlines requirements (accuracy, latency, security) and compares design options (wired vs wireless sensors, PTP vs NTP sync, MQTT/OPC UA protocols, edge vs cloud processing). We present a 12-month pilot implementation plan and a conceptual system architecture. The solution aims to reduce scrap and rework, lower warranty risks, enable predictive maintenance, and support smart factory initiatives by providing early-warning NVH quality insights for each produced gear.

Abstract: This paper numerically and experimentally establishes a connection between shear deformation of the AISI 304 steel surface layer and the sliding velocity of a diamond indenter in multi-pass nanostructuring burnishing. Results of finite-element simulation of the process fully correspond to the experimental data obtained when changing the sliding velocity from 40 to 280 m/min after one and five tool passes. The experiment’s burnishing force was assumed to be 150 and 175 N, and feed was 0.025 mm/min. After surface machining, the maximum microhardness reached 400 HV0.05 at the depth of 30 µm from the surface after five indenter passes with the sliding velocity values of 40 and 200 m/min and burnishing force of 175 N.

Abstract: Deformations and damage frequently occur during the grasping of flexible and fragile objects, rendering traditional force-control and vision-based strategies inadequate for meeting robustness and safety requirements. This paper proposes an adaptive grasping method integrating tactile-visual feature alignment with semantic prior guidance.First, a large model extracts semantic constraints such as "material–deformation threshold–operation region" and aligns them with tactile encodings. Then, adaptive impedance control enables real-time force and pose adjustment. Experiments covering 60 categories of flexible/fragile objects and over 5,000 grasping tests demonstrate: compared to a diffusion policy without haptic feedback, this method achieves a +15.2% increase in grasping success rate, a 33.5% reduction in target damage rate, and an 18.4% decrease in 6D pose error. Ablation analysis indicates that tactile and semantic priors contribute +6.3% and +4.5% performance gains, respectively. These results demonstrate the method's effectiveness for real-time flexible grasping.

Abstract: The growing demand for high-strength and low-core-loss soft magnetic materials in high-efficiency energy conversion devices necessitates the development of novel alloys that combine excellent mechanical and soft magnetic properties. This work investigated the effect of Ta content on the microstructure and properties of as-cast (Fe₇Co₆Ni₆)93-xTaxAl7 (x=3, 5, 7) multiprincipal element alloys (MPEAs). The alloys featured an FCC matrix, in which Ta addition led to the precipitation of a Ta-rich Laves phase and significant grain refinement. The Ta5 alloy demonstrated an optimal balance of properties, with a yield strength approaching 1 GPa, an elongation of ~10%, a saturation magnetization of 92.88 emu/g, and a coercivity of 446.43 A/m, indicating good strength, ductility, and soft magnetic performance. An appropriate amount of Ta enhanced strength via precipitation and grain-boundary strengthening, while the saturation magnetization showed only a moderate reduction. The coercivity was effectively kept low by the fine, dispersed Co2Ta Laves phases, which minimized domain wall pinning.

Abstract: Metal additive manufacturing (AM) has emerged as a transformative route for producing lightweight, high-precision, and geometrically complex components in aerospace, biomedical, and microelectronic sectors. Among AM technologies, Laser Powder Bed Fusion (LPBF) offers exceptional design freedom; however, its widespread adoption particularly for titanium alloys remains constrained by two persistent challenges: shrinkage-induced dimensional deviation and porosity-related performance loss. In LPBF-processed Ti-6Al-4V, residual linear deviation typically falls within 0.1–0.8% when geometric compensation, preheating, and support strategies are implemented, while raw, uncompensated shrinkage is more commonly reported in the range of 1.2–2.0%, especially for thin-wall or thermally constrained geometries. Volumetric contraction (approximately 2–6%) may remain significant depending on part architecture and localized thermal accumulation. Concurrently, gas-induced and lack-of-fusion pores continue to undermine fatigue resistance and dimensional reliability. Research into process optimization, thermal management, and post-processing such as Hot Isostatic Pressing (HIP), vacuum sintering, and stress-relief annealing has improved density and mechanical integrity, while recent developments in AI-assisted monitoring, physics-informed models, and digital-twin frameworks are redefining defect prediction and control. Drawing on more than 100 peer-reviewed studies, this review synthesizes mechanism-driven insights and outlines a forward-looking roadmap, demonstrating how hybrid processing, real-time sensing, and data-centric control collectively advance the pathway toward defect-minimized, industrial-scale manufacturing of titanium components.

Abstract: The complexity of modern cyber-physical systems is steadily increasing as their functional scope expands and as regulations become more demanding. To cope with this complexity, organizations are adopting methodologies such as Model-based Systems Engineering (MBSE). By creating system models MBSE promises significant advantages such as improved traceability, consistency, and collaboration. On the other hand, the adoption of MBSE faces challenges in both the introduction and the operational use. In the introduction phase, challenges include high initial effort and steep learning curves. In the operational use phase, challenges arise from the difficulty of retrieving and reusing information stored in system models. Research on the support of MBSE through Artificial Intelligence (AI), especially Generative AI, has so far focused mainly on easing the introduction phase, for example by using Large Language Models (LLM) to assist in creating system models. However, Generative AI could also support the operational use phase by helping stakeholders access the information embedded in existing system models. This study introduces an LLM-based multi-agent system that applies a Graph-Retrieval-Augmented-Generation (GraphRAG) strategy to access and utilize information stored in MBSE system models. The system’s capabilities are demonstrated through a chatbot that answers questions about the underlying system model. This solution reduces the complexity and effort involved in retrieving system model information and improves accessibility for stakeholders who lack advanced knowledge in MBSE methodologies. The chatbot was evaluated using the architecture of a battery electric vehicle as a reference model and a set of 100 curated questions and answers. When tested across four large language models, the best-performing model achieved an accuracy of 93 percent in providing correct answers.

Abstract: Self-driving laboratories are redefining autonomous experimentation by integrating robotic manipulation, computer vision, and intelligent planning to accelerate scientific discovery. This work presents a vision-guided motion planning framework for robotic manipulators operating in dynamic laboratory environments, with a focus on evaluating motion smoothness and control stability. The framework enables autonomous detection, tracking, and interaction with textured objects through a hybrid scheme that couples advanced motion planning algorithms with real-time visual feedback. Kinematic modeling of the manipulator is carried out using the screw theory formulations, which provides a rigorous foundation for deriving forward kinematics and the space Jacobian. These formulations are further employed to compute inverse kinematic solutions via the Damped Least Squares (DLS) method, ensuring stable and continuous joint trajectories even in the presence of redundancy and singularities. Motion trajectories toward target objects are generated using the RRT* algorithm, offering optimal path planning under dynamic constraints. Object pose estimation is achieved through a vision pipeline that integrates feature-based detection with homography-driven depth analysis, enabling adaptive tracking and dynamic grasping of textured objects. The manipulator’s performance is quantitatively evaluated using smoothness metrics, RMSE pose errors, and joint motion profiles including velocity continuity, acceleration, jerk, and snap. Simulation studies demonstrate the robustness and adaptability of the proposed framework in autonomous experimentation workflows, highlighting its potential to enhance precision, scalability, and efficiency in next-generation self-driving laboratories.

Abstract: Synthetic jets, generated through the periodic suction and ejection of fluid without net mass addition, offer distinct benefits, such as compactness, ease of integration, and independence from external fluid sources. These characteristics make them well-suited for flow control and convective heat transfer applications. However, conventional sin-gle-actuator configurations are constrained by limited jet formation, narrow surface coverage, and diminished effectiveness in the far field. This review critically evaluates the key limitations and explores four advanced configurations developed to mitigate them: dual-cavity synthetic jets, single-actuator multi-orifice jets, coaxial synthetic jets, and synthetic jet arrays. Dual-cavity synthetic jets enhance volume flow rate and surface coverage by generating multiple vortices and enabling jet vectoring, though they remain constrained by downstream vortex diffusion. Single-actuator multi-orifice designs en-hance near-field heat transfer through multiple interacting vortices, yet far-field per-formance remains an issue. Coaxial synthetic jets improve vortex dynamics and overall performance but face challenges at high Reynolds numbers. Synthetic jet arrays with independently controlled actuators offer the greatest potential, enabling jet vectoring and focusing to enhance entrainment, expand spanwise coverage, and improve far-field performance. By examining key limitations and technological advances, this review lays the foundation for expanded use of synthetic jets in practical engineering applications.

Abstract:

The scientific interest in Triply Periodic Minimal Surface (TPMS) lattices for thermal applications has grown exponentially in recent years, largely driven by the advances in additive manufacturing. However, the lack of a transparent and reproducible selection methodology in previously published reviews hinders the clarity and comparability of findings. This paper adopts and customizes the APISSER framework, a structured and repeatable method that guides literature reviews through five steps: defining research questions, identifying sources, screening studies, extracting data, and reporting results. This approach is applied to investigate the use of TPMS structures in heat transfer applications, including heat sinks and heat exchangers. The study covers peer-reviewed journal articles from 2000 to 2024, analyzing key aspects such as application domain, topology, working fluid, flow regime, additive manufacturing method, and numerical modeling details. Results show a predominant use of numerical studies, with Gyroid and Diamond topologies being the most investigated. These structures are frequently modeled as porous media, especially for estimating pressure drops, although detailed thermal analysis often relies on full-resolution geometries. Water and air are the most common working fluids, while turbulence modeling remains limited to RANS approaches. The structured methodology adopted ensures high reproducibility and offers a quantitative foundation for the identified knowledge gaps to guide future experimental and computational research.

Abstract: The article is devoted to the synthesis of mathematical models of metalworking processes by cutting for digital counterparts of metal-cutting machines. Despite the development of modern measuring instruments, data acquisition and transmission systems, as well as the growth of computing power of modern computers, the problem with a high-quality mathematical description of the cutting process is urgent. Methods: When developing mathematical models of elastic-thermodynamic interaction, the authors relied on analytical methods of model construction, as well as on the analysis of experimental data obtained as a result of the conducted research. The STD.201-1 stand was used as measuring equipment; data processing was carried out in the Matlab 2018 mathematical software package. Results: A comparison of the results of mathematical modeling of the synthesized model and the results of measuring cutting processes on a metal-cutting machine show a high degree of convergence. The modeled and experimental graphs of the cutting force decomposed along the deformation axes and the graphs of the cutting temperature differ only in the area of the transient process (tool embedding). Conclusions: The models obtained during synthesis can become the basis for building a digital twin system.

Abstract: The ever-increasing use of diverse types of engineered nanoparticles (ENPs) in industries, medicine, and consumer products has resulted in their uncontrolled release into aquatic environments and soil-plant systems. ENPs may transform and release toxic by-products upon release, raising concerns about their environmental behavior and potential risks. However, accurately measuring the concentrations of ENP in these ecosystems remains challenging. Recent studies have highlighted the toxic effects of ENPs on various organisms, but assessing the risk in aquatic and soil-plant systems consists of a critical issue in nanoecotoxicology. ENPs interact with various environmental materials like organic matter, soil, sludge, and other pollutants. These interactions of ENPs can form complex assemblies, which may alter the toxicity and environmental fate. This study examines the interactions of ENPs in aquatic and soil-plant environments, focusing on their transformation, toxicity, and ecological impact. Identification of the knowledge gaps related to the ENP interaction and outlining the directions for future consideration for a better understanding of the environmental risks have been explained in this study. Additionally, the research addresses the challenges of evaluating nanotoxicity and highlights the need for improved environmental regulations and assessment techniques for engineered nanomaterials.