Search Results (144)

In today’s industrial applications, cutting fluids have attained prime importance due to their all-round features, including increase of tool life by lubrication of the tool at the tool–workpiece interface. This study compares the effects of mango seed oil and pongamia oil on cutting force and surface morphology during the turning of AISI 304 steel. The design of experiments was applied using Taguchi’s method with an L9 array of experiments. During the experiment, it was discovered that mango seed and pongamia-based cutting fluid exhibited the lowest contact angles of 22.1° and 24.4°, respectively, at a 97:3 volumetric concentration of deionized water and eco-friendly oil. The use of mango seed oil as a cutting fluid with MQL (Minimum Quantity Lubrication) resulted in the lowest surface roughness of 0.809 µm, compared to 0.921 µm with pongamia-based cutting fluid. The cutting force was reduced by a maximum of 28.68% using mango seed-based cutting fluid, compared to pongamia-based cutting fluid. ANOVA analysis revealed that feed rate had the maximum influence on the optimization of output parameters for mango seed cutting fluid. For pongamia-based cutting fluid, feed rate had the maximum influence on cutting force, while the depth of cut had the strongest influence on surface roughness. Full article

This study explores the machinability of Incoloy 800HT (high temperature) under a sustainable lubrication approach, employing a twin-nozzle minimum quantity lubrication (MQL) system with groundnut oil as a green cutting fluid. The evaluation focuses on key performance indicators, including surface roughness, tool flank wear, power consumption, carbon emissions, and chip morphology. Groundnut oil, a biodegradable and nontoxic lubricant, was chosen to enhance environmental compatibility while maintaining effective cutting performance. The Taguchi L₁₆ orthogonal array (three factors and four levels) was utilized to conduct experimental trials to analyze machining characteristics. The best surface quality (surface roughness, Ra = 0.514 µm) was obtained at the lowest depth of cut (0.2 mm), modest feed (0.1 mm/rev), and moderate cutting speed (160 m/min). The higher ranges of flank wear are found under higher cutting speed conditions (320 and 240 m/min), while lower wear values (<0.09 mm) were observed under lower speed conditions (80 and 160 m/min). An entropy-integrated multi-response optimization using the MOORA (multi-objective optimization based on ratio analysis) method was employed to identify optimal machining parameters, considering the trade-offs among multiple conflicting objectives. The entropy method was used to assign weights to each response. The obtained optimal conditions are as follows: cutting speed = 160 m/min, feed = 0.1 mm/rev, and depth of cut = 0.2 mm. Optimized outcomes suggest that this green machining strategy offers a viable alternative for sustainable manufacturing of difficult-to-machine alloys like Incoloy 800 HT. Full article

Properly refrigerating hard-to-cut alloys during grinding is key to achieve high quality, strict tolerances, and good surface finishing. Nonetheless, literature about the influence of cooling-lubrication conditions (CLCs) on dimensional accuracy of ground components is still scarce. Thus, this work aims to evaluate surface quality, grinding power, and dimensional accuracy of Inconel 718 workpieces after grinding with silicon carbide grinding wheel at different grinding conditions. Four different CLCs were tested: flood, minimum quantity of lubrication (MQL) without graphene, and with multilayer graphene (MG) at two distinct concentrations: 0.05 and 0.10 wt.%. Different radial depths of cut values were also tested. The results showed that the material’s removed height increased with radial depth of cut, leading to coarse tolerance (IT) grades. Machining with the MQL WG resulted in higher dimensional precision with an IT grade varying between IT6 and IT7, followed by MQL MG 0.10% (IT7), MQL MG 0.05% (IT7-IT8), and flood (IT8). The lower tolerances achieved with MG were attributed to the lowering in the friction coefficient of the workpiece material sliding through the abrasive grits with no material removal (micro-plowing mechanism), thereby reducing grinding power and the removed height in comparison to the other CLC tested. Full article

In the context of sustainable manufacturing practices, minimum quantity lubrication (MQL) has been extensively employed in machining operations involving hard-to-cut materials. While substantial experimental and numerical investigations on MQL-assisted machining have been conducted, existing simulation approaches remain inadequate for modeling the dynamic flow field variations inherent to MQL processes, significantly compromising the predictive reliability of current models. This study introduced an innovative bidirectional iterative coupling framework integrating finite element (FE) analysis and computational fluid dynamics (CFD) to enhance simulation accuracy. Since fluid flow characteristics critically influence tribological and thermal management at the tool–workpiece interface during machining, CFD simulations were initially performed to evaluate how MQL parameters govern fluid flow behavior. Subsequently, an integrated FE-CFD modeling approach was developed to simulate Ti-6Al-4V alloy turning under MQL conditions with varying feed rates. The novel methodology involved transferring thermal flux data from FE simulations to CFD’s heat source domain, followed by incorporating CFD-derived convective heat transfer coefficients back into FE computations. This repetitive feedback process continued until the thermal exchange parameters reached convergence. Validation experiments demonstrated that the proposed method achieved improved alignment between the simulated and experimental results for both cutting temperature profiles and principal force components across different feed conditions, confirming the enhanced predictive capability of this coupled simulation strategy. Full article

Inconel 617 is a nickel-based superalloy that is a primary candidate for use in next-generation nuclear applications such as the Gen IV Molten Salt Reactor (MSR) and Very-High-Temperature Reactor (VHTR) due to its corrosion and oxidation resistance and high strength in elevated temperatures. However, Inconel 617 machinability is poor due to its hardness and tendency to work harden during manufacturing. While the machinability of its sister grade, Inconel 718, has been widely studied and understood due to its applications in aerospace, there is a lack of knowledge regarding the behaviour of Inconel 617 in machining. To address this gap, this paper investigates the influence of cutting parameters in the turning of Inconel 617 and compares the impact of Minimum Quantity Lubrication (MQL) turning against conventional coolant. This investigation was performed through three distinct studies: Study A compared the performance of commercial coatings, Study B investigated the influence of cutting parameters on the surface finish, and Study C compared the performance of MQL to flood coolant. This work demonstrated that AlTiN coatings performed the best and doubled the tool life of a standard tungsten carbide insert compared to its uncoated form. Additionally, the feed rate had the largest impact on the surface roughness, especially at high feeds, with the best surface quality found at the lowest feed rate of 0.075 mm/rev. The utilization of MQL had mixed results compared to a conventional flood coolant in the machining of Inconel 617. Surface finish was improved as high as 47% under MQL conditions compared to the flood coolant; however, work hardening at the surface was also shown to increase by 10–20%. Understanding this, it is possible that MQL can completely remove the need for a conventional coolant in the machining of Inconel 617 components for the manufacturing of next-generation reactors. Full article

This study investigates the machining performance of AZ91D magnesium alloy under three different cooling environments: dry, minimum quantity lubrication (MQL), and nano-reinforced MQL (NanoMQL) with multi-walled carbon nanotubes. Turning experiments were conducted on a CNC lathe with systematically varied cutting parameters, including cutting speed (150–450 m/min), feed rate (0.05–0.2 mm/rev), and depth of cut (0.5–2 mm). The machining performance was evaluated through cutting force measurements, surface roughness analysis, and tool wear examination using SEM. The results demonstrate that the NanoMQL environment significantly outperforms both dry and conventional MQL conditions, providing a 42.2% improvement in surface quality compared to dry machining and a 33.6% improvement over conventional MQL. Cutting forces were predominantly influenced by the depth of cut and the feed rate, while cutting speed showed variable effects. SEM analysis revealed that the NanoMQL environment substantially reduced built-up edge formation and flank wear, particularly under aggressive cutting conditions. The superior performance of the NanoMQL environment is attributed to the enhanced thermal conductivity and lubrication properties of carbon nanotubes, which form a protective tribofilm at the tool–workpiece interface. This study provides valuable insights for optimizing the machining parameters of AZ91D magnesium alloy in industrial applications, particularly where high surface quality and tool longevity are required. Full article

This study examines the impact of PVD coatings on cutting tool wear during the milling of 316L stainless steel under air cooling conditions. In the experiment, a carbide milling cutter coated with a nanocomposite nACo3 (AlTiSiN) coating was used. The coating was deposited using a next-generation device, the PLATIT π411PLUS, which features one central and three lateral rotating cathodes. The nanocomposite nACo3 coating obtained with this method exhibits exceptionally high structural density and excellent mechanical properties. The new generation of the nACo3 coating demonstrates improved surface properties and a lower friction coefficient compared to previous generations. The findings indicate that PVD nACo3 coatings significantly enhance wear resistance, extending tool life while maintaining acceptable surface quality. The optimal cutting time was determined to be approximately 90 min, after which a sharp increase in surface roughness and tool wear was observed. After 120 min of machining, substantial deterioration of surface quality parameters was recorded, suggesting increasing cutting forces and cutting edge degradation. SEM and EDS analyses revealed the presence of adhered material on the tool and sulfide inclusions in the microstructure of 316L stainless steel, which influenced the machining process. The nACo3 coating demonstrated high thermal and wear resistance, making it an effective solution for machining difficult-to-cut materials. This study suggests that selecting appropriate cutting parameters, tool geometry, protective coatings, and cooling strategies can significantly affect tool longevity and machining quality. The novelty of this research lies in the application of innovative nanocomposite PVD coatings during the milling of 316L stainless steel under air cooling conditions. These studies indicate potential future research directions, such as the use of minimum quantity lubrication (MQL) or cryogenic cooling as methods to reduce tool wear and improve post-machining surface quality. Full article

This study compares the hard turning performance under dual-nozzle minimum quantity lubrication (MQL) using mineral oil and 1-butyl-3-methylimidazolium chloride-based ionic fluids. Key performance indicators, including tool life (based on tool wear), surface roughness, cutting power, cutting temperature, cutting sound, carbon emission, and circularity error, were evaluated to assess manufacturing sustainability. The results revealed that ionic fluid-assisted MQL significantly outperformed mineral oil, improving tool life by 28.75% and reducing surface roughness by 5.58%, attributed to the superior lubrication and cooling ability of ionic fluids. Additionally, after 85 min of machining, the power consumption and carbon emission were greatly reduced under ionic fluid conditions, indicating a lower environmental impact. For precision machining concerns, the ionic fluid proved more favorable, as circularity error under mineral oil conditions was 2.67 times higher than with ionic fluids. The weighted Pugh matrix awarded ionic fluid a higher sustainability score (+7) than mineral oil (+1), establishing it as the superior cooling option for hard turning, enhancing sustainability in machining difficult-to-cut metals. Full article

Potassium dihydrogen phosphate (KDP) crystals, vital for high-power laser systems, pose significant machining challenges due to their brittleness, low hardness, and hygroscopic properties. Achieving crack-free, high-precision surfaces is essential but complex. Single-point diamond fly-cutting (SPDF) is the primary method, yet it exposes tools to high mechanical stress and heat, accelerating wear. In dry cutting, worn tools develop adhesive layers that detach, causing scratches and degrading surface quality. Traditional wet cutting improves surface finish but leaves residual fluids that contaminate the surface with metal ions, leading to optical degradation and fogging. To address these issues, this study explores mixed-fat-based minimum quantity lubrication (MQL) as a sustainable alternative, comparing two lubricants: biodegradable-base mixed ester lubrication (BBMEL) and hydrocarbon-based synthetic lubricant (HCBSL). A comprehensive evaluation method was developed to analyze surface roughness, tool wear, and subsurface damage under dry cutting, MQL-BBMEL, and MQL-HCBSL conditions. Experimental results show that MQL-BBMEL significantly enhances machining performance, reducing average surface roughness by 27.77% (Sa) and 44.77% (Sq) and decreasing tool wear by 25.16% compared to dry cutting, outperforming MQL-HCBSL. This improvement is attributed to BBMEL’s lower viscosity and higher proportion of polar functional groups, which form stable lubricating films, minimizing friction and thermal effects. Structural analyses confirm that MQL-BBMEL prevents KDP crystal deliquescence and surface fogging. These findings establish MQL-BBMEL as an eco-friendly, high-performance solution for machining brittle optical materials, offering significant advancements in precision machining for high-power laser systems. Full article

This study investigates the performance of biobased and nano-additive lubricants for the sustainable machining of Al6082 alloy. The experiments were conducted in five different cutting environments: dry cutting, olive oil-based minimum quantity lubrication (MQL), sunflower oil-based MQL, olive oil-based MQL with nano-SiO₂ additives, and sunflower oil-based MQL with nano-SiO₂ additives. The machining performance was evaluated in terms of key parameters such as surface roughness, cutting forces, tool wear, cutting temperature, and chip morphology. The results show that nano-additive lubricants reduce friction, reduce tool wear, and reduce cutting forces, thus providing lower surface roughness. The nano-SiO₂-additive olive oil-based MQL method showed the optimum performance by providing the lowest cutting force and temperature values. It was also determined that nano-additive lubricants contributed to more regular chip formation. The study reveals that the use of biobased nano-lubricants in sustainable machining processes offers environmental and economic advantages. In the future, it is recommended to examine different types and concentrations of nanoparticles, conduct long-term tool wear analyses, and evaluate the effects on other machining methods. Full article

In hard milling, there has been a significant surge in demand for sustainable machining techniques. Research indicates that the Minimum Quantity Lubrication (MQL) method is a promising approach to achieving sustainability in milling processes due to its eco-friendly characteristics, as well as its cost-effectiveness and improved cooling efficiency compared to conventional flood cooling. This study investigates the end milling of AISI H11 die steel, utilizing a cooling system that involves a mixture of graphene nanoparticles (Gnps) and sesame oil for MQL. The experimental framework is based on a Taguchi L36 orthogonal array, with key parameters including feed rate, cutting speed, cooling condition, and air pressure. The resulting outcomes for cutting zone temperature and surface roughness were analyzed using the Taguchi Signal-to-Noise ratio and Analysis of Variance (ANOVA). Additionally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) prediction model was developed to assess the impact of process parameters on cutting temperature and surface quality. The optimal cutting parameters were found to be a cutting speed of 40 m/min, a feed rate of 0.01 mm/rev, a jet pressure of 4 bar, and a nano-based MQL cooling environment. The adoption of these optimal parameters resulted in a substantial 62.5% reduction in cutting temperature and a 68.6% decrease in surface roughness. Furthermore, the ANFIS models demonstrated high accuracy, with 97.4% accuracy in predicting cutting temperature and 92.6% accuracy in predicting surface roughness, highlighting their effectiveness in providing precise forecasts for the machining process. Full article

This study investigates the optimization of the ball burnishing process for Al8090 aluminum–lithium alloy, focusing on surface quality, mechanical properties, and sustainability metrics. A mixed-design L₁₈ Taguchi experimental approach was employed to evaluate the effects of three critical parameters: burnishing force, feed rate, and number of passes under two lubrication conditions—dry and minimum quantity lubrication (MQL). Surface roughness, Brinell hardness, power and energy consumption, and carbon emissions were measured to assess technical and environmental performance. The results revealed that the MQL environment significantly improved surface roughness, achieving the lowest Ra value of 0.562 µm with a force of 200 N, a feed rate of 0.05 mm/rev, and four passes. In contrast, the highest Brinell hardness (43.6 HB) was observed in dry conditions with a force of 100 N, a feed rate of 0.1 mm/rev, and two passes. Energy consumption and carbon emissions were minimized in the MQL condition, with the lowest energy consumption recorded as 0.0169 kWh and corresponding carbon emissions of 0.0084 kg CO₂. These findings highlight the trade-offs between surface quality, hardness, and sustainability, providing valuable insights for optimizing the ball burnishing process for advanced materials like Al8090. Full article

Ti-6Al-4V is a titanium-based alloy that is widely used in a diverse range of applications, especially in industries such as biomedical and aerospace. Several lubricooling techniques have been introduced to enhance the machinability of these materials. Among them, environmentally friendly strategies are gaining in importance, with sustainability trends rising in manufacturing. The present research investigates the effect of two eco-friendly lubricooling techniques (minimum quantity lubrication and cryogenic cooling), along with other cutting parameters (cutting speed and feed per tooth), on the surface roughness and microhardness of the machined surfaces, which are identified as one of the most frequently implemented indicators of surface integrity in the ball-end milling of the Ti-6Al-4V alloy. In addition, the total electrical energy consumption of the machine tools under different cooling/lubrication conditions was also analyzed. The results obtained showed that cryogenic cooling enhanced milling performance as compared to MQL. Moreover, a multi-objective parameter optimization model integrating the machining responses (surface roughness, microhardness, energy consumption, and productivity) and sustainability metrics (environmental impact, operator’s health and safety, and waste management) was introduced. It was found that cryogenic cooling outperformed the MQL method in terms of both machining performance and environmental impact. An analysis of variance (ANOVA) was carried out to evaluate the significance of each process parameter on the multiple performance index. The results indicate that feed per tooth, cooling method, and cutting speed were significant, with respective contributions of 39.4%, 36.8%, and 22.9%. Finally, the optimal parameter setting was verified through a confirmation test and the results reveal that an improvement was observed in the machining responses and multiple performance index. Full article

Hard turning is a precision machining process used to cut materials with hardnesses exceeding 45 HRC using single-point tools. It offers an efficient alternative to traditional grinding for finishing operations in manufacturing. This paper explores the machinability of hardened AISI 4340 steel for a hard turning process utilizing dry and cryogenic (Cryo) plus minimum quantity lubrication (MQL) (Cryo+MQL) techniques, focusing on critical machinability aspects such as cutting force, surface roughness, and tool life. The orthogonal dry turning was performed with a cutting speed (V) ranging from 300–400 m/min, a feed rate (f) between 0.05 and 1 mm/rev, and a depth of cut (doc) from 0.1 to 0.3 mm. A statistical analysis of the obtained results revealed that the feed rate was the most influential parameter, contributing 50.69% to the main cutting force and 80.03% to surface roughness. For tool life, cutting speed was identified as the dominant factor, with a contribution rate of 39.73%. Multi-objective optimization using Grey relational analysis (GRA) identified the optimal machining parameters for the hard turning of AISI 4340 alloy steel as V = 300 m/min, f = 0.05 mm/rev, and doc = 0.1 mm. The Cryo+MQL technique was subsequently applied to these parameters, yielding significant improvements, with a 48% reduction in surface roughness and a 184.5% increase in tool life, attributed to enhanced lubrication and cooling efficiency. However, a slight 4.6% increase in cutting force was observed, likely due to surface hardening induced by the low-temperature LN₂ cooling. Furthermore, reduced adhesion and tool fracture on the principal cutting edge under Cryo+MQL conditions justify the superior surface quality and extended tool life achieved. This research highlights the industrial relevance of hybrid lubrication in addressing challenges associated with hard turning processes. Full article

We investigated the orthogonal cutting process by using machine learning models to predict its performance. This study used the AZ91 magnesium alloy as the workpiece material, and machining was performed under the Minimum Quantity Lubrication (MQL) technique. The input parameters were the feed, cutting speed and MQL flow rate. Additionally, the outputs were flank tool wear, the chip contact length, peak distance, valley distance, pitch distance, chip segmentation ratio, compression ratio and shear angle. Studies on machine learning (ML) models being employed to evaluate the performance of the MQL-assisted orthogonal machining of AZ91 are very rarely found in the literature. This study explored machine learning (ML) as a data-driven alternative, evaluating decision tree regression, Bayesian Optimization, Random Forest Regression and XGBoost for predicting machinability. A comprehensive dataset of the cutting parameters and outcomes was utilized to train and validate these models, aiming to enhance the accuracy of the predictive analysis. The performance of each model was evaluated based on error metrics such as the mean squared error (MSE) and R-squared values. Among these models, XGBoost demonstrated a superior predictive accuracy, outperforming the other methods in terms of its precision and generalizability. These findings suggest that XGBoost provides a more robust solution for modeling the complexities of the orthogonal cutting process, offering valuable insights into process optimization. The analysis supports that the XGBoost model is the most accurate, with a 34.1% reduction in the mean squared error and a 17.1% reduction in the mean absolute error over these values for the Decision Tree. It also outperforms the Random Forest Regression model, achieving a 19.8% decrease in the mean squared error and a 7.1% decrease in the mean absolute error. Full article