Search Results (13)

This study presents a multi-modal investigation into the wear behavior of bio-based epoxy composites reinforced with multi-walled carbon nanotubes (MWCNTs) at 0–0.75 wt%. A Taguchi L16 orthogonal array was employed to systematically assess the influence of MWCNT content, load (20–50 N), and sliding speed (1–2.5 m/s) on wear rate (WR), coefficient of friction (COF), and surface roughness (Ra). Statistical analysis revealed that MWCNT content contributed up to 85.35% to wear reduction, with 0.5 wt% identified as the optimal reinforcement level, achieving the lowest WR (3.1 mm³/N·m) and Ra (0.7 µm). Complementary morphological characterization via SEM and AFM confirmed microstructural improvements at optimal loading and identified degradation features (ploughing, agglomeration) at 0 wt% and 0.75 wt%. Regression models (R² > 0.95) effectively captured the nonlinear wear response, while a Random Forest model trained on GLCM-derived image features (e.g., correlation, entropy) yielded WR prediction accuracy of R² ≈ 0.93. Key image-based predictors were found to correlate strongly with measured tribological metrics, validating the integration of surface texture analysis into predictive modeling. This integrated framework combining experimental design, mathematical modeling, and image-based machine learning offers a robust pathway for designing high-performance, sustainable nanocomposites with data-driven diagnostics for wear prediction. Full article

In recent days, aluminum-based hybrid composites have garnered more interest than monolithic alloys owing to their remarkable properties, encompassing a high strength-to-weight ratio, excellent corrosion resistance, and impressive wear durability. The present study attempts to optimize the multiple wear attribute characteristics of Al6061/SiC/Al₂O₃ hybrid composites using grey and entropy-based VIKOR techniques. The composites were produced by adding equal proportions of SiC/Al₂O₃ (0–12 wt.%) ceramics through the stir-casting process, using an ultrasonication setup. Dry sliding wear experiments were executed with tribometer variants, namely reinforcement content (wt.%), load (N), sliding velocity (v), and sliding distance (SD), following L₂₇ OA. The optimal combination of process variables for achieving high GRG values from grey analysis was found to be A₃-B₃-C₃-D₃. The S/N ratios and ANOVA results for GRG indicated that RF content (wt.%) is the predominant component determining multiple outcomes, followed by sliding distance, load, and sliding velocity. The multi-order regression model formulated for the VIKOR index (Q_i) displayed high significance and more accuracy, with a variance of 0.0216 and a coefficient of determination (R²), and adjusted R² values of 99.60% and 99.14%. Subsequent morphological studies indicated that plowing, abrasion, and adhesion mechanisms are the dominant modes of wear. Full article

This study examined the impact of molybdenum disulfide (MoS₂) addition as a filler in epoxy composites reinforced with chopped basalt fibers (CBF), maintaining the basalt fiber content at a constant 40 wt. %. The investigation focused on physical, microstructural, mechanical, and sliding-wear properties. Testing revealed that tensile, impact, compressive, and flexural strengths improved with MoS₂ content from 0 to 8 wt. %. However, at 12 wt. % loading, these properties declined due to uneven dispersion and particle agglomeration. An increase in hardness was observed with rising MoS₂ content, with a maximum value of 98 HV at 16 wt. %. Wear testing was conducted using a Taguchi L16 orthogonal array, evaluating the effects of multiple parameters. The results indicated that MoS₂ content had the most significant influence on wear rate (WR), followed by applied load (P) and sliding distance (D), while sliding velocity (V) had minimal impact on specific wear rate (SWR) and coefficient of friction (COF). Scanning electron microscopy (SEM) was used to analyze wear mechanisms, and analysis of variance (ANOVA) confirmed the optimal conditions. Full article

In this study, the AZ31 hybrid composites reinforced with boron carbide (B₄C) and graphene nano-platelets (GNPs) are prepared by the stir casting method. The main aim of the study is to study the effect of various wear parameters (reinforcement percentage (R), applied load (L), sliding distance (D), and velocity (V)) on the wear characteristics (wear rate (WR)) of the AZ91/B₄C/GNP composites. Experiments are designed using the Taguchi technique, and it was determined that load (L) is the most significant parameter affecting WR, followed by D, R, and V. The wear mechanisms under conditions of maximum and minimum wear rates are examined using SEM analysis of the worn-out surfaces of the specimens. From the result analysis on the WR, the ideal conditions for achieving the lowest WR are R = 4 wt.%, L = 15 N, V = 3 m/s, and D = 500 m. Machine learning (ML) models, including linear regression (LR), polynomial regression (PR), random forest (RF), and Gaussian process regression (GPR), are implemented to develop a reliable prediction model that forecasts output responses in accordance with input variables. A total of 90% of the experimental data points were used to train and 10% to evaluate the models. The PR model exceeded the accuracy of other models in predicting WR, with R² = 0.953, MSE = 0.011, RMSE = 0.103, and COF with R² = 0.937, MSE = 0.013, and RMSE = 0.114, respectively. Full article

An optimisation model for small datasets was applied to thixocasted/compocasted composites and hybrid composites with hypereutectic Al-18Si base alloys. Composites were produced with the addition of Al₂O₃ (36 µm/25 nm) or SiC (40 µm) particles. Based on the design of experiment, tribological tests were performed on the tribometer with block-on-disc contact geometry for normal loads of 100 and 200 N, a sliding speed of 0.5 m/s, and a sliding distance of 1000 m. For the prediction of the tribological behaviour of composites, artificial neural networks (ANNs) were used. Three inputs were considered for ANN training: type of reinforcement (base alloy, Al₂O₃ and SiC), amount of Al₂O₃ nano-reinforcement (0 and 0.5 wt.%), and load (100 and 200 N). Various ANNs were applied, and the best ANN for wear rate (WR), with an overall regression coefficient of 0.99484, was a network with architecture 3-15-1 and a logsig (logarithmic sigmoid) transfer function. For coefficient of friction (CoF), the best ANN was the one with architecture 3-6-1 and a tansig (hyperbolic tangent sigmoid) transfer function and had an overall regression coefficient of 0.93096. To investigate the potential of ANN for the prediction of two outputs simultaneously, an ANN was trained, and the best results were from network 3-5-2 with a logsig transfer function and overall regression coefficient of 0.99776, but the predicted values for CoF in this case did not show good correlation with experimental results. After the selection of the best ANNs, the Taguchi grey multi-response optimisation of WR and CoF was performed for the same combination of factors as the ANNs. For optimal WR and CoF, the combination of factors was as follows: composite with 3 wt.% Al₂O₃ micro-reinforcement, 0.5 wt.% Al₂O₃ nano-reinforcement, and a load of 100 N. The results show that developed ANN, the Taguchi method, and the Taguchi grey method can, with high reliability, be used for the optimisation of wear rate and coefficient of friction of hypereutectic Al-Si composites. Microstructural investigations of worn surfaces were performed, and the wear mechanism for all tested materials was light abrasion and adhesion. The findings from this research can contribute to the future development of hypereutectic Al-Si composites. Full article

This paper addresses peculiarities in the formation and adherence of a tribofilm on the wear track surface of antifriction PI- and PEI-based composites, as well as a transfer film (TF) on a steel counterface. It is shown that during hot pressing, PTFE nanoparticles melted and coalesced into micron-sized porous inclusions. In the PEI matrix, their dimensions were much larger (up to 30 µm) compared to those in the PI matrix (up to 6 µm). The phenomenon eliminated their role as effective uniformly distributed nanofillers, and the content of 5 wt.% was not always sufficient for the formation of a tribofilm or a significant decrease in the WR values. At the loaded content, the role of MoS₂ and graphite (Gr) microparticles was similar, although filling with MoS₂ microparticles more successfully solved the problem of adhering to a PTFE-containing tribofilm in the point tribological contact. This differed under the linear tribological contact. The higher roughness of the steel counterpart, as well as the larger area of its sliding surface with the same PTFE content in the three-component PI- and PEI-based composites, did not allow for a strong adherence of either the stable PTFE-containing tribofilm on the wear track surface or the TF on the steel counterpart. For the PEI-based composites, the inability to shield the steel counterpart from the more reactive polymer matrix, especially under the conditions of PTFE deficiency, was accompanied by multiple increases in the WR values, which were several times greater than that of neat PEI. Full article

In order to provide effective solid lubrication to Ni₃Al coating, 10 wt.% Ag and different amounts of MoO₃ solid lubricant were mechanically mixed with the SHSed Ni₃Al powder and sprayed HVOF. Microstructure, mechanical properties, and tribological behavior from 25 °C to 800 °C of the coatings were studied, and the basic wear mechanisms were explored and discussed as well. Results show that the hardness and adhesive bonding strength of the coatings are slightly decreased, while there is little effect on the microstructure and mechanical properties of the Ni₃Al-based composite coating when the content of MoO₃ additive in the feedstock powder is lower than 15 wt.%. The composite coating formed by feedstock powder containing 15 wt.% MoO₃ additive also presents excellent anti-friction and anti-wear performance from 25 °C to 800 °C, especially at 800 °C, where a complete, smooth, and thicker lubricating film comprised of NiO, Al₂O₃, MoO₃, and Ag₂MoO₄ was formed, which reduced the friction coefficient (COF) and wear rate (WR) to the lowest value of 0.36 and 6.03 × 10⁻⁵ mm³/(Nm), respectively. An excessive amount of MoO₃ in the feedstock powder (20 wt.%) results in inferior interlayer bonding of the formed coating, and the coating is more prone to delamination and abrasive wear above 200 °C. Full article

The tribological properties of nanofluids are influenced by multiple factors, and the interrelationships among the factors are deserving of further attention. In this paper, response surface methodology (RSM) was used to study the tribological behavior of reduced graphene oxide–Al₂O₃ (rGO-Al₂O₃) nanofluid. The interaction effects of testing force, rotational speed and nanoparticle concentration on the friction coefficient (μ), wear rate (W_r) and surface roughness (R_a) of steel disks were investigated via the analysis of variance. It was confirmed that all the three input variables were significant for μ and W_r values, while testing force, nanoparticle concentration and its interaction with testing force and rotational speed were identified as significant parameters for R_a value. According to regression quadratic models, the optimized response values were 0.088, 2.35 × 10⁻⁷ mm³·N⁻¹·m⁻¹ and 0.832 μm for μ, W_r and R_a, which were in good agreement with the actual validation experiment values. The tribological results show that 0.20% was the optimum mass concentration which exhibited excellent lubrication performance. Compared to the base fluid, μ, W_r and R_a values had a reduction of approximately 45.6%, 90.3% and 56.0%. Tribochemical reactions occurred during the friction process, and a tribofilm with a thickness of approximately 20 nm was generated on the worn surface, consisting of nanoparticle fragments (rGO and Al₂O₃) and metal oxides (Fe₂O₃ and FeO) with self-lubrication properties. Full article

The wear performance of jute/coir unsaturated polyester composites, filled with eggshell powder (ESP) and nanoclay (NC), were examined, concentrating on two measured parameters, coefficient of friction (COF) and wear rate (WR). To assess the possibilities of this material, a Taguchi study, based on grey relational analysis (GRA), was carried out, based on three testing parameters of the wear performance, load (10, 20, and 30 N), speed (100, 150, and 200 rpm), and sliding distance (30, 40, and 50 m). The material showed promising characteristics especially at high load, low speed, and high sliding distance. When comparing the respective influence of the three different parameters, the speed proved to be the most critical, this suggested the possible application of the biocomposite only for very low values of it. On the other hand, it was also elucidated that the presence and interfacial adhesion of the two fillers considerably hindered the formation of ploughing during wear test, despite the fact that degradation might be continuous and critical as far as loading progresses. Full article

In recent years, several high-performance nature-inspired metaheuristic algorithms have been proposed. It is important to study and compare the convergence, computational burden and statistical significance of these metaheuristics to aid future developments. This study focuses on six recent metaheuristics, namely, ant lion optimization (ALO), arithmetic optimization algorithm (AOA), dragonfly algorithm (DA), grey wolf optimizer (GWO), salp swarm algorithm (SSA) and whale optimization algorithm (WOA). Optimization of an industrial machining application is tackled in this paper. The optimal machining parameters (peak current, duty factor, wire tension and water pressure) of WEDM are predicted using the six aforementioned metaheuristics. The objective functions of the optimization study are to maximize the material removal rate (MRR) and minimize the wear ratio (WR) and surface roughness (SR). All of the current algorithms have been seen to surpass existing results, thereby indicating their superiority over conventional optimization algorithms. Full article

Duplex stainless steels (DSSs) exhibit excellent corrosion resistance and are being used in a variety of industrial applications. Reducing/eliminating the amount of nickel in such alloys will contribute significantly to its economic viability. Moreover, a well-established wear behavior for these alloys is also an essential development in most of their applications. Hence, in this work, the Taguchi technique was effectively implemented to investigate the effect of operating factors such as sliding speed and applied load on the wear behavior of different compositions of nickel-free DSSs. It was observed that the composition had a higher contribution of 33.66% to the wear rate (WR) and the contribution of the sliding speed to the coefficient of friction (COF) was found to be 68.17%. With a good agreement, a regression model was also developed to predict the WR and COF within a certain range of factors. Wear tests have also shown that the developed nickel-free DSS is a promising candidate in terms of wear resistance as compared to austenitic stainless steels (ASS). Full article

We report the synthesis and characterization of two different types of Ni-based laminated composites (Types I and II). In Type-I composites, layers of Ni and Ti₃SiC₂ (Ni–Ti₃SiC₂) were interleaved with Ni, whereas in Type-II composites, Ni–Ti₃SiC₂ layers were interleaved with Al and Ni. The laminate thickness and Ti₃SiC₂ content in the individual Ni–Ti₃SiC₂ layers were systematically varied in both the composites. Detailed SEM studies showed that Ti₃SiC₂ particulates are well distributed in the Ni-matrix with little or no interfacial reactions with interparticle porosity. However, there were interfacial reactions between Ni and Al in Type II composites. In general, Type I multilayered composites had higher ultimate compressive strength (UCS) in parallel orientation as compared to perpendicular orientation (layers are aligned parallel or perpendicular to the wear surface then it will be referred to as parallel or perpendicular orientation). Comparatively, in Type II composites, the UCS was greater in perpendicular orientation as compared to parallel due to the presence of Al layers as bonding layers. Both the composite designs showed triboactive behavior against alumina disks and sensitivity to laminate thickness and orientation. In Type-I composites, the decrease in µ and wear rate (WR) with laminate thickness was more pronounced in the perpendicular orientation as compared to the parallel orientation. Comparatively, Ni–Ti₃SiC₂/Al/Ni composites showed that the parallel orientation was more effective in enhancing the triboactive performance. SEM analysis of tribosurfaces showed signs of triboxidation and abrasion, which led to the formation of O-rich tribofilms. Full article

In this paper, we report the synthesis and characterization of ultra-high molecular weight polyethylene (UHMWPE)-lignin composites. During this study four different compositions, namely UHMWPE, UHMWPE-13 wt. % lignin, UHMWPE-25 wt. % lignin and UHMWPE-42.5 wt. % lignin were fabricated by hot pressing. Detailed microstructural studies by scanning electron microscopy (SEM) showed that UHMWPE and UHMWPE-13 wt. % lignin had a uniform microstructure, whereas UHMWPE-25 wt. % lignin and UHMWPE-42.5 wt. % lignin samples were riddled with pores. UHMWPE and UHMWPE-13% lignin showed comparable flexural strengths of ~32.2 MPa and ~32.4 MPa, respectively. However, the flexural strength dropped drastically in UHMWPE-25 wt. % lignin and UHMWPE-42.5 wt. % samples to ~13 MPa and ~8 MPa, respectively. The tribology of UHMWPE-lignin composites is governed by the tribofilm formation. All the compositions showed similar µ_mean values and the specific wear rates (WR) decreased gradually as the concentration of lignin in UHMWPE was increased. Full article