The phase and elemental compositions of the contact layer of AISI 1020 steel (pin) were determined in dry sliding against AISI 1045 steel (disc) under mild (normal) and catastrophic wear conditions. Formation of a tribolayer on the sample contact surface was demonstrated. The layer was consisted of FeO, α-Fe, and γ-Fe phases. A study of the sample morphological features of the sliding surface revealed that this layer was deteriorated by an abrasive mechanism in one sector (sector 1) of the nominal contact area. The other sector (sector 2) was deteriorated with visual signs of melting and adhesive interaction. The main attention was paid to the oxygen concentration as an indicator of FeO formation. A high oxygen concentration (30–40 at%) was observed on the sliding surface in sector 2. Sliding surface area with melt signs was consisted of iron and oxygen. Melt signs were explained by supposition the appearance of strong pulses of electric induction vector in the contact layer (FeO-rich medium) which can transfer the FeO crystal lattice to melt state in thin contact layer during friction. The FeO-melt was not capable for strengthening the tribolayer. The oxygen concentration in the bulk of the tribolayer was less than 5 at%. This indicated weak strengthening of the contact layer due to the formation of crystalline FeO.
The operational viability of offshore renewable energy systems is constrained by the reliability of gear transmission assemblies. This paper provides an exhaustive review of state-of-the-art literature concerning marine gearing, synthesising the multi-physics synergies between mechanical loading and environmental aggression. This review characterises the technical evolution from deterministic industrial standards toward high-fidelity frameworks that account for the coupling of contact fatigue, progressive wear, and electrochemical corrosion. Key focuses include the modelling of emerging subsurface-initiated modes, such as Tooth Interior Fatigue Fracture (TIFF), through depth-dependent material property gradients and multiaxial fatigue criteria. The review evaluates advanced numerical strategies, including the Extended Finite Element Method (XFEM) for autonomous crack path prediction and Rigid-Flexible Multibody Dynamics (MBS) for resolving system-level responses to stochastic Non-Torque Loads (NTLs). Furthermore, experimental validation protocols within the Very High Cycle Fatigue (VHCF) regime and quantitative non-destructive diagnostics, such as Acoustic Emission (AE), are assessed for their role in calibrating Digital Twin and Prognostic Health Management (PHM) frameworks. Finally, considerations are proposed to guide future research, linking microscopic material integrity and macroscopic system reliability for the next generation of offshore renewable energy assets./p>
Abrasive wear remains a critical challenge in tooling and manufacturing processes, as it leads to accelerated material loss, reduced component life, and increased operational costs. This complex phenomenon is governed by dynamic contact conditions, plastic deformation, fracture, and the properties of both the material and interacting abrasive particles. This study uses finite element analysis (FEA) to investigate abrasive wear behavior of JIS SKD11 tool steel coated with Ti- and Cr-based hard coatings, including TiN, TiAlN, CrN, and AlCrN. A single-asperity scratch model was developed to simulate the ploughing wear mechanism, incorporating strain-rate sensitivity via a modified Johnson-Cook model. The simulation results, validated against experimental data, showed good agreement in surface profiles. Wear performance was assessed using degree of wear (ß) and degree of penetration (Dp). TiN exhibited the lowest penetration and highest plastic deformation, indicating strong wear resistance. CrN offered the best substrate protection, while AlCrN showed the highest material loss due to brittle failure. Overall, the findings highlight the critical role of coating selection in improving abrasive durability and demonstrate the potential of advanced FEA-based approaches for optimizing protective coatings in tool steel applications.
The study uses the grey relational analysis (GRA) to maximize the turning conditions of Aluminum Alloy 6061 (AA6061) to obtain an optimal compromise between roughness and hardness of the surface. Even though AA6061 is a high-strength material with a high level of corrosion resistance, the highest machinability is not easily attained. A Taguchi L27 orthogonal array was used to explore the interactive effects of cutting speed, feed rate, and depth of cut. The best parameter combination that was determined through GRA was a cutting speed of 230 m/min, a feed rate of 0.07 mm/rev, and a depth of cut of 0.2 mm, which gave the best Grey Relational Grade of 0.8067 (Experiment 10). The surface finish of 0.53 µm and the hardness of 75.73 HV were excellent. The hypothesis that feed rate has the most significant variable influence on machining outcomes was tested using ANOVA. The outcomes of the ANOVA proved the dominance of feed rate, which has 42.3% contribution to the Grey Relational Grade (p = 0.001), depth of cut (20.6%, p = 0.017), and cutting speed (7.2%, p = 0.21). The remaining error explained 29.9 percent of the overall variation, implying that other variables, including interactions of parameters, wear development of tools or even variability of materials, might affect the performance of multi-objective machining. In contrast to the earlier GRA-based research on AA6061 that dealt with the isolated parameter effects, this study is the first to measure parameter interaction dynamics and provides a statistically rigorous baseline with error contribution analysis (29.9%), as well as an established benchmark of future integration of sustainable cooling strategies.
This study evaluates TiO₂-, MoS₂-, and hybrid TiO₂/MoS₂ nanoparticles as additives for a characterized 50:50 Peltophorum pterocarpum methyl ester:waste cooking oil methyl ester base fluid. The base fluid was dominated by methyl oleate, methyl linoleate, and methyl palmitate and had an acid number of 0.27 mg KOH/g, water content of 318 mg/kg, and oxidation stability of 4.7 h. A two-stage design screened additives at 0.05 wt.% and then optimized the 25:75 TiO₂:MoS₂ hybrid from 0.025 to 0.10 wt.%. Tribofluids with 0.20 wt.% Span 80 were assessed by density, viscosity, apparent zeta potential, sedimentation, four-ball testing, and SEM/EDS. The 0.05 wt.% 25:75 hybrid gave the best batch-specific response, with apparent zeta potential of -45.7 ± 1.1 mV, 60 d sedimentation index of 2.5 ± 0.2%, coefficient of friction of 0.049 ± 0.001, wear scar diameter of 0.51 ± 0.01 mm, and comparative wear-rate index of 3.10 ± 0.08 × 10⁻⁶ mm³/N·m. Friction, wear scar diameter, and wear-rate index decreased by 43.0%, 28.2%, and 40.4% relative to the base fluid. SEM/EDS indicated smoother tracks and more uniform Ti/Mo/S-associated signals than the overloaded 0.10 wt.% formulation. The optimum applies to this batch and preparation route.
Basalt fiber reinforced polymer (BFRP) composite pipes were reinforced with 0.25 wt% graphene nanoplatelets by ultrasonic mixing technique. Solid particle erosion tests were carried out at 23 °C, 50 °C and 100 °C and different impingement angles (30°, 60°, 90°). The highest erosion rate in Basalt pipe was at 90° erosive particle impingement angles, while in graphene nanoplate reinforced pipes, this rate was realized at 60° and 90°. In other words, at 23°C, the erosion resistance gain achieved with graphene addition was 1,52-2,89 times greater depending on the impact angle, but even experiments conducted at 50°C and 100°C demonstrated a 55% increase in the practical relevance of graphene use. To provide evidence for damage mechanisms based on the detailed data obtained, the focus was on clearly demonstrating the quantitative impact of graphene's presence using scanning electron microscopy. Furthermore, enhanced performance and durability of additive composite materials will pave the way for their widespread use in industrial applications, with sectoral output.
Three-body abrasive wear is a major degradation mechanism in fiber-reinforced thermoplastic composites used in demanding automotive and aircraft components. Under abrasive wear circumstances, this work examines the wear performance and predictive modeling of carbon nanofiber (CNF) filled short glass fiber/polyphenylene sulfide (GF/PPS) hybrid nanocomposites. To assess the impact of CNF content on microstructure, density, hardness, interlaminar shear strength, and abrasive wear behavior, nanocomposites with varying CNF levels (0–1 wt%) were produced using extrusion and injection molding. The microstructural data are in support of the integration of CNF to strengthen the fiber/matrix interface and boost the load transmission and crack-bridging features by the formation of a well-dispersed reinforcing structure at the nanoscale. Density study revealed that excessive loading led to agglomeration and increased porosity whereas moderate CNF addition (0.6 and 0.8 wt%) reduced void content and enhanced packing effectiveness. The baseline composite had a Barcol hardness of 44.2, which improved to 54.3 with 0.8 wt% CNF. Box–Behnken design was planned for the abrasive wear trials considering load, abrading distance and filler content and response surface methodology was used for the analysis and optimization. Statistical analysis indicates that load and CNF content have a significant impact on wear loss. Additionally, compared to the GF/PPS composite, the addition of 0.8 wt% CNFs greatly increased the abrasion resistance of baseline composites, as evidenced by the development of very small and shallow grooves, smoother worn surfaces, and decreased wear loss. ANOVA and response surface analysis revealed that CNF content and applied load were the most significant parameters determining wear loss, while the proposed model exhibited good predictive accuracy (R2=86.87%, adjusted R2=83.29%). A dependable framework for creating wear-resistant GF/PPS hybrid nanocomposites for cutting-edge tribological applications is provided by the established prediction model.
Artificial intelligence and digital twin technologies have emerged as transformative tools for engineering systems that require accurate prediction, real-time monitoring, and adaptive decision-making. In tribology, conventional wear mitigation approaches rely heavily on empirical testing, simplified analytical models, and periodic condition-based maintenance strategies. While these methods have supported decades of engineering practice, they struggle to capture the multi-scale, multi-physics nature of friction, wear, and lubrication under dynamically changing operating conditions. Limitations such as delayed fault detection, limited adaptability to unseen regimes, high experimental cost, and an inability to integrate real-time system feedback motivate the need for more intelligent and responsive frameworks. Recent advances in artificial intelligence-enabled digital twins address these challenges by combining physics-based models, real-time sensor data, and data-driven learning algorithms within a continuously updated virtual representation of the physical system. In tribological applications, such frameworks enable near real-time prediction of frictional behavior, wear evolution, lubrication regime transitions, and remaining useful life, while supporting adaptive control and optimized maintenance decisions. This review consolidates the current research literature on artificial intelligence-enabled digital twin applications in tribology and lubricated mechanical systems, with emphasis on sensing technologies, hybrid modeling approaches, data management strategies, and system architectures. Key challenges, including data quality, computational burden, model interpretability, and scalability, are discussed alongside emerging solutions. Through an in-depth synthesis of peer-reviewed studies, this paper highlights how the integration of artificial intelligence and digital twins offers a robust pathway toward more reliable, efficient, and sustainable tribological systems.
Fiber reinforced polymer composites (FRPCs) have been widely known for their high strength and better wear resistance in dry sliding condition mainly related to adhesion transfer or fatigue. However, the way FRPCs behaves under abrasion wear situation needs proper investigation. Hence in this study, three-body abrasion wear behaviour of Neat Nylon66 (NN), short-glass fiber (SGF) reinforced Nylon66 (SGF/NN) composites and talc filled SGF/NN (T-SGF/NN) hybrid composites were investigated. Talc fillers were varied from 2 to 8 wt.% at a step of 2 wt.% and designated as 2T-SGF/NN, 4T-SGF/NN, 6T-SGF/NN, and 8T-SGF/NN hybrid composites. These composites were fabricated by melt blending technique using twin screw extrusion and injection molding process. Three body abrasion wear test was performed using rubber wheel abrasion tester in accordance at ASTM G-65 for different loading conditions (10, 15 and 20 N) and abrading distances (500, 750, and 1000 m). Results revealed that wear loss of all the composites increased with increase in the applied load and abrading distance which is attributed to penetration of abrasive particles, micro-cutting and fiber-matrix debonding. However, specific wear rate (SWR) decreased with respect to increase in abrading distance due to the formation of protective tribo-layer that stabilizes wear. In contrast, SWR increased with increasing in applied load because severe contact stress and intensified material removal. Worn surface of the talc filled SGF/NN composites were examined field emission scanning electron microscope (FESEM). Average surface roughness and dept of wear were measured using 3D optical profilometer.
Waste cooking oil (WCO) was valorized into an eco-friendly biolubricant and further enhanced with titanium dioxide (TiO2) nanopowder (0.15 wt%) for aluminum alloy 6061 (AA6061) sliding contacts. WCO was converted to fatty acid methyl ester (FAME) and subsequently transesterified with ethylene glycol to produce the base biolubricant, while TiO2 was dispersed by stirring–sonication. Physicochemical (density and Fourier transform infrared spectroscopy, FTIR), rheological (viscosity–temperature and Vogel–Tammann–Fulcher, VTF, modeling), thermal (thermogravimetric analysis/differential thermal analysis, TG/DTA), and tribological (pin-on-disc; SKD-11 pin against AA6061 disc) characterizations were conducted. The biolubricant showed near-Newtonian behavior and viscosity decreased with increasing temperature and with TiO2 addition. TG/DTA showed improved early-stage thermal resistance with TiO2 (20–400 °C mass loss decreased from 23.98% to 16.73%), while the DTA exotherm shifted from 490.46 °C to 284.59 °C. Tribologically, the average coefficient of friction (n=5) decreased from 0.471 (dry) to 0.231 (biolubricant) and 0.131 (biolubricant+TiO2). Wear was markedly reduced: compared with the biolubricant without TiO2, adding TiO2 decreased disc mass loss from 0.0046 g to 0.0020 g and reduced specific wear rate from 0.000341 to 0.000148 mm³/N·m (˜56.6% reduction). Relative to dry contact, the TiO2-enhanced biolubricant reduced mass loss and specific wear rate by ˜92.8%. Overall, the TiO2-enhanced WCO-based biolubricant is promising for aluminum-related metalworking and other Al–steel sliding interfaces under moderate operating conditions.
The article presents the results of an analytical and experimental study of the tribological characteristics of a rope-plate conveyor intended for transporting large-sized and abrasive materials. The frictional interaction between traction ropes and the supporting-gripping elements of the load-carrying belt using shock-absorbing supporting idlers and pressure roller supports, which ensure redistribution of contact pressure and stabilization of the adhesion regime, is considered. The effective coefficient of friction under operating conditions, the adhesion coefficient, contact pressure, and wear rate were analyzed in relation to the wrap angle, wedge angle, linear load, and stiffness of the elastic elements of the system. It was established that the coefficient of adhesion increases with an increase in the wrap angle and decreases with an increase in the wedge angle, while optimization of the geometry of the contact elements contributes to reducing slippage of the traction ropes. It was shown that the introduction of shock-absorbing elements reduces contact pressure by 15–25% and decreases the wear rate by 18–22% compared to the conventional design. The analytical relationships were experimentally confirmed, with the discrepancy between the results amounting to 7–11%. The coefficient of friction in the contact zone varies within the range of 0.28–0.98 depending on the operating conditions. The proposed design provides increased traction capacity, reduced intensity of tribological wear, and improved reliability of the conveyor system during transportation of abrasive materials under severe operating conditions.
In this work, the burnishing parameters, including the spindle speed (S), feed rate (f), depth of penetration (D), and number of rollers (N) are optimized to maximize the Vickers hardness (VH) and minimize energy consumption (EC) as well as average surface roughness (Ra). Predictive models of burnishing responses are proposed using the optimized Extreme Gradient Boosting (OXGBoost) approach. An efficient algorithm entitled Grasshopper Optimization Algorithm (GOA) is used to create optimal solutions. The entropy method and Pareto-Edgeworth Grierson (PEG) are utilized to calculate weights and select the best data. The findings presented that the optimal S, f, D, and N are 1075 rpm, 0.07 mm/z, 0.06 mm, and, respectively. At the optimal point, the VH is enhanced by 5.1%, while the EC and Ra are reduced by 5.4% and 23.3%, respectively. The EC model was significantly affected by the f, S, N, and D, respectively. The Ra model was significantly affected by the D, f, N, and S, respectively. The VH model was significantly affected by the D, N, f, and S, respectively. The OXGBoost-Entropy-GOA-PEG was a prominent solution to deal with complicated optimization issues, as compared to the conventional one. The outcomes can be applied to enhance energy efficiency and surface properties of the burnishing AISI 5140 process.
The study presents a novel semi-empirical dimensionless mathematical model for predicting surface roughness (Ra) in the finish turning of AISI 316LVM stainless steel. The proposed model integrates dimensional analysis (DA), design of experiments (DoE), and regression analysis (RA) to obtain a realistic and industrially applicable model. Dimensional analysis was applied to derive a dimensionally homogeneous mathematical model. Buckingham's p theorem was used to express the relationship between surface roughness and the influencing factors in dimensionless form. As a result, the normalized surface roughness Ra/(f²/rε) is expressed as a function of the normalized cutting parameters: ap/ap,rec, f/frec, and vc/vc,rec,rec, which are normalized with respect to the tool manufacturer’s recommendations. Taguchi’s design of experiments and analysis of variance (ANOVA) were used to investigate the effects of four process factors (corner radius, depth of cut, feed, and cutting speed) on surface roughness. Regression analysis was then used to determine the unknown coefficient and exponents in the mathematical model. The proposed approach is physically consistent and industrially relevant, providing a practical framework for developing predictive surface roughness models in turning operations.
This work investigates the boundary lubrication behaviour of Peltophorum pterocarpum biodiesel enhanced with MoS₂ nanoparticles through statistically planned pin-on-disc experiments and response surface modelling. The biodiesel satisfied the principal ASTM D6751 and EN 14214 property limits considered in this study. MoS₂ nanoparticles with platelet/nanosheet morphology and 70-90 nm average particle size were dispersed at 0-150 ppm, while normal load and sliding speed were varied within 10-40 N and 0.25-1.00 m/s, respectively. A Box-Behnken design was used to model coefficient of friction (COF), wear rate, and post-test surface roughness for AISI 52100 steel contacts. COF and wear rate ranged from 0.094-0.132 and 2.30-4.20 x 10⁻⁶ mm³/N·m, respectively. ANOVA showed significant quadratic models for COF (R² = 0.945) and wear rate (R² = 0.902), with nano-additive concentration, load, and speed being the dominant factors. Confirmation testing at 135 ppm MoS₂, 12 N, and 1.00 m/s yielded 0.093 ± 0.002 COF, 2.34 ± 0.09 x 10⁻⁶ mm³/N·m wear rate, and 0.26 ± 0.01 µm roughness, corresponding to 3.33-4.00% absolute prediction errors. XRD and EDS results indicate a thin MoS₂-/oxide-/carbon-containing tribofilm, supporting a lubrication mechanism involving reduced metal-to-metal contact, lower shear at the interface, and partial filling or smoothing of surface asperities.
The growing demand for sustainable alternatives to petroleum-based lubricants has accelerated interest in bio-lubricants derived from renewable resources. Palm oil has emerged as a promising bio-lubricant alternative due to its renewable sourcing, biodegradability, and advantageous physicochemical properties. Although palm oil esters are suitable for automotive engine oil applications, they have constraints and limitations that can be improved with appropriate additives. This study aims to identify the optimal composition of palm oil ester-based oil blended with lubricating additive oil (OA) for automotive engine oil using the Weighted Aggregated Sum Product Assessment (WASPAS) method. The palm oil ester base oil used in this study is trimethylolpropane trioleate (TMPTO), with an OA and ethylene-vinyl acetate copolymer (EVA). The kinematic viscosity results were determined using a Cannon-Fenske viscometer at temperatures ranging from 40°C to 100°C. The coefficients of friction (COF) and wear scar diameter (WSD) were measured using a four-ball tribometer. From the WASPAS method, TMPTO with 8 wt.% OA provided the optimum performance with the lowest COF and WSD. The sample was further improved by EVA with additives range from 0.5 wt.% to 3.0 wt.% concentration. The composition of 8 wt.% OA and 1 wt.% EVA was identified as the optimal formulation of the blended palm oil ester with additives, as it complies with the Engine Oil Viscosity Classification (SAE J300) standard.
Abrasive wear is characterized by material loss, while adhesive wear results from high stresses inducing material transfer. Corrosion wear arises from chemical reactions, whereas erosive wear occurs due to particle impact. Fatigue wear, including contact fatigue, is attributed to cyclic stress and misalignment. Effective wear mitigation strategies, such as material selection, surface texturing, and lubrication optimization, are explored. Additionally, the influence of surface roughness and structural integrity on wear rates is highlighted. This review comprehensively examines the primary wear mechanisms in tribological systems, including abrasion, adhesion, erosion, corrosion, and fatigue. The novelty of this review lies in integrating recent advancements in wear-resistant materials, nanostructured coatings, and smart lubrication techniques. Emerging trends and challenges, including the role of artificial intelligence in predictive maintenance and sustainable tribological solutions, are also discussed, providing insights into future research directions.
This paper deals with the effect of a synthetic oil based on synthetic esters on a QHD 17R hydraulic gear pump with a geometric volume of 17 cm³, intended for use with mineral oil, during a 500-hour lifetime test. Changes in kinematic viscosity (a decrease of 1.9% at 40°C and 3.9% at 100°C), total acid number (an increase of 2.6%), ISO 4406 purity code (a change from 23/21/17 to 18/16/13), and the morphology of wear particles were observed and analysed to assess the influence of long-term workload on the hydraulic gear pump. Apart from changes of tribological properties, presence of unwanted particles in oil was evaluated. These particles occurred because of internal wear of the hydraulic gear pump and other hydraulic components. Amount of Copper in hydraulic oil increased by 308% to 0.49 mg·kg-1 and amount of Tin increased by 167% to 3.61 mg·kg-1. Metallic particles emitted this way can produce more wear, as they behave as additional abrasive particles flowing in the hydraulic medium. Although these particles did not immediately reduce the machine’s efficiency during the test, their long-term accumulation may accelerate internal wear and lead to reduced flow performance over time.
Glass fiber-reinforced polymer (GFRP) composites have gained widespread attention due to their superior mechanical and tribological properties, making them highly suitable for engineering applications such as bearings, gears, brakes, and seals. The primary objective of this research is to assess and compare the performance of GFRP composites fabricated using different reinforcement materials and manufacturing techniques. This study employs a comprehensive methodology combining experimental analysis and literature review to evaluate the mechanical strength, wear behavior, and surface characteristics of GFRP composites under various conditions. Key manufacturing processes such as hand lay-up, resin transfer molding, compression molding, injection molding, filament winding, and pultrusion are reviewed and compared in terms of performance output and feasibility. The findings reveal that increasing the glass fiber content significantly enhances mechanical strength, thermal stability, and wear resistance. Moreover, composites fabricated through precision-controlled techniques like resin transfer molding and injection molding exhibit improved surface finish and dimensional accuracy. The study concludes that the choice of manufacturing technique and material composition plays a critical role in optimizing composite performance. These insights provide a valuable foundation for selecting suitable composites in high-performance industrial applications and suggest future directions for enhancing material-environment compatibility.
In this study, the rubber-granite contact for a dry surface and two types of contamination, were analyzed by performing a series of experiments under controlled conditions, in the laboratory, using specialized equipment. These experiments were carried out under conditions close to those in reality, respectively for three cases, as follows: clean rubber-granite contact, contact in the presence of a runway deicer fluid and contact in the presence of aircraft deicing fluid. The paper presents a comparative analysis for the measured values of the friction coefficients in the rubber-granite contact, sliding friction, for three different values of the normal force, dry contact and the two types of contamination, at ten values of the measurement speed on each set. Conclusions resulting from the study are: in sliding motion, in the case of friction on a dry surface, the values of the friction coefficient are mainly influenced by adhesion and the microtopography of the contact surface, while in the case of contamination adhesion decreases; for the analyzed contact, the contaminant with lower viscosity affects the values of the friction coefficient in contact less than the contaminant with higher viscosity, which significantly decreases the values of the friction coefficient.
Due to the increasing importance of engineering plastics in diverse engineering fields, demand for these materials has grown alongside technological progress, driving the development of tailored properties for specific applications. This study evaluates the wear behavior of three polymer groups manufactured using additive manufacturing (Material Extrusion, MEX): polylactic acid and carbon fiber-reinforced polylactic acid, both fabricated by the deposition technique and reinforcement with epoxy resin. High-precision three-dimensional models were fabricated for each composite polymer to improve accuracy and performance. Samples were prepared as hollow cylinders with deposition thicknesses defined by diameter reduction ratios (wt.%) of 10%, 20%, and 40%, aiming to investigate the effect of epoxy reinforcement on wear behavior.Both wt.% and applied load during testing were considered as key factors influencing composite performance. Wear properties were assessed using a pin-on-disc device, following a systematic approach to examine the relationship between these parameters and the tribological behavior of the materials. Data analysis was conducted with MINITAB 19, employing the Taguchi method for experimental design and evaluation. Results demonstrated that the adopted design methodology enhanced adhesive wear resistance in all composites. Furthermore, the wt.% had a greater influence on wear performance than the applied load within the scope of this study.
