Green machining technology has attracted considerable attention as an environmentally sustainable alternative to conventional cutting fluids. This study investigated the feasibility of tree-based vegetable oils, namely palm oil, virgin coconut oil, and olive oil, as base lubricants for minimum quantity lubrication (MQL), with and without molybdenum disulfide (MoS 2 ) nanoparticles, during the milling of AISI 304 stainless steel. Machining performance was evaluated in terms of cutting force, surface roughness, and chip morphology under dry cutting, pure vegetable oil-based MQL, and nanofluid MQL containing 0.3, 0.6, and 0.9 wt% MoS 2 nanoparticles. The results showed that vegetable oil-based MQL significantly improved machining performance compared with dry cutting. The greatest reduction in resultant cutting force was obtained using virgin coconut oil with 0.6 wt% MoS 2 at 150 m/min. This corresponded to a 41.72% reduction compared with dry cutting. Meanwhile, 0.3 wt% MoS 2 achieved a 37.16% reduction. The lowest surface roughness was obtained with palm oil containing 0.6 wt% MoS 2 , corresponding to a 44.0% reduction compared with dry machining; virgin coconut oil was 36.7%, and olive oil was 33.7%. Furthermore, virgin coconut oil-based nanofluid at 0.3–0.6 wt% MoS 2 produced the most favorable chip morphology, characterized by more homogeneous chip segmentation and smoother free surfaces, indicating improved lubrication at the tool–chip interface. Overall, these findings demonstrate that the investigated tree-based vegetable oils can contribute to improved machining performance during AISI 304 stainless steel milling under the specified conditions.
A deterministic bearing calculation gives one value per performance measure, although its inputs are uncertain. This paper attributes that uncertainty for one bearing class: a plain journal bearing with L / D = 0.5 and ISO VG 46 oil, at two reference duty points and across a twelve-point duty sweep. The finite-width Reynolds equation with the Guembel condition is coupled to a Walther viscosity–temperature law and a global energy balance, and a similarity-based operator solves it in about 30 μs. Against published experiments, the predicted eccentricity agrees within 0.012 between 4 and 10 kN at 2000 rpm, whereas the maximum temperature is underpredicted. Eight documented uncertain inputs are propagated by Monte Carlo and Latin hypercube sampling and decomposed with Sobol indices. At heavy duty the supply temperature explains 67% of the variance of the minimum film thickness and 73% of that of the friction power loss; the load explains 80% of that of the peak pressure. Interactions are weak. The roughness share of the film-parameter variance (65%) is largely definitional. The local clearance elasticity of the film thickness changes sign at a nominal eccentricity ratio of 0.64, and the clearance index falls from 0.51 to a minimum of 0.002. From 10 7 runs, a nominal film parameter of 6.0, twice the full-film limit, still leaves a mixed-lubrication probability of 2.51 × 10 −3 . The leading input changes only if the supply-temperature standard deviation falls below about 3 K, whereas absolute probabilities depend on the input model and thermal closure.
This study examines how prescribed temperature and lubricant shear thinning affect piston ring-cylinder liner lubrication over an engine cycle. A transient mixed-lubrication model combines the averaged Reynolds equation, Patir-Cheng type roughness flow factors, Greenwood-Williamson asperity contact, transient squeeze effects, and Vogel and Carreau–Yasuda viscosity relationships. The new contribution lies in quantifying the combined effects of prescribed temperature and shear thinning on film formation, load sharing, and friction energy, together with the computational gains from full-state warm starting. The model was calibrated and assessed using out-of-calibration load cases validated against experimental minimum film thickness measurements. The independent predictions achieved an RMSE of 0.144 µm, a MAPE of 13.3%, and an R 2 of 0.886. The warm-start strategy reduced the computational time by 53.1% and the number of pressure iterations by 54.3%, while changing the main physical results by less than 0.055%. The numerical simulations show that the most severe lubrication conditions occur near the firing top dead center, where the minimum film thickness decreases to approximately 0.10 µm and asperity contact becomes significant. Compared with the Newtonian assumption, the Carreau-Yasuda model predicts a thinner lubricant film, greater asperity load sharing, and higher cycle-integrated friction. Increasing lubricant temperature further reduces film thickness and shifts the contact toward more severe mixed lubrication. Findings show that thermal and non-Newtonian effects should be considered together to avoid overestimating film formation and underestimating friction and surface interaction in piston ring-cylinder liner contacts.
Gear wear prediction and sustainable lubrication are critical for Industry 4.0 predictive maintenance. In this study, spur gear wear under lubrication with palm oil and a lithium complex grease was evaluated using a Taguchi L32 design and ANOVA. Results showed palm oil outperformed grease, yielding lower mass losses (6–17 mg vs. 9–18 mg), with minimum loss (6–8 mg) at 192 rpm. Vibration analysis revealed lower Root Mean Square (RMS) values for palm oil at 480 rpm (0.0017–0.0081 mm) than grease (0.0033–0.0119 mm), while grease reached Peak-to-Peak amplitudes up to 0.034 mm. Taguchi S/N ratio analysis identified optimal conditions (palm oil, 192 rpm, 40 min) with palm oil achieving a higher mean S/N ratio (39.7 vs. 37.5). ANOVA indicated significant contributions from duration (20.96%, p = 0.037) and lubricant type (16.76%, p = 0.009). In contrast, 50.54% unexplained error highlighted complex interactions. To address data constraints, 32 physical samples were augmented using the Archard equation, yielding a total of 192 samples. A CatBoost model trained via 5-fold GroupKFold cross-validation achieved high accuracy in the LOW wear regime (mean R 2 logK = 0.7198, linear R 2 K = 0.6994, MSE logK = 0.0092, linear RMSE K = 6.9 × 10 −9 ). Conversely, the HIGH wear regime exhibited non-linear instability (mean R 2 logK = −2.7017, linear R 2 K = −11.7590). Feature importance confirmed regime-dependent behavior: revolutions (∼28%) and duration (∼22%) dominated in the LOW regime, whereas sliding distance (∼90%) dominated in the HIGH regime. Overall, this hybrid physics-informed ML approach robustly predicts gear wear under stable operating conditions while identifying critical wear-transition boundaries.
Aerostatic bearings are key enabling components in ultra-precision and high-stability engineering applications owing to their negligible friction, high motion accuracy, and excellent stability. As advanced manufacturing and precision motion systems move toward nanometer- and subnanometer-level accuracy, traditional passive bearing systems face serious physical limitations. For example, very small film gaps that are used to increase stiffness often cause air hammer instability. Also, passive structures do not have the ability to adjust to multiphysics coupling errors during operation. And common continuum models cannot accurately predict nanoscale flow behavior, such as rarefied gas effects. To solve this problem, this review analyzes advanced analytical methods. In particular, it focuses on high-fidelity Fluid-Structure Interaction (FSI) models and Direct Simulation Monte Carlo (DSMC) methods for micro-scale gas flows. At the same time, recent structural designs are also examined. These include biomimetic micro-texture designs based on topological optimization and intelligent compensation using piezoelectric actuators. Evidence shows that combining optimized micro-grooves and active restrictors can increase load-carrying capacity and reduce vortex-induced micro-vibrations.
