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Measurements of the Casimir–Lifshitz (dispersion) forces at distances below 50 nm are difficult due to the snap-in effect, and precision is poor due to increasing relative uncertainty in the distance. In this paper, a method of adhered cantilever that avoids the loss of stability is used to measure the interaction energy between Si and Ru surfaces in direct contact. Background capillary and short-range interactions are strongly suppressed because surface roughness significantly deviates from a normal distribution. Electrostatic interaction is not fully compensated, but the potential is limited to a value less than 29.8mV. Adhesion energy is measured directly, and the average equilibrium distance is predicted theoretically. The ability to achieve high precision in this type of experiment is demonstrated.

Cr coatings were deposited on both the polished and rough substrate using DC magnetron sputtering. The influence of substrate surface roughness on the morphology, crystalline phase and mechanical properties of the coatings were analyzed. The results showed that surface roughness had a significant effect on the surface morphology of the coating, as the coating inherited the surface morphology of the substrate. Surface roughness before and after coating for the polished and rough surfaces were 0.054–0.046 and 0.107–0.563$\mu$m, respectively. The hardness and Young’s modulus of the coating deposited on the polished surface were 5.5 and 188.6GPa, respectively, while the mechanical properties of the coating deposited on the rough surface are about the same. The results showed that the adhesion of the coating can be improved by appropriately increasing the surface roughness.

This paper uses fractal modeling techniques to effectively depict the roughness characteristics of the rails examined in this study by means of both the structure function and the Weierstrass–Mandelbrot function to capture the intricate nature of the rail roughness. Experimental analysis of the roughness of the railway rails from Faurei Railway Testing Center (RTC Faurei) in Romania proves that the roughness height exhibits distinct mathematical fractal characteristics. The study evaluated and compared 41 classical statistical parameters derived from roughness measurements with simulated fractal parameters. The classical roughness parameters, including the Autocorrelation function, Amplitude Density Function, as well as Bearing Area Curves, and rail acoustic roughness, were determined from a profile obtained using the Weierstrass function and compared to the measured ones, revealing a noticeable congruence between the generated charts. The research findings indicate a strong convergence between experimental measurements and simulated profiles, with most parameters falling within a 10% relative error range. This aspect highlights the approach of fractal potent in assessing rail roughness behavior. Consequently, the simulated parameters could potentially analyze rail roughness quality to maintain and track grinding and mitigate the rolling noise.

This study investigates the performance of deep cryogenically treated brass (CuZn25Al5) electrodes in the die-sink EDM process. Two different cryogenic treatment durations, 6 h and 12 h, were applied to 8 mm diameter electrodes, and their effects were compared to untreated electrodes. The machining tests were conducted under moderate and aggressive conditions. In the machining tests, the 6-h cryo-treated electrode exhibited a 16.8% increase in material removal rate (MRR) under moderate conditions and a 19.7% increase under aggressive conditions compared to the reference electrode. The 12-h cryo-treated electrode showed similar MRR values to the reference electrode but improved tool wear resistance by 9.4% under moderate conditions. The kerf angle was minimized, indicating better hole verticality, in the 6-h cryo-treated electrode group. The improvement in machining performance was attributed to the enhancement in electrical conductivity of the electrodes, which increased by 28% for the 6-h cryo-treated electrode and 20% for the 12-h cryo-treated electrode. X-ray diffraction (XRD) analysis revealed shifts in peak positions and possible phase transformations due to cryogenic treatment. Surface roughness measurements showed improved surface conditions in the cryo-treated electrodes under aggressive conditions. The results indicate that cryogenic treatment enhances MRR, reduces tool wear, and improves surface quality in die-sink EDM. These improvements are attributed to increased electrical conductivity and changes in the internal structure of the brass electrode.

The necessity for innovative biomaterials has been growing recently due to the rising cost of materials for intricate biomedical equipment. An important tactic to improve critical attributes like hemocompatibility, osseointegration potential, corrosion resistance, and antibacterial capabilities is surface modification. In this paper, an investigation has been made in the field of laser surface modification and the complex interactions between laser parameters and output performance metrics, such as contact angle and surface roughness. Surface modification by laser has been successful and, in this research, the laser parameters such as laser energy (Watts), standoff distance(mm), and frequency (kHz) along with dimple distance on the surface ($\mu$m) were considered on the output performance namely surface roughness in “$\mu$m” and contact angle in “degree”. The experiment has been carried out using the L16 orthogonal array to interpret the complex correlations between these factors and the resulting surface features. Non-dominated sorting genetic algorithm II (NSGA-II) has successfully navigated the complex parameter space and found the optimal combinations that yield the intended outcomes. The results show how important dimple distance and laser frequency are in creating hydrophobic surfaces, as well as how much of an impact they have on surface properties. It has been discovered that higher frequencies and longer standoff distances specifically reduce surface roughness, which is a crucial component in ensuring enhanced biomaterial performance. The result shows that the dimple distance and frequency of the laser have a significant effect on the development of hydrophobic surfaces. Moreover, high frequency and more standoff distance reduce the surface roughness. The predicted combination of laser parameters as per the NSGA-II is 102.91$\mu$m, 33.35W, 223.12mm, 50.01kHz, and gives a surface roughness of 0.86$\mu$m and contact angle of 158.83^∘. In essence, this study not only sheds light on the intricate dynamics governing laser-based surface modification but also paves the way for the design and development of advanced biomaterials with tailored surface properties, poised to revolutionize biomedical applications.

Monel K 400 is a potential superalloy that is used in heat exchanger piping, process vessels, gasoline, and portable water owing to superior mechanical properties at zero temperature. Square holes are needed in the high-temperature application components and are difficult to machine using conventional methods. Hence, in this study, the square holes have been fabricated on Monel K 400 superalloys with varying pulse duration, current, pulse interval, and servo voltage and its influences on Recast Layer Thickness (RLT), Corner Radius (CR), and Surface Roughness (SR) have been analyzed. Experiments are planned on using Taguchi design and the responses are analyzed with mean surface plots. The overall analysis found that the SiC powder mixed in EDM oil has a larger improvement % than that of CFRP and ZrSiO₄ powder. The performance of SiC powder mixed in EDM oils increased to 62 % for RLT, 32 % for CR, and 51 % for SR. The current and pulse duration are revealed to be the most significant parameters using Analysis of Variance (ANOVA). Quadratic and Adaptive Neuro-Fuzzy Inference System (ANFIS) models are developed for prediction. The Mean Absolute Percentage Error (MAPE), coefficient of determination (R), and Root Mean Square Error (RMSE) are calculated for evaluating the models and it is discovered that both models have low RMSE, MAPE, and high R. Finally, the multiple response decision making based on Taguchi based Data Envelopment Analysis based Ranking (DEAR) methodology is an improvement of 28 % for RLT, a 16 % for CR, and a 21 % for SR.

A polymer valve seat is an essential component of ball valves. However, a gas pipeline valve leakage during the gas or oil transportation process causes major issues. Therefore, the fine finishing of the polymer valve seat is important. This work describes the effect of the magnetorheological finishing (MRF) process on the surface characteristics of the polymer valve seat. To achieve a finely finished polymer valve seat surface, the best process parametric combination of current, workpiece rotational, and tool rotational speed are obtained using the response surface methodology. The optimum process parameters are further used to enhance surface characteristics (including surface roughness, microhardness, and surface morphology) through fine finishing. Using the optimum process conditions, the surface roughness was reduced to 110nm from 570nm in 140min of finishing on a 4878mm² surface area of the polymer valve seat. The circularity of the valve seat surface’s dimensional correctness is investigated further. The current MRF process can fine-finish the polymer valve seat surface consistently, resulting in improved surface characteristics and functional performance.

This paper highlights the surface roughness optimization of a specific material, Al 3003, which has been subjected to the non-equal channel angular pressing (NECAP) process. Considering spindle speed, feed rate, and depth of cut as input variables and surface roughness as an output variable, experiments have been conducted based on the L27 orthogonal array of the Taguchi method. Four prediction models, namely exponential and response surface methodology (RSM) as mathematical models, and artificial neural networks (ANNs) prediction models with different training algorithms (Bayesian Regularization (BR) and Levenberg–Marquardt (LM)), are proposed. Applying effectiveness and performance criteria, the prediction accuracy of the exponential model (90.35%), RSM (93.07%), BR (97.83%), and LM (97.54%) shows that all proposed prediction models are efficient enough. The ANN model trained with BR is found to be the best fit for predicting surface roughness. In order to optimize surface roughness, a newly introduced optimization method called the Intelligible-in-time Logics Algorithm (ILA) is employed. High spindle speed (1000rev/min), low feed rate (100mm/min) and depth of cut (0.5mm) have been the optimum cutting parameter combinations to obtain minimum surface roughness (0.4956$\mu$m). The results have been verified by confirmation tests and Particle Swarm Optimization (PSO) method. ILA and PSO predict the same optimum parameter combinations and minimum surface roughness, while ILA performs optimization in less time (114.4s), about 3.5 times faster than PSO. The paper’s findings strongly advocate the application of ILA in machining data optimization.

The plasma electrolytic polishing (PEP) process on Q235 low-carbon steel anode in $({\text{NH}}_4{)}_2$SO₄ electrolyte was investigated, and its surface properties under different PEP conditions were evaluated. The surface roughness of PEP samples under different electrolyte concentrations, initial roughness, voltages and treating times were measured. The surface morphologies and compositions of typical PEP samples were analyzed, and their wettability and surface free energy under different polishing times were evaluated. It was found that the near-surface temperature of the steel sample raised quickly with increasing the voltage, and then remained at about 100°C after 200V, which is beneficial to keep the microstructure and mechanical properties of Q235 low-carbon steel. Under the parameters of 3.0wt.% $({\text{NH}}_4{)}_2$SO₄ aqueous solution and applied voltage of 200V, the 8min PEP treatment could reduce the surface roughness of Q235 low-carbon steel from 2.100$\mu$m to 0.437$\mu$m. In addition, the polishing efficiency was the highest in the initial PEP stage, meanwhile, it also increased with the increase of initial roughness of the sample. After the PEP treatment, the contact angle of water on low-carbon steel decreased, and its surface free energy was slightly reduced. Moreover, the thickness of natural oxide film on Q235 low-carbon steel was reduced by about 30% after 8 min polishing treatment.

Aluminum alloys are widely used in the automotive and aerospace industries due to their lower mass-to-strength ratio than other metallic alloys. Apart from their inherent properties, aluminum alloys like other metallic alloys show a significant change in their mechanical properties according to the machining parameters. The research literature on obtaining optimum mechanical properties of aluminum alloys that undergo machining is very limited. Moreover, the combined effect of several parameters on the machinability of aluminum alloys has not yet been explored. In this paper, the effect of three machining parameters (Depth of Cut (DoC)), feed rate (FR), and cutting speed (CS) on the subsurface damage and fatigue life of aerospace-grade aluminum alloy (Al-6082-T6) is observed. Samples are prepared using a full fractional approach to effectively capture the effect of all input parameters. Thereafter, samples were subjected to surface roughness, micro-hardness, and fatigue life tests. Results of surface roughness and micro-hardness tests are compared with fatigue life. The general linear model was employed to capture the percentage effect of each input parameter on the output parameters. The results showed that DoC was the main contributing factor that caused subsurface damage, while surface roughness and fatigue life were mainly affected by FR and CS. Optical microscope images showed a white layer formation that had higher hardness than the base metal. Overall, this research work proposes the input parameters that can be used to achieve minimum surface damage and fatigue life.

This paper aims to conduct an economic and environmental machining procedure (dry condition) during face milling of the Polyoxymethylene Co-polymer. In this context, an experimental study based on Taguchi’s design was conducted to develop empirical models using Response Surface Methodology (RSM) on one hand and Support Vector Machine (SVM) for regression on the other hand. The ANalysis of VAriance (ANOVA) is conducted to determine the contribution and the significance of each cutting parameter on the surface quality and productivity (MRR). The obtained models are being compared to determine the most efficient approach. The last part is to find the optimum cutting combination by using Genetic Algorithm (GA) optimization based on SVM models, whether to minimize surface roughness Ra, or for composite objective to improve the quality and to increase the productivity. The results show that feed per tooth (fz) is the most affecting parameter on Ra followed by depth of cut (ap) and then the cutting velocity (Vc). SVM was more robust than RSM with less deviation error.

Titanium is a highly operational alloy for use in dental implants. Surface inertness of the titanium alloy surfaces reduces osseointegrate, raising concerns about implant loosening. Hydroxyapatite (HA)-rich porous titanium surfaces with moderated surface roughness have better osteoconductivity. This study focused on the surface alteration of Ti6Al4V via hydroxyapatite mixed electric discharge-assisted centerless turning (HA-mixed-EDCLT) to find the optimum surface. The experiments were planned based on a four-factor, three-level Box-Behnken design. HA-powder concentration (Cp), revolutions per minute (RPM), duty factor and impulse current (IP) were the input parameters. The Ra value of machined surfaces ranged from 1.12$\mu$m to 1.63$\mu$m, which is in the bone growth supportive implants range. The average hardness reached 415.8–962.7HV, where untreated surfaces’ hardness was $340\pm 6$HV. Porous, hydrophilic coating with a high Ca, P, and O content deposited on the implant surfaces supports the biocompatibility of implants. Analysis of the elements and compounds shows that the machined surface layer is rich with Ca₃(PO${}_4{)}_2$ and TiO${}_{2,}$which improves the bioactivity of the alloy.

Polymer nanocomposite is commonly used to develop structural components of space, aircraft, biomedical, sensor, automobile, and battery sector applications. It remarkably substitutes the heavyweight metallic and nonmetallic engineering materials. The machining principles of polymer nanocomposites are intensely different and complex from traditional metals and alloys. The nonhomogeneity, abrasive, and anisotropic nature differs its machining aspect from conventional metallic materials. This investigation aims to execute the CNC drilling of modified nanocomposite using Graphene–carbon (G-C) @ epoxy matrix. The process constraints, namely, cutting speed (S), feed (F), and wt.% of graphene oxide (GO) vary up to three levels and are designed according to the response surface methodology (RSM) array. The nonlinear model is created to predict surface roughness (Ra) and delamination (Fd) on regression analysis. It has been found that the average error for Ra is 0.94% and for Fd it is 3.27%, which is acceptable in model predictions. The metaheuristics-based evolutionary Dragonfly algorithm (DA) evaluated the optimal parametric condition. The optimal setting prediction for the DA is observed as cutting speed (S)-37.68m/min, feed (F)-80mm/min, and wt.% of graphene oxide (GO)-1%. This algorithm demonstrates a higher application potential than the previous efforts in controlling Ra and Fd values. Both the drilling response values are found to be minimized when the cutting speed increases and the feed decreases. The best fitness value for the DA is 1.626 for surface roughness and 5.086 for delamination. This study agreed with the prediction model’s outcomes and the process parameters’ optimal condition. The defects generated during the sample drilling, such as fiber pull out, uncut/burr, and fiber breakage, were examined using FE-SEM analysis. The optimal findings of the DA module significantly controlled the damages during machining.

The LM24–7.5ZrB₂–2.5FA hybrid composite presents machining difficulties as it has excellent durability and low-temperature conductivity, making it challenging to machine using standard methods. In spite of this aspect, Electrical Discharge Machining (EDM) is well-suited for machined materials made of aluminum alloys, including LM24–7.5ZrB₂–2.5FA. LM24 aluminum alloy-based hybrid composite prepared using 7.5wt% of zirconium diboride (ZrB₂) and 2.5wt% of Fly Ash (FA) by stir casting route. This study explores the machinability of the hybrid composite and the primary goal is to establish a connection between the process variable’s key efficiency indicators, which include Material Removal Rate (MRR), Surface Roughness (SR), and Tool Wear Rate (TWR), which collectively reflect dimensional accuracy and machining efficiency. The experimentation involves three adjustable process parameters, namely pulse current (I), pulse-on time ($T_{\text{on}}$), and pulse-off time ($T_{\text{off}}$). These variables are subjected to variation, and a total of 20 experimental runs are set up utilizing the Response Surface Methodology (RSM) and a full-factorial Central Composite Design (CCD). Analysis of Variance (ANOVA) is employed to determine which operating variables substantially influence performance characteristics. The analysis reveals that current has a strong influence on MRR, SR, and TWR by 49.5%, 37.7%, and 36.1%, respectively, followed by pulse-on time and pulse-off time. The developed quadratic regression models for MRR, SR, and TWR demonstrated good performance in predicting the responses, as evidenced by the $R^2$ values of 0.9030, 0.9890, and 0.9302 obtained from ANOVA results.

Super-duplex stainless steel (SDSS) 2507 is well known for its complex microstructure and alloying components, which contribute to its exceptional mechanical qualities and resistance to corrosion. Nevertheless, these features provide considerable difficulties during machining. In order to address these challenges and advance sustainability, this work presents a novel machining technique that combines minimal quantity lubrication (MQL) with surface-textured tools. Two different kinds of texture tools were examined: one with linear grooves and the other with a hybrid texture that combined both circular pits and holes. A CNC center lathe machine operating under MQL conditions was used for the experiments. It had two distinct cooling conditions, different tool textures, and cutting parameters like depth of cut, feed rate, and speed. Cutting speed, followed by feed rate, was the most important factor affecting cutting force and tool flank wear, according to analysis of variance (ANOVA). Comparing the hybrid-textured tool under MQL to dry machining conditions, the cutting force was reduced by 40%. A cutting speed of 40m/min, feed rate of 0.1mm/rev, and depth of cut of 0.4mm were found to be the optimal machining parameters employing MQL and hybrid-textured tools for minimizing cutting force and tool wear, the optimal values are 119.57N and 113.875$\mu$m respectively. These findings provide notable enhancements in machinability and sustainability for SDSS 2507, demonstrating the efficiency and usefulness of the suggested approach in MQL machining.

To evaluate the comparative effects of air-turbine and electric handpieces on dental preparation, 60 premolars were categorized into four groups, each comprising 15 specimens, based on the preparation method employed: air-turbine handpiece (Group A), and electric handpiece set at rotational speeds of 200,000rpm (Group B), 50,000rpm (Group C), and 20,000rpm (Group D). Analysis revealed no statistically significant difference in the surface roughness of the preparations across the four groups ($p>0.05$). Similarly, no significant variance was observed in fracture resistance among the groups ($p>0.05$). However, a notable distinction was detected in the degree of microleakage following the aging test ($p<0.05$), with veneers prepared using the electric handpiece at 200,000rpm demonstrating the lowest average microleakage. It is noteworthy that the surface roughness of the preparations and the veneers’ fracture resistance remained unaffected by the variations in preparation method.

This research reports the development of anti-reflective films for solar cell application by employing the hot embossing technique with laser-patterned microstructures. The goal is to increase the light-trapping ability of crystalline silicon (c-Si) wafers by employing micro-textured polycarbonate films to decrease surface reflectance. A series of micron-sized rhombus patterns were first created on the titanium-grade-5 mold using a fiber laser, and then, polycarbonate sheets were hot embossed under the optimized conditions. In order to investigate the influence of the embossing temperature, pressure, and time on the average reflectance and surface roughness of the films, a parametric analysis was carried out through the Taguchi method. The most effective embossing parameters were the embossing temperature of $220^{\circ }$C, pressure of 50 kg/cm², and an 8 min embossing duration, which resulted in a significant decrease of 41.53% reflectivity. The findings in the existing study and a fuzzy logic-based multi-objective optimization approach also supported these findings, suggesting the scalability and efficiency of the process. It is evident that the proposed method could provide a more significant cost reduction in fabricating anti-reflective films with large-area applications to optoelectronics devices such as solar cells, LEDs, and optical sensors. This study opens the door to further studies about using micro-patterned films to enhance light management for other energy-efficient devices.

Abrasive water jet (AWJ) machining does not impact material quality and modern machining processes. The trials follow the Taguchi L25 orthogonal array and the workpiece material is Ti grade 5 alloy. The optimal process parameters are determined using response surface methodology and these parameters include transverse speed (TS), pressure (P), abrasive flow rate (AFR) and stand-off distance (SoD). The aim of this research is to identify, evaluate, and improve the impression of AWJ machining parameters on response variables (machining time [MT], surface roughness [SR], and hardness). Achieving this objective involves utilizing grey relational analysis in conjunction with principal component analysis. TS has the least influence on performance while AFR has the greatest. The best configuration for the lowest MT, SR, and Highest Hardness Rockwell C scale (HRC) is (AFR = 600 g/min, SoD = 4 mm, P = 20 kpsi, and quality = 5 approximately, speed 75 mm/min). Here, quality = 5 corresponds to a TS of approximately 75 mm/min and the factor AFR has the greatest impact on material machining performance, according to the analysis of variance, followed by factors P, standoff distance, and TS. The chronological sequence of influence is as follows: AFR > P > SoD > TS with contributions of 85.45%, 8.06%, 2.90%, and 2.09%, respectively. Experiments showed that the proposed methodology enhanced AWJ machining performance by 0.3665 which is represented as multi-response performance index.

A non-traditional machining method based on thermal energy, wire electrical discharge machining (WEDM) can precisely machine conductive materials. In addition to the cost of slot cuts, process parameters are important since they vary depending on the material properties. Hence, it is necessary to employ an appropriate technique to regulate the process parameters and ensure the desired quality of the slot cut. This study involved using WEDM to machine Inconel-825. The experiment used a Taguchi $L_9$ orthogonal array (OA) for maximum cutting rate and minimum surface roughness (SR). Peak current (I), spark on time ($P_{\mathrm{\text{on}}}$), and spark off time ($P_{\mathrm{\text{off}}}$), which are WEDM process parameters, were considered. Analysis of variance (ANOVA) and ANOM identified the significant contribution of cutting rate and SR in terms of WEDM parameters. A MOORA-PCA analysis was conducted to determine the optimum parameter settings. The OA results showed that the best values were at I (5A), $P_{\mathrm{\text{on}}}$ (50ms), and $P_{\mathrm{\text{off}}}$ (8ms), with an assessment value of 0.9935 and 1.0558 for linear and circular cut, respectively. The best outcomes from the MOORA-PCA optimization procedures were compared to the experimental outcome, and an improvement of 22.5643% and 17.7085% in linear and circular cuts were found, respectively. Optimizing WEDM parametrically utilizing a commercially available Inconel 825 machining technique based on MOORA-PCA MCDM is feasible.

The material composition and surface structure of dental and orthopaedic implants have important effects on integration of the implants with the surrounding bone, and at the same time, the release of the constituent elements of the implants into the surrounding tissues. It is the aim of this paper to study the degree of release of Ti in relation to the surface roughness of the implants. For this purpose, screw shaped implants were prepared with two different surface topographies; one group was left as-machined, i.e. as machine-turned surface, and the second group was blasted with 25 µm Al₂O₃ particles. The surface topography was measured with a confocal laser scanning profilometer and the surface roughness was characterized using height and spatial descriptive parameters. The as-machined surfaces had an average surface roughness (S_a) of 0.96 µm and the blasted surfaces had an S_a value of 1.16 µm. The implants were inserted into rabbit bone for 1 year. Six samples (three of each type) were prepared for PIXE analysis. The PIXE analysis was done using proton beams from a tandem type accelerator. with an energy of 2 MeV. The results show that at distances of about 2 mm from implant surface, titanium release into hard tissues was found at similar amounts for the as-machined and the blasted implants inserted in the tibia.