Search Results (1,340)

Power cycling reliability is one of the most widely used frameworks to evaluate the lifetimes of power semiconductor switching devices from a thermal stress perspective. Experimental tests can be used to predict their lifetimes under operating conditions. An estimation of the number of cycles to failure $N_f$ can also be given by several lifetime models, which express the number of cycles to end of life as a function of empirical coefficients. In the existing literature, these empirical coefficients are generally estimated using the classical least squares method (to find the best-fitting line through data points), where outliers are removed using the Random Sample Consensus algorithm. The aim of this paper is to present a general strategy for the calculation of empirical coefficients for different lifetime models, such as Coffin–Manson, Coffin–Manson–Arrhenius, Norris–Landzberg, and simplified Bayerer, aiming at minimizing the number of required experimental tests. The results show that the number of experimental trials required varies between two and four, depending on the number of empirical coefficients to be determined, which is specific to the lifetime model used. Furthermore, a limited number of experimental data points are selected to avoid any degradation in accuracy. The accuracy of coefficient estimation is significantly improved by excluding outliers: some relative errors decrease by 25%. Additionally, each empirical coefficient is determined under specific thermal stress conditions, such as a constant junction temperature swing $\mathsf{\Delta }{T}_j$, constant current per bond wire I, constant cycling frequency f, or constant mean junction temperature $T_{jm}$. Furthermore, a limited number of experimental data are selected to avoid any degradation in accuracy due to outliers. Moreover, this general method can be applied to all power devices, such as IGBTs or MOSFETs. Finally, the limitations of the analytical solution for the Scheuermann lifetime model are discussed. Full article

Introduction: Marine heatwaves are increasing in frequency and intensity, posing major challenges for fishes inhabiting shallow coastal ecosystems. Short-term exposure to extreme warming can alter metabolic performance and thermal tolerance, with potential consequences for species persistence and school composition in thermally variable habitats. Understanding the capacity of coastal fishes to withstand acute warming events is therefore essential for predicting ecological responses to climate change. Objective: We aimed to determine the effects of simulated marine heatwaves on thermal tolerance and metabolic performance in juvenile grey mullets, Chelon labrosus and Chelon aurata, two abundant sympatric species inhabiting the Ria Formosa lagoon (southern Portugal). Methodology: Juvenile mullets acclimated at 17 °C were exposed to simulated heatwave treatments of 23, 27, or 33 °C and sampled either at peak temperature or after 48 h and 1-week recovery at 17 °C. Critical thermal maximum (CTmax, using a 1 °C/min thermal ramp), static oxygen consumption (MO2), and intermittent respirometry parameters were measured. Standard metabolic rate (SMR), maximum metabolic rate (MMR), and aerobic scope (AS) were derived from intermittent respirometry. A complementary temperature-ramp (>3 h at each temperature step 17, 23, 27 and 33 °C) was performed to evaluate routine metabolic rate and estimate Q10 values across increasing temperatures. Additional plasma and tissue analyses are being conducted to assess energetic substrate mobilization and cellular responses to thermal and oxidative stress. Results: CTmax increased significantly with warming in both treatment modes, demonstrating rapid heat hardening in juvenile mullets. Fish exposed to 27 and 33 °C exhibited higher CTmax than control fish, and this elevated tolerance persisted after recovery. Chelon labrosus showed slightly higher CTmax values than C. aurata. Oxygen consumption increased with temperature, with the strongest responses occurring at 33 °C. SMR increased markedly with warming, particularly in heatwave-exposed fish, while MMR increased mainly at the highest temperature treatment. In contrast, AS showed no clear thermal optimum or decline across treatments. Routine metabolic rate increased non-linearly with temperature in the complementary ramp experiment, with a mean Q10 of 2.28, confirming strong thermal dependence of metabolism. Conclusions: Juvenile mullets possess substantial short-term thermal plasticity and can rapidly increase heat tolerance during marine heatwaves but this enhanced tolerance is accompanied by elevated metabolic costs under extreme warming, indicating potential energetic trade-offs near upper thermal limits. Differential physiological responses between species may influence school composition and ecological performance across thermal landscapes. Ongoing plasma and tissue analyses will further clarify the energetic and cellular mechanisms underlying thermal and oxidative stress resilience in coastal fishes. Full article

Aqueous precipitation and crystallization are central to impurity removal, product formation, and resource recovery in mineral and chemical processing, but robust inline monitoring remains challenging because supersaturation is not measured directly and conductivity signals are affected by temperature, composition drift, bubbles, solids, polarization, and fouling. Electrical conductivity (EC) is attractive as a low-cost, rugged process analytical tool, yet its usefulness depends on mechanistic interpretation: EC reflects charge-carrier concentration and mobility rather than supersaturation itself. This review organizes the literature into a layered framework covering (i) measurement integrity and deployment, (ii) bulk-signal extraction in multiphase media, (iii) estimation of latent variables such as dissolved concentration or supersaturation proxies, and (iv) control readiness based on conductivity-derived targets. Frequency-aware conductivity extraction, event-anchored verification, and observer-based estimation are treated as optional, complementary modules. A Ca-carbonate/CaCO₃ system is used as an illustrative case because its coupling among conductivity, pH/speciation, supersaturation, and precipitation is especially transparent, although the framework is intended for broader processing systems, including complex liquors and slurries. Opportunities are also highlighted for nanomaterials to improve both precipitation control and EC information content. Full article

Soft magnetic composites (SMCs) are attracting increasing attention for electromagnetic applications owing to their three-dimensional shape flexibility and reduced eddy current loss. In this study, the 2-Pressing 1-Annealing (2P1A) process was applied to Fe–5.0 wt.%Si SMC toroidal cores to investigate the effects of pressing temperature and 1st pressing level on densification behavior, interparticle insulation structure, and frequency-dependent electromagnetic response. DEFORM-3D FEM simulations compared relative density distribution, hydrostatic stress, effective strain, and reaction load under single-press and 2P1A conditions. The 1st pressing stage was conducted at 350 °C with 30%, 50%, and 70% pressing levels, followed by final densification at 550 °C. Increasing compaction temperature reduced reaction load and hydrostatic stress range, while the 1st pressing level affected the final density distribution and stress state after 2nd pressing. TEM-EDS confirmed continuous interparticle insulation layers, and thickness measurements were used to compare local boundary structures. Among the 2P1A conditions, the 50% → 100% condition showed the smallest upper/lower relative density difference and the narrowest insulation-layer thickness range, indicating the most balanced condition in terms of densification uniformity and interparticle boundary structure. Compared with the 550 °C single-press condition, the 2P1A compacts showed higher permeability retention and Q-factor values in the 5–20 kHz range. These results indicate that the 1st pressing level influences staged densification behavior, interparticle boundary structure, and frequency-dependent electromagnetic response in Fe–5.0 wt.%Si SMC cores. Full article

This paper presents the design, structural optimization, and two-dimensional (2D) technology computer-aided design (TCAD) simulation of a gallium nitride (GaN) vertical Merged P-i-N/Schottky (MPS) diode incorporating a multi-drift-layer doping profile, composite SiO₂/Si₃N₄ passivation, and field-plate (FP) termination. The proposed device is constructed on an n⁺-GaN substrate with a three-sub-layer n-type drift region and a p-GaN/p⁺-GaN anode region. Systematic TCAD simulations are performed to investigate the dependences of key performance metrics—including knee voltage (V_knee), specific on-resistance (R_on), breakdown voltage (BV), reverse leakage current (J_leak), and Baliga’s figure of merit (BFOM)—on the Schottky metal work function, multi-drift-layer doping concentration, drift-layer thickness, Schottky-to-PN contact length ratio (γ_w), operating temperature, and reverse recovery switching transients. Results demonstrate that the MPS architecture effectively decouples forward conduction loss from reverse blocking capability, overcoming the conventional R_on–BV trade-off. The optimal doping profile (n_mm = 2 × 10¹⁵, n_m = 2 × 10¹⁵, n = 1 × 10¹⁶ cm⁻³) achieves a BFOM of ~31.97 GW·cm⁻² with BV ≈ 5.98 kV and R_on ≈ 1.12 mΩ·cm². Joint doping–thickness optimization further identifies a graded doping profile (n_mm = 2 × 10¹⁵, n_m = 5 × 10¹⁵, n = 1 × 10¹⁶ cm⁻³) combined with layer thicknesses (T_nmm, T_nm, T_n) = (4.49, 5, 20) μm as the overall optimum, achieving BFOM = 55.36 GW·cm⁻² (BV = 6.61 kV, R_on = 0.79 mΩ·cm²)—a +73% improvement, governed by the punch-through/field-stop design principle. The optimal contact ratio of γ_w = 1.33 yields a BFOM of 38.71 GW·cm⁻². Temperature analysis confirms a positive BV temperature coefficient due to drift-region-limited avalanche breakdown, and the BFOM improves monotonically from 33.31 to 37.82 GW·cm⁻² between 200 K and 450 K. Mixed-mode switching simulations show that increasing γ_w substantially reduces reverse recovery charge (Q_rr), demonstrating the strong potential of the proposed MPS diode for high-voltage, high-frequency, and high-temperature power electronic applications. Full article

This study investigates the nonlinear dynamic response and chaos evolution of a shape memory alloy hybrid composite (SMAHC) actuator beam under coupled thermal, harmonic, and aerodynamic excitations. A reduced-order nonlinear dynamic model was developed by combining Euler–Bernoulli beam theory, von Karman geometric nonlinearity, the Brinson SMA constitutive relation, and first-order piston-theory aerodynamics. The governing equations were derived from Hamilton’s principle, discretized by the weighted residual method, and solved using the Newmark-beta algorithm. Chaotic evolution was quantified using a largest Lyapunov exponent-based chaos intensity indicator rather than the exact Kolmogorov–Sinai entropy. The reduced-order model was compared with ABAQUS finite element simulations under representative coupled aerodynamic and harmonic loading. The MATLAB prediction and ABAQUS response gave a dominant frequency of approximately 9.50 Hz, close to the prescribed excitation frequency of 9.55 Hz, with peak displacement amplitudes of approximately 0.0285 mm and 0.0324 mm, respectively. A supplementary ABAQUS modal-frequency separation check supported the use of the two-mode reduced-order model for the dominant low-frequency response, while also clarifying its limitation for high-dimensional chaotic modal interactions. The parametric results showed that an increasing excitation amplitude and aerodynamic load promoted frequency broadening and chaotic transitions. The Lyapunov-based indicator rose near γ = 65 under λ* = 100 and near λ* = 328 under γ = 30. Temperature-dependent SMA recovery stress further shifted the transition threshold by modifying the effective stiffness and internal restoring action of the beam. These results provide a reduced-order framework for interpreting nonlinear response transitions in SMAHC actuator beams in thermo-aeroelastic environments. Full article

Highway–rail grade crossing (HRGC) incidents remain a persistent safety concern due to repeated interactions between roadway users and rail operations under varying environmental and operational conditions. Existing studies rely on raw incident counts or partial exposure measures that can be influenced by differences in infrastructure exposure and do not account for spatial dependence, limiting consistent comparison across locations. This study developed an exposure-normalized framework to model incident intensity at the county level using accumulated incidents per crossing (AIPC), which normalizes cumulative incidents by crossing exposure. The analysis integrated statistical distribution modeling, spatial clustering, and supervised machine learning. The study combined county-level HRGC data for the contiguous United States from 1975 to 2025 with infrastructure, traffic, environmental, and accessibility variables. Results showed that AIPC was consistent with a gamma distribution, indicating a continuous representation of incident intensity without discrete risk regimes. Local Moran’s I identified statistically significant high-intensity clusters in specific regions, confirming spatial dependence in incident intensity. Machine learning models achieved strong predictive performance, with the extra trees model reaching AUC = 0.907 (F1 = 0.528) and ensemble methods consistently outperforming linear and kernel approaches. SHAP and permutation-based feature importance analysis identified temperature, train frequency, and accessibility measures as the most influential predictors, while aggregate density measures contributed the least. The results provided consistent evidence that incident intensity was associated with environmental conditions, operational exposure, and network structure. The proposed framework supports exposure-based risk assessment and enables identification of high-intensity counties for targeted intervention. This approach provides a transparent and transferable method for improving HRGC safety analysis and prioritizing resource allocation across large geographic areas. Full article

In our research, we developed a Distributed Relaxation Spectrum Delay Differential Equation (DRSDDE) model to simulate viscoelastic responses exhibited by materials with multiple-scale relaxation mechanisms and finite delay times. Our model expanded upon traditional integer-order viscoelastic models to include a continuum relaxation process using a log-time-space Gaussian distribution representing a continuum of relaxation processes, including a direct representation of the effect of delayed feedback via an explicit time delay term. Consequently, the resultant model can be viewed as a generalized Maxwell-type formulation where the viscoelastic behavior exhibits distributed relaxation dynamics and has finite signal propagation characteristics. We then used experimental data obtained from three representative materials: PDMS Sylgard 184, bovine brain white matter, and polyurethane foam to calibrate the model. Calibration was achieved by estimating model parameters through the use of Gauss-Legendre quadrature combined with non-linear optimization of the relaxation spectrum. The results indicate that the coefficients of determination for each of the materials exceeded $R^2>0.83$. Therefore, the proposed DRSDDE model outperformed the classical Zener model when simulating materials that exhibit a wide relaxation spectrum. The parameter values estimated for each of the examined materials provided additional insight into their physical behaviors. Specifically, the characteristic relaxation times for the studied materials were determined based upon ${\tau }_c= {10}^{\mu }$ ranging from about 63 s to 158 s. These results illustrate different dominant relaxation regimes for the investigated materials. Additionally, both characteristic equations and frequency domain analyses were utilized to study the stability and bifurcation properties of the DRSDDE model. A significant finding resulted from identifying a delay-insensitive stability regime for materials with $\tilde{K}< 1$ (as illustrated by bovine brain white matter). For materials with $\tilde{K} > 1$, the analysis revealed Hopf bifurcation results illustrating critical delay thresholds and frequencies for the onset of oscillations. Further, it was established that all calibrated delay values were significantly less than these threshold values. This indicates that all identified models functioned well below the oscillation thresholds at realistic delay times. Ultimately, the proposed DRSDDE model represents a physically intuitive, robust, and flexible method for modeling complex viscoelastic systems. Future research will involve investigating temperature-dependent effects, nonlinear bifurcations, and experimental validations of predicted oscillatory dynamics. Full article

Microwave-based remote sensing possesses the capability to penetrate through atmospheric obstructions such as cloud layers and fog, making it extensively utilized for estimating parameters including soil water content, atmospheric moisture levels, and terrestrial surface temperatures. Extended temporal datasets serve as fundamental requirements for climatological investigations; however, individual satellite operational lifespans remain constrained and prove inadequate for establishing multi-decade temporal sequences. Consequently, conducting comparative analyses and implementing cross-calibration procedures across measurements obtained from distinct sensors exhibiting comparable operational features becomes imperative. The FengYun (FY)-3G spacecraft, deployed into orbit during April 2023, hosts China’s most recent orbiting microwave radiometric instrument, designated as the Microwave Radiation Imager–Rainfall Mission (MWRI-RM). The FY-3G satellite’s unique drifting equator crossing time orbit plays a critical role in the calibration behavior of the MWRI-RM instrument, representing a key novelty of this study. The reliability of its brightness temperature (TB) observations has attracted considerable attention. Within this investigation, we conduct comparative assessments of orbital TB observations acquired from FY-3G/MWRI-RM against corresponding measurements obtained from the Advanced Microwave Scanning Radiometer 2 (AMSR2) installed on the Global Change Observation Mission–Water 1 (GCOM-W1) platform, and establish a straightforward linear inter-calibration methodology. Both sensing systems show strong consistency, with correlation coefficients exceeding 0.9 for all corresponding channels and systematic biases ranging from −1.40 K to −0.14 K. FY-3G/MWRI-RM generally reports lower TB values than GCOM-W1/AMSR2. The inter-sensor differences vary with frequency, land cover type, and TB range. Larger negative biases are mainly observed at 23.8 GHz and over water bodies, whereas the biases at 89 GHz are generally close to zero for most surface types. Latitude-dependent TB biases are most evident at 10.65 and 18.7 GHz, especially for vertical polarization at high latitudes, while orbit-dependent differences are more pronounced for vertically polarized low- and mid-frequency channels. After applying an inter-calibration procedure using AMSR2 as the reference, the agreement between FY-3G/MWRI-RM and GCOM-W1/AMSR2 is improved substantially, with mean biases below 0.25 K and RMSE values below 2 K for all channels. Validation using independent datasets further supports the stability of the calibration. The calibrated FY-3G/MWRI-RM TB data provide a basis for constructing long-term passive microwave brightness temperature records and for retrieving land and atmospheric parameters. Full article

The influence of internal substrate geometry on thermal stability during Laser Directed Energy Deposition Repair (DED-R) remains insufficiently understood, particularly for components containing internal cavities and cooling channels. This study investigates the thermal response of solid (Alpha), blind-hole (Bravo), and channeled (Charlie) AISI 316L substrates using dual infrared thermography, transient finite element modeling, and Short-Time Fourier Transform (STFT)-frequency-domain analysis. Despite substantial differences in internal heat-dissipation pathways, all substrate configurations exhibited similar peak surface temperatures (~1700–2100 °C), indicating that conventional temperature monitoring alone is insufficient to distinguish geometry-dependent melt-pool behavior. To address this limitation, a Spectral Entropy Index (SEI) derived from STFT analysis was proposed to quantify thermal stability. The channeled substrate exhibited the lowest entropy value (H_s = 0.172), compared with the solid (H_s = 0.181) and blind-hole (H_s = 0.183) configurations, indicating a more ordered and predictable thermal response. Furthermore, distinct variations in the spectral stability shadow revealed geometry-dependent oscillatory behavior that was not observable from thermal histories. Finite element simulations showed good agreement with experimental measurements in conduction-dominated regions (RMSE ≈ 46 °C), whereas deviations were observed within the melt-pool region (~250–310 °C), highlighting the increasing influence of fluid-flow phenomena not captured by the conduction-based model. The results demonstrate that internal substrate architecture primarily influences melt-pool stability through frequency-domain thermodynamics rather than significant changes in peak temperature. The proposed STFT method provides a quantitative approach for monitoring thermal stability and assessing the feasibility of L-DED repair over complex internal geometries. Full article

This study investigates the dielectric relaxation and conduction mechanisms in $S{e}_{90}S{n}_{6}P{b}_4$ chalcogenide glassy material, which is of interest for applications in phase-change memory devices, optical memory, and thermoelectric sensors. Despite previous studies on chalcogenide glasses, the conduction mechanisms at varying temperatures and the role of correlated barrier hopping (CBH) remain unclear. Using impedance spectroscopy in the frequency range 1 Hz–1 MHz at temperatures from 288 K to 318 K, the real ($Z\prime$) and imaginary ($Z″$) parts of the complex impedance were recorded. The sample was also characterized by X-ray diffraction (XRD) to confirm its glassy nature, and X-ray photoelectron spectroscopy (XPS) to determine the surface chemical composition and oxidation states of the elements. Peaks in $Z″$ at each temperature were used to evaluate the relaxation time τ, revealing thermally activated processes with an activation energy of 0.62 eV. Nyquist plots showed semicircular behavior with decreasing radii at higher temperatures, indicating enhanced d.c. conductivity with an activation energy of 0.63 eV. A.C. conductivity analysis demonstrated frequency-dependent behavior consistent with the CBH model, with hopping energy calculated as 0.32 eV. The dielectric loss increased with temperature and decreased with frequency, stabilizing above 250 Hz at 318 K. These findings provide new insights into the dielectric and conduction properties of $S{e}_{90}S{n}_{6}P{b}_4$ glasses, supporting their optimization for practical electronic applications. Full article

Vanadium dioxide (VO₂) is a phase-change material, which changes its properties under thermal or optical stimuli. Thanks to the fact that the material phase transition appears at conditions which are close to environmental ones, VO₂ has been widely used in diverse structures, including metasurfaces, that acquire switching and reconfigurability capabilities. In this paper, we numerically study the functionality-enabling properties of dielectric resonator-based nondiffractive meta-atoms that comprise small VO₂ components, i.e., covers or drops, in switchable coding and wavefront-manipulating scenarios at THz frequencies. The goal is to unveil the potential of these meta-atoms in switching the reflected wave’s phase coverage under temperature variations. The main attention is paid to how the shape and size of the VO₂ components affect the functionality switching that is enabled by the changes in coverage. It is shown that metallic and insulator states of VO₂ can play different roles in diverse switching scenarios. Different resonance regimes exert different influences on the resulting capability of switching, while contributing to multifunctional operating scenarios. Possible roles of state-dependent absorption are clarified. Full article

High-temperature vulcanized (HTV) silicone rubber serves as the core material for composite insulators, and its high-frequency dielectric properties directly dictate its macroscopic insulation performance. However, traditional electrical detection methods encounter a “high-frequency blind zone” above the gigahertz (GHz) range due to limited precision and ambiguous physical mechanisms. In this study, terahertz time-domain spectroscopy (THz-TDS) was employed to characterize the complex permittivity spectra of silicone rubber specimens, incorporated with varying ratios of alumina trihydrate (ATH) and silica (SiO₂) fillers, across the 0.1–3.0 THz frequency range. Experimental results reveal that the terahertz dielectric characteristics of silicone rubber exhibit a pronounced filler dependency: as the ATH content increases from 95 phr to 185 phr, the real part of the permittivity at 1 THz increases by 32%. Notably, all specimens manifest a sharp dielectric transition near 1.2 THz, characterized by distinct dual absorption peaks in the imaginary permittivity spectra. To characterize this non-linear transition, a hybrid Debye–Lorentz model is innovatively introduced. This approach overcomes the inherent limitations of traditional double Debye models, which are restricted to relaxation processes and fail to account for high-frequency resonance. Fitting results and physical analysis demonstrate that the response at 1.2 THz is primarily attributed to the bending vibrations of Si-O-Si bonds in the polymer backbone, alongside the collective vibration modes of Al-O bonds and the hydrogen-bonded network within the fillers. The hybrid model successfully decouples three distinct polarization mechanisms: conduction loss (<0.5 THz), dipole relaxation (0.5–1.0 THz), and lattice resonance (>1.0 THz). This work provides a robust characterization framework for the quantitative evaluation of the high-frequency dielectric response and microstructural integrity of composite insulators. 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

Additive manufacturing by material extrusion enables the fabrication of geometrically complex components, yet the extent to which process parameters can be used to tailor stiffness and damping in a frequency-dependent manner remains insufficiently understood. This study investigates the influence of key material-extrusion process parameters (layer height, printing speed, extrusion temperature, build plate temperature, and flow rate) on the flexural storage modulus $E^{\prime }$ and loss factor $\eta$ of PETG specimens over a frequency range of 125 to 4000 Hz. Frequency-resolved regression models were established for six reference frequencies using VIF-based term reduction and hierarchical backward elimination. The results reveal a clear contrast between stiffness- and damping-related responses. The model structure for $E^{\prime }$ remained invariant across all frequencies, achieving consistently high coefficients of determination ($R^2$ = 0.831–0.847). In contrast, the model structure for $\eta$ varied markedly with frequency ($R^2$ = 0.215–0.763). Extrusion temperature was identified as a consistently significant factor for $\eta$ across all frequencies ($p<0.05$), while a robust nonlinear dependence on flow rate dominated most frequency bands. Reduced model adequacy for $\eta$ was observed at specific bands, showing significant lack-of-fit at 500 Hz ($p_{LOF}=0.049$) and non-normal residuals at 4000 Hz ($p_{JB}=0.003$). These findings demonstrate that stiffness can be tuned reliably using frequency-invariant process relationships, whereas damping requires frequency-aware parameter selection. This approach provides a statistically rigorous basis for optimizing additively manufactured components where both stiffness and energy dissipation are performance-critical. Full article