Composite material systems involving impinging flame jets on flat surfaces are critical in applications requiring precise thermal management and energy efficiency. A prominent example is Teflon-coated steel cookware, where the Teflon layer not only provides a nonstick surface but also influences thermal performance. Optimal coating thickness is essential: excessive thickness can reduce heat transfer efficiency, while insufficient thickness may compromise adhesion and durability. This study investigates the thermal interaction between a premixed flame jet and a Teflon-coated steel substrate using high-fidelity simulations in OpenFOAM. A conjugate heat transfer approach captured the coupled heat fluxes between the flame, steel substrate, and Teflon layer. Teflon thicknesses ranging from 0.01 to 0.20 mm were systematically analyzed to evaluate their effect on heat transfer performance. Simulation results enabled the development of a thermal efficiency model as a function of Teflon thickness, achieving a high correlation (R² = 0.9923). The proposed model offers quantitative guidance for optimizing coating thickness, providing a practical tool for the design and manufacturing of thermally efficient cookware.
Drilling of Fiber Metal Laminates (FMLs) is a very important manufacturing operation, as the metal-composite layered structure tends to undergo delamination, burrs, and dimensional errors. Although to date research work has mainly focused on cutting conditions, drilling tools, and lubrication methods for improved performance, the role of surface engineering on metal sheets prior to laminate fabrication has not received significant consideration. In this study, the effect of the variation of feed rate values during the drilling operation of FMLs with laser-textured titanium sheets is examined. Specifically, prior to FML consolidation, a unidirectional pattern with spacing characteristics of 200 µm was created on the titanium sheets; then, drilling tests using twist drill bits were carried out at a fixed cutting speed while varying the feed rate on two levels. The results show that a higher feed rate value induces both greater thrust force – and therefore improved adhesion resistance at the interface between the various materials of the multilayer – and a deterioration in the surface quality of the hole, increasing the occurrence of fiber pull-out and metal transfer phenomena.
Laser surface texturing (LST) is increasingly adopted to functionalize the surface in injection molding, enabling the control of interfacial and tribological phenomena without altering bulk material properties. While most studies have focused on mold cavities, the functionalization of ejection system components remains largely unexplored, despite its critical role in part release and process stability. This work presents a preliminary investigation of laser surface texturing for cylindrical ejector pins to promote lubricant retention at the pin–mold interface. A parametric study was first carried out on a flat to define a process window compliant with the maximum allowable groove depth constraint (20 µm). Based on this campaign, a stable ablation regime was identified and transferred to cylindrical ejector pins, where textures were fabricated along axial length. Different micro-texture geometries and spatial distributions were designed to generate controlled micro-reservoirs for lubricant retention. The textured surfaces were characterized in terms of groove depth, morphology and uniformity, confirming the feasibility of producing shallow and well-defined features within industrial constraints. The preliminary results demonstrate the technical feasibility of laser texturing on cylindrical ejector pins and its potential to modify the pin–mold interface. However, the comparative effectiveness of the different texture geometries in promoting lubricant retention will be further evaluated under extended service conditions. The study, therefore, establishes the basis for the functional optimization of textured ejection systems in injection molding applications.
Efficient thermal management is a key factor in improving the sustainability and productivity of injection moulding processes, particularly at the micro-scale where thermal transients strongly affect part quality and cycle stability. This work investigates the thermal behaviour of hybrid moulds composed of polymeric support plates manufactured in Precision Resin V01 and stainless-steel inserts manufactured by additive manufacturing. An experimental campaign was carried out on a micro-injection moulding machine to characterize the intrinsic thermal response of the mould under uncooled conditions. Temperatures were monitored through embedded thermocouples and used to develop and calibrate a three-dimensional transient numerical model in COMSOL Multiphysics. Particular attention was devoted to the identification and calibration of heat transfer coefficients at the injection and extraction interfaces, which were found to play a dominant role in governing insert temperature evolution. The calibrated model accurately reproduces the experimental thermal transients, with deviations below 10%, demonstrating its reliability as a predictive tool for analysing mould thermal behaviour and supporting early-stage design and process optimization. The results highlight the advantages of hybrid architectures in promoting thermal stability and provide a robust methodology for modelling heat exchange in unconventional mould configurations.
Conventional hard-bait lure prototyping relies on manual shaping, full-body additive manufacturing or early-stage injection moulding, each associated with limitations in geometric repeatability, development time or tooling cost. This paper evaluates a hybrid approach combining thermoformed PETG outer shells with additively manufactured internal frames to produce batches of geometrically consistent lure bodies with tuneable internal mass layouts. Across several educational development projects, the process enabled fast replication of outer form, systematic variation of ballast and harness configuration, and prototype assembly suitable for qualitative hydrodynamic observation. Compared with full additive manufacturing or manual crafting, the method reduced fabrication effort for multi-variant batches and delivered mould-like surface quality. Joining reliability of shell halves emerged as the dominant limitation, with elastic polyurethane adhesives outperforming brittle cyanoacrylate and poorly controllable low-energy fusion. The results position thermoforming as a methodologically valuable prototyping tool where external geometry is stable but internal behaviour requires iterative adjustment. Future work should address seam design, cage-shell tolerances and sealing repeatability to support quantitative hydrodynamic testing and assess whether the process has potential beyond prototyping applications.
Dry machining of gears demands advanced coating technologies to withstand high thermal and mechanical stresses. In this study, AlCrN coatings were deposited using the newly developed Focused Magnetron Sputtering (FMS) process and compared with conventional Cathodic Arc Evaporation (CAE)-AlCrN and boroncontaining CAE-AlCrBN coatings. XRD analysis showed that FMS produced a finegrained crystal structure with half the full width at half maximum (FWHM) of CAE-AlCrN. Stressoptimised deposition allowed a 60 % higher coating thickness with improved adhesion. Analogy gear hobbing tests (fly cutting tests) demonstrated that FMS-AlCrN had 52 % lower crater wear than CAE-AlCrN, while CAE-AlCrBN also improved crater wear resistance due to boroninduced grain refinement. However, both finegrained coatings exhibited increased flank wear compared to the coarse-grained CAE-AlCrN coating. The results show that FMS enables the production of dense, fine-grained coatings with superior adhesion and crater wear resistance, highlighting its potential for dry gear hobbing. Further optimisation of hardness and microstructure is required to balance crater and flank wear behaviour.
This study investigates the effect of incorporating Phase Change Material (PCM) into the collector of a Solar Chimney Power Plant (SCPP). Numerical simulations were conducted using a 2D axisymmetric geometry, considering transient, turbulent, and radiative heat transfer. The results show that the PCM-enhanced system maintains higher temperatures at the collector outlet, with an average increase of approximately 6%. This thermal regulation enhances buoyancy-driven airflow, raising the chimney base velocity from 2.5 m/s in the conventional system to 3 m/s in the PCM configuration, with a corresponding increase in mass flow rate of 0.013 kg/s. Furthermore, the collector efficiency improves significantly, reaching 23% with PCM compared to 11% without. These findings demonstrate that integrating PCM into the collector effectively boosts thermal energy retention and system performance.
Non-woven fabrics are made of fiber mesh and carded or spun. They have the advantages of low cost, high output, easy production. The manufacturing process has a small carbon footprint and environmental benefits, which is in line with today's environmental issues and the important value of a circular economy. However, non-woven fabrics are thin and not very hard. They are usually assembled with other materials by vibration welding or adhesion, but they often lose their replaceability. This study attempts to develop a nonwoven fabric locking method and explore the impact of different friction stir drilling parameters on the formation of the boss and bushing.
Carbon fiber reinforced thermoplastic (CFRTP), such as carbon fiber reinforced polyetheretherketone (CF/PEEK), are applied in aerospace structures because of their high specific strength and recyclability. In this study, cutting tests were conducted to investigate cutting force in drilling of CF/PEEK composites. The lower cutting force was measured at the higher spindle speed. Temperature distributions on the exit side of the hole were compared between two spindle speeds, the temperature at high spindle speed indicates a higher value. The different conditions on machined hole walls were observed between the two spindle speeds. Then, an energy based force model was applied to analyze the thrust and torque during drilling, in which the cutting edge was discretized, and the chip flow was determined to minimize cutting energy. Based on the predicted shear and friction works, a finite difference thermal analysis was performed to evaluate temperature distributions in the tool, chip, and workpiece. The analysis indicated that higher spindle speed leads to an increase in cutting temperature. The results suggest that temperature-dependent behavior of the thermoplastic matrix may influence the shear stress on the shear plane and thereby contribute to the reduction in cutting force at the higher spindle speed.
This study proposes an automated framework for online cutting tool wear classification in CNC turning using low-cost optical equipment and Convolutional Neural Networks (CNNs). Longitudinal turning experiments were performed on CK45 medium carbon steel using a HAAS TL1 lathe under dry machining conditions. Tool wear evolution was monitored via a lathe-mounted digital microscope, with images classified into three distinct stages: Low (Vb<160 μm), Medium (160≤Vb≤200 μm), and Critical (Vb>200 μm). A shallow CNN architecture, consisting of three convolutional blocks and a Softmax output layer, was developed to balance model complexity with computational efficiency for potential edge deployment. To enhance robustness against positional changes, data augmentation techniques including random translations and rotations were applied. The results demonstrate good performance, with the model achieving 94.7% accuracy and a weighted F1-score of 95.4% on the testing subset. While the model showed exceptional performance in identifying Low and Medium wear, data scarcity in the Critical wear class remained a limiting factor for recall. Overall, the study confirms that shallow CNNs can accurately capture spatial hierarchies for image-based wear assessment.
Additive manufacturing by laser powder bed fusion (LPBF) is increasingly applied to aluminium alloys; however, the resulting surface quality and machining behaviour remain critical challenges, particularly when post-processing is required. In this context, the interaction between LPBF process parameters and advanced cooling strategies during machining remains largely unexplored.This study examines the impact of cryogenic machining on the surface integrity of LPBF-produced AlSi7Mg components, fabricated with varying layer thicknesses. Specimens were machined under fixed cutting parameters using either conventional flood cooling or cryogenic cooling. Cutting forces, surface roughness, defect morphology, and subsurface microstructure were systematically evaluated.Cryogenic cooling consistently reduced cutting forces and improved surface quality, effectively suppressing tearing formation. In contrast, under flood cooling, the influence of the microstructural differences induced by layer thickness remained significant, with increasing LPBF layer thickness further enhancing both surface and subsurface integrity. Overall, the results reveal a strong interaction between LPBF parameters and cooling strategy, highlighting the unexpectedly beneficial role of cryogenic machining in improving the surface integrity of LPBF-processed AlSi7Mg alloys.
Studying turbulent mixing, stress redistribution, pressure losses, secondary flows, and energy dissipation enhances industrial process efficiency, improves equipment durability, minimizes operational costs, optimizes fluid transport systems, supports design and promotes energy conservation. However, little is known about the influence of outlet geometry on turbulence characteristics (i.e. turbulent kinetic energy, turbulent intensity, effective viscosity , and effective thermal conductivity) in air, water, and kerosene flowing through Y-shaped copper ducts featuring regular, converging, and diverging outlets. A hybrid geometric configuration incorporating angular inlets and asymmetric outlets enables detailed analysis of turbulent mixing, stress redistribution, pressure losses, and secondary flow development in complex internal flow regimes. Numerical simulations were conducted using ANSYS Fluent 2023 R2, employing the shear stress transport (SST) turbulence model for accurate resolution of adverse pressure gradients and boundary-layer effects. High-quality meshing, grid independence validation, and robust solver configurations ensured numerical reliability and convergence. The results demonstrate that outlet geometry significantly influences turbulence intensity, effective viscosity, and thermal conductivity across different working fluids. For air, increasing inlet velocity enhances turbulent kinetic energy and turbulence intensity at the regular outlet, while the diverging outlet exhibits peak turbulence at higher cold-fluid velocities. In water flows, the converging outlet shows substantial turbulence growth under increased velocity conditions, highlighting the role of geometric restriction in enhancing mixing and energy dissipation. For kerosene, the regular outlet achieves maximum effective thermal conductivity due to improved fluid interaction and turbulence under elevated velocity conditions, whereas the diverging outlet exhibits lower viscosity as a consequence of geometric flow dispersion. Overall, the findings underscore the critical role of outlet configuration in determining turbulence behavior and thermal-fluid transport characteristics.
Local electrical properties of a 4H-Silicon Carbide SiC(0001) 4°off macrostepped surface, obtained after liquid Si melting in a SiC/Si/SiC sandwich configuration, are investigated by Atomic Force Microscopy (AFM) in both DC and RF modes. On the same sample, macrosteps that are wide enough for allowing spatial resolution of the signal from terraces and step risers, but also some unreacted areas with standard flat surface (without macrosteps) are characterized. Scanning Spreading Resistance (SSRM, DC mode) reveals homogeneous conductivity on the wide terraces of the 4H-SiC(0001) macrosteps. On unreacted areas, which contain many step risers, the resistance is found higher than on the wide terrasses but it is also noisier. In addition, the AFM-RF scanning Microwave Impedance Microscopy (sMIM) mapping confirms the previous results by revealing lower conductivity on the unreacted areas than on the terraces of the macrosteps. Based on these results, some points defects located at the step risers which contribute negatively to the electrical properties of 4H-SiC(0001) surface are identified and electrically characterized.
The vacancy-carbon interactions control defect kinetics and the properties of ferritic iron. Here we use spin‑polarized density‑functional theory on 3×3×3 bcc‑Fe supercells with one to four carbon atoms in octahedral interstitial sites to quantify how carbon modifies vacancy energetics. Two limiting families are considered: dilute configurations with C far from the vacancy and compact VCₙ clusters ( n=1-4 ) with C placed in the nearest octahedral shell. For the dilute case, the vacancy formation energy remains close to that of pure Fe. In contrast, for compact clusters the effective formation energy of a vacancy bound to carbon, E f_VC , decreases markedly with increasing n, while the total binding energy increases and then saturates. The incremental stabilization E add stays positive up to n = 3 and turns negative for n=4 . E trap is small and positive for n=1-2, but becomes negative for n ≥ 3, consistent with carbon‑rich microenvironments biasing vacancies into VCₙ states. Increasing n moderately reduces N(E F ) , particularly in the minority-spin channel, which is consistent with strengthened Fe-C hybridization and the larger binding energies of the VC n complexes. Finally, using a Seydel thermodynamic trapping model parameterized by our ab initio E bind ( VC n ) we predict effective vacancy diffusivities D v_eff ( T,C tot ) that exhibit trends in line with prior analyses, while reflecting the stronger trapping implied by our energetics. Consistent with our DOS analysis, increasing carbon content strengthens Fe-C hybridisation, shifts Fe d states to lower energies and reduces N(E F ) , indicating a gradual transition from metallic Fe-Fe bonding to a more covalent Fe-C bond character. This work closes an important gap between electronic-structure data and mesoscale modelling by providing a consistent set of vacancy-carbon energetics and effective vacancy diffusivities for dilute C in α-Fe, which serves as a model system for ferritic Fe-based alloys.
In this study, the thermomechanical behavior of PMMA(poly-methyl methacrylate) during high-temperature vacuum forming was analyzed through both experimental and computational approaches. The material behavior of PMMA was modeled as a temperature and strain-rate dependent viscoplastic response, coupled with time-dependent creep deformation. The creep behavior was represented by the Norton–Bailey power law (Eq. 1), while the constitutive model for the strain rate and temperature-dependent stress-strain behavior was implemented in ABAQUS via a user subroutine (UHARD). The forming process was simulated by using ABAQUS/Standard VISCO solver, incorporating vacuum pressure loading and clamping conditions. The numerical framework enables effective analysis of deformation behavior under thermomechanical forming conditions and provides a basis for process-oriented modeling of PMMA vacuum forming.
Silicon carbide is a leading wide-bandgap semiconductor for high-voltage power electronics. For 6.5–10 kV operation, thick epitaxial layers (≥60 µm) are required to sustain depletion width and maintain uniform electric fields, placing a premium on low extended-defect densities in both substrate and epilayer. Thick epitaxial 4H-SiC layers of 60 µm and 110 µm were grown on 6-inch substrates in a multi-wafer warm-wall reactor and evaluated by synchrotron X-ray topography in grazing-incidence (22-4 16) and transmission (11-20) geometries. Transmission imaging showed substrate dislocation content near the lower bound typically reported for 6-inch wafers. Notably, grazing-incidence topography (penetration depth >40 µm) revealed no basal-plane dislocations propagating into the epilayers, consistent with efficient dislocation conversion at the substrate–epilayer interface. The 3C-SiC inclusion density was ~30 per 6-inch wafer for 60 µm epilayers and ~60 per wafer for 110 µm epilayers; the average micropipes density varies from 0 to 5 for both 60 and 110 um epiwafers. Threading dislocation densities—screw, edge, and mixed—were on the order of 1.0–2.0 × 10³ cm⁻². These results establish thick 4H-SiC epilayers with suppressed basal-plane propagation and substantially reduced extended-defect content, providing a strong basis for reliable 6.5–10 kV device fabrication.
This study presents a static finite element analysis of the milling of a flexible unidirectional glass fiber–reinforced polymer (UD-GFRP) plate. The workpiece is modeled as a clamped–free cantilever, with cutting forces evaluated independently of structural deflections and applied along the machined edge. SC8R continuum shell elements are employed to accurately represent through-thickness loading and bending behavior. A mesh sensitivity analysis is conducted to determine a suitable discretization, leading to a 64 × 56 × 8 element mesh. For the investigated configuration (, mm/tooth), the out-of-plane displacement reaches approximately 120 µm near the free end of the plate, whereas in-plane displacements reach up to-75 µm. These in-plane displacements are greater than or equal to the nominal feed per tooth, indicating a highly significant influence on chip formation. This work provides a basis for understanding the structural response of flexible composite plates during trimming and emphasizes the need for coupled force–deformation formulations.
This study presents a computational investigation of melting enhancement in a triplex tube heat exchanger TTHX with rectangular fins considering RT-82 as the phase change material (PCM) and copper as nanoenhanced PCM (NEPCM). The number of fins was increased from 8 to 12, and copper nanoparticles were dispersed at a volume fraction of 2% and 3% to assess their effect on thermal energy storage TES. The evolution of the solid–liquid interface was simulated using the enthalpy–porosity formulation, and the system performance was evaluated in terms of liquid fraction, total melting time, and melting efficiency, defined as the ratio of latent heat absorbed to the input energy. Raising the number of fins from 8 to 12 reduced melting time by 30% relative to the reference case with eight fins only, indicating enhanced heat conduction and faster initial melting. The use of copper nanopcm with 3 vol% further cuts melting times by 55% compared with the same reference case due to the increase in effective thermal conductivity. Hence, in brief, moderate fin numbers coupled with 3 vol% copper rendered the fastest melting rates and highest storage efficiencies among those tested.
In modern precision manufacturing, optimizing complex processes like turn-milling is crucial for reducing production costs and ensuring high surface integrity. In this study the application of artificial intelligence, specifically machine learning (ML), for modeling turn-milling processes is investigated. The complexity of machining operations and the multitude of influencing input parameters often lead to time-consuming setups, particularly in single-part or small series manufacturing. Traditional process monitoring methods frequently fall short due to system complexity, prompting the exploration of ML for process optimization and automation. Focusing on orthogonal turn-milling, experimental data was collected to address regression problems such as tool wear and surface roughness, as well as tool condition classification. Three regression models - linear, polynomial, and support vector regression (SVR) - and four classification models - logistic regression, neural networks, support vector machines (SVM), and decision trees - were trained and validated using k-fold cross-validation. For regression models, root mean square error (RMSE) was used as the performance evaluation metric, while accuracy and F1-score were employed for classification problems. The results indicate that ML algorithms provide enhanced flexibility and accuracy compared to traditional statistical techniques, offering potential reductions in time and costs in process setups. By optimizing parameters iteratively, ML models demonstrate higher precision, reducing the need for extensive empirical research and the associated experimental costs. The developed models can be adapted to various manufacturing processes with minimal code adjustments, broadening their applicability and efficiency.
8-inch 4H-SiC single crystals were grown under different temperature fields and nitrogen doping conditions by physical vapor transport method. The distributions of basal plane dislocation (BPD) in 4H-SiC single crystals under different growth conditions were studied by molten KOH etching and X-ray Topography (XRT). The results indicate that the BPDs in the crystals grown under convex temperature field are distributed at the edge. In comparison, the BPD distributions in crystals grown under a concave temperature field are relatively closer to the center. Furthermore, the BPDs distributions in nitrogen-doped crystals exhibit quadratic symmetry caused by prismatic slip. In contrast, no prismatic slip-induced slip bands were observed in the undoped crystals, and the BPD distributions in the undoped crystals are consistent with the shear stress distribution caused by basal plane slip.
