Experimental Data Research Articles

Theoretical Design of Strengthened Nanotwinned γ*-Boron.

The distinctive electron deficiency and unusual multicenter bonding situations of boron give rise to fascinating chemical complexity and imaginative structural polymorphism. Herein, we employ an independently developed method to construct the new twinned γ*-boron based on the well-known hardest elemental boron, γ-B28. Notably, the newly propounded γ*-boron phases exhibit considerably close energy levels with γ-B28 under ambient conditions. The simulated X-ray diffraction patterns of stable twinned structure present excellent agreement with experimental data. First-principles calculations reveal a 7.5% increase in the ideal Vickers shear strength of γ*-boron compared to γ-B28, attributed to diverse bond responses within the twinned slabs. The evaluated hardness of nanotwinned γ*-B reaches 59 GPa in consideration of the size hardening effect. Our research presents an efficient strategy for constructing new polymorphs of boron with improved mechanical properties and expands the knowledge about twinning structures of boron.

A study of U-isotope ground state properties with Covariant Energy Density Functional

Abstract In this paper we have systematically analyzed the ground state of uranium isotopes from 225 to 240. In our calculations, we used the covariant energy density functional of density dependent meson exchange interaction (DD-ME2) with separable pairing interaction (TMR). Using the multiple deformation constraint, we calculate the potential energy surface (PES) of the uranium isotopes for
both even-even and even-odd nuclei with quadrupole and octupole deformation. By comparison with experimental data and Hartree-Fock-Bogoliubov calculations with Gogny D1S, the ground state of the uranium isotopes is always preferred to reflection-asymmetric deformation with our calculation.

Acrylonitrile‐butadiene‐lignin thermoplastic rubber adhesive for enhanced metal‐to‐metal joining

AbstractWith the growing requirement for lightweight structural materials in automotive, aerospace, and infrastructure applications, multi‐material joints made with adhesive have attracted intense research interest. Commercial thermoset adhesives are one‐time cures, and difficult to disassemble the bonded components for repair and recycling. Our prior work with a thermoplastic acrylonitrile‐butadiene‐lignin rubber (ABL) addresses this sustainability/recycling challenge, but the adhesive exhibits deficient joining strength compared to standard thermosets. Here, we modify the ABL matrix by loading particulate fillers to enhance its modulus and toughness. The goal is to manufacture a cure‐free thermoplastic adhesive system with a simple dispensing protocol and characteristic ductility combined with a high yield stress for improved shear strength of a bonded joint. Fumed silica (FS) and epoxidized glass spheres (EGS) were used as fillers in the ABL to promote the dispersion of lignin particles that tailored the functionalities and free energy components of the adhesive surface. With optimal loading of FS (5 wt%) and EGS (30 wt%) in the ABL adhesive matrix, the lap‐shear strength of the bonded aluminum joint was elevated by 128%, compared to the neat ABL, reaching 21 MPa, which is 90% of the performance of a commercial epoxy‐based adhesive.Highlights A partly renewable filler‐toughened thermoplastic adhesive has been developed. This thermoplastic gives nearly equivalent performance of adhesively bonded aluminum joint compared to standard thermosets. Experimental and simulation data help understand the adhesive reinforcing mechanism by the fillers.

Andrographolide Improves ApoE4-Mediated Blood-Brain Barrier Injury by Alleviating Inflammation.

The pathological and physiological studies of Alzheimer's disease (AD) have been in-depth, and apolipoprotein E4 (ApoE4) has been proven to be highly correlated with AD, and clinical and experimental data show that ApoE4 can cause blood-brain barrier (BBB) injury, and the change of BBB permeability is an important factor affecting the development of AD. Andrographolide (Andro), as the active component of the natural plant Andrographis paniculata, has been proven to have anti-inflammatory and antioxidant effects, which have potential neuroprotective effects. To verify the protective effect of Andro on BBB in a short term, our research group used atorvastatin (Atorva)-mediated zebrafish brain injury model and the ApoE4-mediated cell co-culture model of BBB injury to explore the protective effects and mechanisms of Andro on BBB injury. Studies have shown that Andro can inhibit the activation of CypA/NF-κB/MMP-9 signaling pathway and has achieved the effect of antagonizing the inhibition of ApoE4 on intercellular tight junction proteins (occludin, claudin-5, and ZO-1). At the same time, Andro can inhibit the secretion of cell adhesion molecules (VCAM-1 and ICAM-1) in cells, thereby delaying the occurrence and progression of neuroinflammation and playing a protective role in BBB. In conclusion, Andro is a potent natural product which can protect the blood-brain barrier.

Using asymptotic homogenization and machine learning to derive a 3D trace theory for GFRP

Tsai’s modulus was proposed as a trace theory for CFRP, where only one property is measured experimentally and the other four properties are computed using the normalized trace relation. An extension of this theory is proposed for GFRP. Laminae elastic properties are generated using the asymptotic homogenization and a machine learning training is performed applying decision trees algorithm to define normalized trace relations for GFRP. The results are validated with experimental data of 12 GFRP laminae, indicating average errors around 5% for longitudinal and transversal elastic moduli and in-plane Poisson’s ratio, and around 10% for in-plane and out-of-plane shear moduli.

Blasting Vibration Control and Signal Analysis of Adjacent Existing Deterioration Tunnels

Building a new tunnel adjacent to an existing tunnel has become a common means of transformation in engineering. Existing tunnels are prone to some deterioration, such as cavities and cracks under long-term traffic load. This kind of deterioration tunnel is prone to collapsing under the action of blasting. Therefore, the vibration caused by blasting should be strictly controlled. Based on the reconstruction project of the Bo Jiling Tunnel, this paper puts forward the method of mechanical cutting in a central position combined with an ordinary detonator to reduce blasting vibrations. ANSYS/LS-DYNA version 19.2, was used to simulate two conditions of full-section blasting and central mechanical cutting blasting. By comparing the stress and velocity of the existing tunnel, the damping effect of mechanical cutting blasting is analyzed. Via field experiments, the superiority of the mechanical cutting method in reducing blasting vibration is further discussed. At the same time, the relationship between the main vibration frequency and the peak velocity of the existing deterioration tunnel is obtained by wavelet packet analysis of the field experimental data. The frequency band energy distribution in each direction of vibration velocity is also obtained. The results show that the central mechanical cutting increases the blasting free surface, and the mechanical cutting method reduces the vibration velocity by 36.3%. The third frequency band (31.25~46.875 Hz) is the most concentrated, which is the dominant frequency band of the signal. The novelty of this paper is to propose mechanical cutting of the central hole instead of traditional blasting for existing deterioration tunnels. The feasibility of this method is verified by numerical simulation and field tests. The relationship between peak vibration velocity, band energy, and tunnel frequency is clarified, which can better control blasting vibration and ensure the safety of existing deterioration tunnels.

THE EFFECT OF ORGANIC FERTILIZERS ON THE CONTENT OF TRACE ELEMENTS IN THE GREEN MASS OF CORN

The optimal content of trace elements in green feeds is of fundamental importance for ensuring high productivity of cattle. For the production of green fodder, corn grown in fields located near livestock complexes and fertilized by by-products of animal husbandry (BPA) is used. In conditions of a high load of BPA (manure, manure runoff), the accumulation of trace elements in the green mass of corn (GMC) occurs more intensively than when using fertilizers in mineral form. Corn cultivation promotes active removal of Fe, Zn, Mn from the soil and weak removal of Cu. The accumulation of trace elements in plants grown on acidic soils fertilized by BPA is higher than when grown on neutral soils. According to the absolute content in GMC, trace elements are arranged in the following sequence: Mn>Fe>Zn>Cu. The corresponding series coincides with the sequence of decreasing concentrations of mobile forms of elements in agro-ecosystems, but differs from the sequence of changes in the indices of accumulation (IA) of trace elements. The IA values calculated on the basis of experimental data are arranged as follows: Fe>Zn>Mn>Cu. The IA of all trace elements, except Cu, in the GMC was higher than one, so corn can be classified as battery plants. IA Cu in different agro-ecosystems was below 1 and practically did not depend on the level of metabolic acidity. The weak accumulation is probably due to the low Cu content in sod-podzolic soils, the high stability of Cu complexes with organic ligands and the significant content of nitrogen available to plants in soils fertilized by BPA.

Efficient Estimates of Surface Diffusion Parameters for Spatio-Temporally Resolved Virus Replication Dynamics.

Advanced methods of treatment are needed to fight the threats of virus-transmitted diseases and pandemics. Often, they are based on an improved biophysical understanding of virus replication strategies and processes in their host cells. For instance, an essential component of the replication of the hepatitis C virus (HCV) proceeds under the influence of nonstructural HCV proteins (NSPs) that are anchored to the endoplasmatic reticulum (ER), such as the NS5A protein. The diffusion of NSPs has been studied by in vitro fluorescence recovery after photobleaching (FRAP) experiments. The diffusive evolution of the concentration field of NSPs on the ER can be described by means of surface partial differential equations (sufPDEs). Previous work estimated the diffusion coefficient of the NS5A protein by minimizing the discrepancy between an extended set of sufPDE simulations and experimental FRAP time-series data. Here, we provide a scaling analysis of the sufPDEs that describe the diffusive evolution of the concentration field of NSPs on the ER. This analysis provides an estimate of the diffusion coefficient that is based only on the ratio of the membrane surface area in the FRAP region to its contour length. The quality of this estimate is explored by a comparison to numerical solutions of the sufPDE for a flat geometry and for ten different 3D embedded 2D ER grids that are derived from fluorescence z-stack data of the ER. Finally, we apply the new data analysis to the experimental FRAP time-series data analyzed in our previous paper, and we discuss the opportunities of the new approach.

Study on the effect of humidity on formaldehyde emission parameters of wood-based panels

Wood-based panels are a significant source of indoor volatile organic compounds (VOCs), crucial for managing indoor pollution. The emissions from wood-based panels are influenced by both intrinsic factors, including their composition and manufacturing process, and extrinsic environmental conditions, such as temperature and humidity. In this paper, the emission of formaldehyde in plywood was studied under different temperature conditions (15°C, 23°C, 32°C and 37°C) and different humidity conditions (40%, 60% and 80%) by environmental chamber method. To understand the influence of relative humidity on the key parameters of panels from a mechanism perspective, this paper investigates the influence of relative humidity on the initial emittable concentration of formaldehyde, based on the hydrolysis reaction of urea-formaldehyde resin. Concurrently, the molecular dynamics and porous panels model were amalgamated to assess the influence of relative humidity on both the partition and diffusion coefficient. To simulate the emission process in a real environment, a control equation that allows for variations in both temperature and humidity is needed. This paper employs the method of variable separation to derive a simplified control equation. Subsequently, experimental data was utilized to validate the derived equation’s effectiveness within the practical indoor environmental range.

Radiological Measurement of Sella Turcica Dimensions in Different Skeletal Patterns in Romanian Patients

(1) Background: The purpose of this study was to evaluate the morphology and linear dimensions of sella turcica in Romanian participants from all three skeletal classes to see whether there were any differences. (2) Method: We examined 90 lateral cephalometric radiographs of patients aged 12 and older and divided them into skeletal classes I, II, and III (30 participants in each). Sella turcica linear measurements such as length, depth, and anteroposterior diameter were measured and studied. To see the nature of our data, Q–Q plots tests were performed. By examining these tests performed for each variable belonging to a particular class, it can be noted that the points are fairly well distributed along some lines, meaning that the data are normally distributed. An Anova test with Bonferroni correction was used to compare the mean values of the examined parameters between the classes. Also, to observe the correlation between our experimental data, the Pearson correlation coefficient was calculated. (3) Results: In all three skeletal classes, the average length of the sella was 8.98 mm ± 1.470, the average depth was 7.99 mm ± 1.081, and the average diameter was 10.29 mm ± 1.267. For all examined linear dimensions, there was a statistically significant difference between class I and class III subjects and between class II and class III subjects (p < 0.001). The morphology of sella turcica was found to be normal in 51.1% of instances, representing the majority across all skeletal classes. In the Romanian population, sella turcica has shown a significant amount of variation. Class III subjects had larger sella dimensions, whereas class II subjects had smaller values. (4) Conclusions: The measurements and morphology of the sella analysed in the present research can serve as standards for subsequent research concerning the sella turcica region in individuals from Romania.

Time series-based prediction of antibiotic degradation via photocatalysis using ensemble gradient boosting.

This study aims to evaluate the effectiveness of the laboratory-made catalyst Ni2P-ZrO2 (NPZ) in the degradation of an antibiotic in an aqueous suspension when exposed to ultraviolet (UV) light. The degradation of amoxicillin (AMX) was predicted using time series forecasting through the ensemble gradient boosting model. The degradation experiments were conducted utilizing two distinct photocatalyst compositions of Nickel phosphide-zirconium dioxide (NPZ) in the proportions of 1:9 and 2:8. The most effective experimental results were obtained using a natural pH, a catalyst concentration of 0.20 g/L and reaction duration of 0.5 h after testing the different catalysts. Experimental data were used for training, validating and confirming time series predictions. The use of ensemble technique highly affected the experimental findings. The model's performance was quite satisfactory in terms of correlation coefficient (94.00%), normalized mean square error (0.01) and mean square root error (0.0911) which significantly contributed to the model's accuracy. All input variables, such as pH, catalyst dose and irradiation time, had a significant impact on the degrading efficacy. The study has demonstrated that time series forecasting can be used for predicting the degradation process precisely.

A MODEL FOR SURFACE NANORELIEF FORMATION UNDER GAS CLUSTER ION IRRADIATION

A model for the evolution of surface nanorelief under irradiation with gas cluster ions is proposed. The model is based on consideration of individual collisions of clusters with a surface. The amount of substance sputtered from the collision area and the e ciency of its redeposition onto other surface elements are determined. The performance of the model is shown when compared with experimental data. The stages of smoothing of a harmonic relief are studied. A new measure of the e ectiveness of surface smoothing is proposed.

Numerical investigation of unsteady heat transfer from a vertical helical coil

A comprehensive investigation was conducted utilizing three-dimensional transient Computational Fluid Dynamics (CFD) to analyze conjugate heat transfer, with a specific focus on determining the cooling time of a hot vertical helical coil. The heat transfer characteristics of the helical coil are assessed numerically with respect to parameters within certain ranges such as Rayleigh number 104≤Ra≤108, surface emissivity 0≤ε≤1, and geometrical parameters of the coil, such as coil-to-rod diameter ratio 8≤D/d≤24, and pitch-to-rod diameter ratio 3≤p/d≤7.5. Furthermore, a lumped heat capacitance model is also proposed, which significantly reduces computational time while maintaining accuracy by utilizing the steady-state correlation of Nusselt number vs. Rayleigh number (Nu vs. Ra) and other pertinent geometrical parameters of the coil. Comparative analysis between the lumped heat capacitance model and transient simulations reveals a remarkable correspondence, with deviations not exceeding 2%. Validation of the computational model is conducted against experimental data, showing good agreement. Results indicate that coils with higher pitch-to-rod diameter ratio (p/d) and coil-to-rod diameter ratio (D/d) dissipate heat more quickly, while higher initial Rayleigh numbers lead to faster cooling rates. Temperature distribution and Nusselt number variations during the cooling process are analyzed and compared between steady and unsteady behavior. The study provides valuable insights into the cooling behavior of helical coils, crucial for industrial applications requiring efficient heat transfer.

Research on the Effect of Spray Ventilation Cooling Systems on Human Physiological and Psychological Indexes

Hot working environment not only affects work efficiency, but also poses a potential threat to the physical and mental health of staff. The current common method for dealing with high temperatures is spray-only or ventilation-only. To investigate the impact of different spray and ventilation modes on indoor high-temperature environments, this study examined their effects on indoor environmental parameters, average skin temperature, and psychological indicators. By establishing an experimental platform for high-temperature thermal environments, a spray ventilation cooling system was implemented, and its cooling efficacy in the indoor thermal environment was analyzed. The environmental classification of the high-temperature working environment under experimental conditions is provided based on the experimental data. A comparison and analysis of environmental parameters and physiological and psychological indicators between moderate and high-temperature environments were conducted. The combination of spray and ventilation modes resulted in a 5.3 °C reduction in air temperature, a 24.1% increase in average relative humidity, and a 3.3 °C reduction in average Wet-bulb Globe Temperature (WBGT). The cooling effect was increased by 2.3 °C and the average relative humidity was increased by 10.8% compared to spray-only and ventilation-only modes. In spray and ventilation mode, when the spray volume is increased by 15 mL/min, the air temperature is reduced by 8.2 °C, the average relative humidity is increased by 31.9%, and the average WBGT is reduced by 5.1 °C. This study has guiding significance for finding a reasonable cooling scheme to cope with indoor high-temperature environments.

Simultaneous effect of pH, deposition time, deposition potential, and step potential on the stripping peak current of lead and cadmium by response surface methodology

In this paper, a simple voltammetric method has been reported for the lead, and cadmium determination using platinum nanoflowers modified glassy carbon electrode (PtNFs/GCE). The effects of pH, deposition time, deposition potential, step potential were investigated on the stripping peak current of lead, and cadmium based on response surface methodology (RSM). The results of RSM analysis and analysis of variance (ANOVA) have shown that the experimental data could be well described by quadratic regression equations with determination coefficients (R2) of 0.935, and 0.972 for the stripping peak current of lead, and cadmium, respectively. Results of the statistical analysis showed that the fit of the model was good in all cases. The maximum stripping peak current of the lead, and cadmium of 5.54µA, and 2.81µA, respectively were obtained at the optimum levels of process variables (pH (4.72), deposition potential (-1.14V), deposition time (120s), step potential (7mV)). Testing the model to analyze lead, and cadmium on the PtNFs/GC electrode using differential pulse anodic stripping voltammetry (DPASV) and obtained with the stripping peak current of the lead, and cadmium of 5.43µA, and 2.75 µA, respectively.

Dynamics of Analog Switching Behavior in Thin Polycrystalline Barium Titanate

AbstractEngineering of interfaces and point defects in ferroelectric memristors is an efficient way for manipulating the resistive switching effects of mixed ionic‐electronic nature. However, an interplay between the defects, interfacial properties, and ferroelectric polarization as well as their influence on the resistance state tuning are yet to be revealed. By considering the memristive device built on a thin polycrystalline BaTiO3 film, a drift‐diffusion model of non‐stationary processes is developed. The model is based on Poisson and continuity equations solved numerically and accounts for various transport mechanisms for electrons, holes, and oxygen vacancies. Comparing simulated resistive effects with experimental data taken in a wide temperature range, it is shown that an appearance of the analog resistive switching cannot be explained solely by oxygen‐ionic transport. Investigated switching dynamics claims the oxygen vacancies redistribution to be responsible for the analog character of the switching at the prevalence of the electron hopping transport. Crucially, the required dynamics of the vacancies redistribution is achieved only in a narrow range of their mobility. These results can be used in designing the ferroelectric memristors for nonvolatile multilevel memory devices satisfying the requirements that arise at the stages of their integration into neuromorphic architectures.

An optimal sparse sensing approach for scanning point selection and response reconstruction in full-field structural vibration testing

Non-contact vibration measurements, such as 3D scanning laser Doppler vibrometry (3D SLDV), are becoming more prevalent in testing next-generation lightweight aerospace structures. This approach reduces the impact of attached sensors and improves measurement reliability. Acquiring precise measurement data for the whole area is feasible, albeit it would demand a considerable amount of time and storage space for testing. The concept of compressed sensing has been recently approved as an effective way to exploit signal sparsity and achieve full response reconstruction with very few measurements. The objective of this work is to enhance the efficiency of non-contact vibration testing by utilizing the state-of-the-art compressive sensing approach. In contrast to conventional sensor placement methods that rely on effective independence, modal kinetic energy, or modal assurance criterion matrix as targets, this paper proposes a novel sensor placement methodology from the perspective of dynamic response reconstruction. The scanning points are chosen with a minimal number to reduce testing time and are well-placed such that full-field vibration responses of the test structure can be reconstructed accurately. This allows for the spatially-detailed vibration responses to be obtained efficiently and accurately with optimal sparse sensing placement and effective response reconstruction through ℓ1 algorithm. Two case studies will be presented in this work to demonstrate and validate the methodology. The first case study is focused on a simplified cantilever beam using the numerical data from the FE analysis to demonstrate the methodology. The second case study is focused on using 3D SLDV experimental testing data from a full-scale industrial fan blade. Based on the results, it is evident that the proposed approach can significantly decrease the scanning points required for a full-field dynamic response reconstruction during full-field vibration testing.

MA'AT Analysis: Unbiased Multi-State Conformational Modeling of Exocyclic Hydroxymethyl Group Conformation in Methyl Aldohexopyranosides.

MA'AT analysis (J. Chem. Inf. Model. 2022, 62, 3135-3141) has been applied to model exocyclic hydroxymethyl group conformation in methyl β-D-glucopyranoside (βGlcOMe), methyl β-D-galactopyranoside (βGalOMe), and methyl β-D-mannopyranoside (βManOMe) in an unbiased manner. Using up to eight NMR J-couplings sensitive to rotation about the C5-C6 bond (torsion angle ω), two-state models of ω were obtained that are qualitatively consistent with the relative populations of the gg, gt, and tg rotamers reported previously. MA'AT analysis gave consistent unbiased gt ⇌ tg models of ω in βGalOMe, with gt more populated than tg and mean values of ω for each population similar to those obtained from aqueous 1-μs MD simulation. Using different combinations of J-couplings had little effect on the βGalOMe model in terms of the mean values of ω and circular standard deviations (CSDs). In contrast, MA'AT analysis of ω in βGlcOMe and βManOMe produced more than one two-state model independent of the ensemble of J-values used in the analyses. These models were characterized by gg ⇌ gt conformer exchange as expected, but the mean values of ω in both conformers varied significantly in the different fits, especially for the gg rotamer. Constrained (biased) MA'AT analyses in which only staggered geometries about ω were allowed gave RMSDs slightly larger than those obtained from the unbiased fits, precluding an assignment of an unbiased model. It is unclear why MA'AT analysis gives consistent and predictable unbiased models of ω in βGalOMe but not in βGlcOMe and βManOMe. One possibility is that the distribution of ω in one or both of the gg and gt conformers in the latter does not conform to a von Mises function (i.e., is not Gaussian-like), but rather to a broad and/or flat distribution that cannot be fit by the current version of MA'AT. Nevertheless, the results of this study provide new evidence of the ability of MA'AT analysis to treat multi-state conformational exchange using only experimental NMR data, extending recent MA'AT applications to furanosyl ring pseudorotation (Biochemistry 2022, 61, 239-251).

Hyper ensembled extreme gradient boosting techniques via novel triple algorithms to appraise the hardened properties of modern ternary admixed concrete

Mechanical parameters used in many design codes can be achieved by expensive and time-consuming experiments or by non-destructive approaches such as estimative modelling. This investigation proposed Extreme Gradient Boost (XGB) for estimating the slump (SL) and compressive strength (CS) of high-performance concrete (HPC). In addition, to bring the results of the models closer to the experimental data and increase the accuracy, algorithms were combined with the model, including Sunflower Optimizer (SFO) and Jellyfish Search Optimize (JSO). The relevant models have been examined in three frameworks: individual, hybrid, and ensemble-hybrid. For this purpose, several evaluators were provided to determine the errors, compare, and accuracy of the presented models. The XGFJ model has demonstrated exceptional performance, achieving remarkable results in terms of RMSE (Root Mean Square Error) and R2 (R-squared) values. Specifically, it has attained an exceptionally small RMSE value of 1.785 for CS and 5.183 for SL, indicating the model’s high precision in predicting these parameters. Additionally, it has achieved the biggest R2 values of 0.9960 for CS and 0.9949 for SL. Additionally, it is worth noting that the XGSF model closely matches the performance of the ensemble form of XGFJ, as evident from its R2 values of 0.9956 for CS and 0.9934 for SL. Based on the study, it was observed that using machine learning to anticipate the mechanical characteristics of concrete is valuable and efficient and can be considered an alternative method instead of time-consuming laboratory methods. This research addresses challenges in predicting HPC properties fueled by the need to overcome drawbacks in traditional methods. Costly and time-intensive laboratory experiments prompted the exploration of alternatives, leading to the proposal of XGB combined with optimization algorithms (SFO and JSO). The study aims to enhance prediction accuracy while tackling broader concerns such as construction costs, material efficiency, and environmental impact. The resource-intensive nature of conventional methods, along with inaccuracies due to material variations, serves as a primary challenge. The proposed resolution advocates for a paradigm shift to machine learning, exemplified by the XGFJ model, showcasing exceptional precision and efficiency in predicting HPC properties.

The milling parameters of mechanical parts are optimized by NC machining technology

In the field of mechanical manufacturing, CNC machining technology plays an important role in improving the precision and efficiency of part processing. However, how to further improve the effect of NC machining by optimizing milling parameters is still a key problem. The aim of this study is to optimize CNC milling parameters through systematic research and experiments to improve the machining efficiency and quality of mechanical parts. By adjusting key parameters such as tool speed, feed speed, and removal rate cutting depth, the influence of these parameters on the milling process was systematically studied using advanced CNC machining equipment. Through the collection and analysis of experimental data, the mathematical model is established, and the optimization algorithm is applied to find the best combination of milling parameters. The experimental results show that under the optimal combination of parameters, the surface quality of parts can be significantly improved, the machining time can be reduced, and the tool wear can be reduced. This research successfully realizes the optimization of milling parameters of mechanical parts by CNC machining technology and provides an effective solution for improving machining efficiency and reducing costs. This not only has guiding significance for the application of CNC machining technology but also has important promotion value in actual production.