Search Results (157)

Due to low thermal conductivity and high specific strength, nickel-based superalloys are prone to service performance degradation caused by thermal damage during traditional high-efficiency grinding processes. Although the heat pipe grinding wheel with minimum quantity lubrication (HPGW-MQL) technology can reduce the probability of thermal damage to a certain extent, further breakthroughs are still needed. Therefore, this study proposes a new integrated process of ultrasonic vibration-assisted grinding by heat pipe grinding wheel with minimum quantity lubrication (UVAG-HPGW-MQL), aiming to balance the requirements of green grinding and the optimization of grinding performance for nickel-based superalloys. However, the mechanism of action of ultrasonic vibration on the cooling and lubrication performance of the proposed process remains unclear. Given that, comparative experiments between UVAG-HPGW-MQL and HPGW-MQL were conducted, focusing on exploring the influence of ultrasonic vibration on their cooling and lubrication performance. The experimental results, obtained when the grinding speed, workpiece feed rate, and grinding depth were set at 15–35 m/s, 40–120 mm/min, and 0.05–0.25 mm, respectively, indicate that, compared with HPGW-MQL, ultrasonic vibration causes periodic “contact-separation” between grains and workpiece. This dynamic process shortens the contact length between grains and workpiece, leading to maximum reductions of 43.85%, 22.15%, 34.16%, and 30.77% in grinding force, grinding force ratio, grinding temperature, and specific grinding energy, respectively. On the other hand, the ultrasonic cavitation effect causes atomization of the lubricating oil film adsorbed on the workpiece surface, leading to a decrease in lubrication performance and resulting in a maximum increase of 27.27% in the friction coefficient. This study provides new theoretical support and technical approaches for the green grinding of nickel-based superalloys. Full article

Nickel–titanium (NiTi) alloys are extensively utilised in aerospace, biomedical, and precision engineering applications due to their distinctive functional properties, including superelasticity and the shape memory effect. However, their poor machinability and strong sensitivity to cutting conditions render it challenging to obtain surfaces with stable functional integrity. The present study investigates the impact of diverse cooling methodologies—namely dry machining, minimum quantity lubrication (MQL) and cryogenic cooling employing liquid nitrogen (LN2)—on the three-dimensional (3D) surface topography of NiTi alloy following milling. A comprehensive set of three-dimensional surface roughness parameters was employed to quantify the surface geometry and evaluate its potential functional performance. The findings indicated that both dry milling and MQL yielded significantly divergent surface parameters, suggesting unstable surface formation, which may potentially compromise component durability. MQL frequently resulted in topographies that were functionally detrimental and characterised by high parameter dispersion. In contrast, cryogenic cooling (LN2) resulted in the most homogeneous surface topography, as evidenced by the lowest dispersion of 3D roughness indicators. To strengthen the analysis, a Taguchi–TOPSIS multi-criteria optimisation was also performed on ten 3D surface parameters, enabling an integrated ranking of all machining trials. The optimisation process confirmed the superior performance of cryogenic machining, with LN2 conditions achieving the highest overall surface quality index. Full article

Hard turning has emerged as a cost-effective and flexible alternative to conventional grinding for machining hardened steels such as AISI 4340. However, its performance is significantly influenced by the choice of cooling and lubrication strategies, as well as the condition of the cutting tool. Inadequate thermal management and tool wear can lead to elevated cutting forces, high interface temperatures, degraded surface quality, and an altered microstructure. This study investigates the machinability performance of AISI 4340 alloy steel (50 HRC) using CVD-coated carbide tools under two distinct cooling/lubrication environments: minimum quantity lubrication (MQL) and cryogenic cooling (LN₂). Experiments were conducted at the beginning and end of tool life with both environments to capture the influence of tool wear on key performance indicators, including cutting force, chip temperature, surface roughness, and microstructural integrity. Results indicate that LN₂ cooling outperformed MQL in mitigating thermal loads and maintaining surface quality, particularly under worn tool conditions. LN₂ reduced cutting forces by up to 37.10%, chip temperature by 56.68%, and surface roughness by 36.95% compared to MQL. Microstructural analysis revealed significantly thinner deformation and white layers under LN₂, suggesting improved subsurface integrity. These findings highlight the potential of LN₂ cooling for enhancing the machinability of hard turning operation and improving overall performance in industrial applications. Full article

High temperatures generated during machining can lead to undesirable outcomes such as surface deterioration, subsurface damage, tool wear, and shortened tool life. Effective heat removal from the cutting zone is therefore essential for maintaining process stability and part quality. Conventional cooling systems typically apply a constant amount of cutting fluid without considering the actual temperature in the cutting zone, which may result in unnecessary coolant use and inefficient temperature control. This study introduces an innovative machining approach that integrates machine learning techniques to estimate the optimal lubrication interval and maintain the desired cutting temperature. The proposed system dynamically adjusts coolant application based on real-time temperature data and machining parameters, preventing excessive or insufficient cooling. Comparative analyses show that the new system reduces coolant consumption by 22.5 mL per minute compared with conventional cooling and by 2.5 mL per minute compared with minimum quantity lubrication (MQL). This improvement corresponds to an annual reduction of approximately 12.3 T of CO₂e emissions. The results demonstrate that the developed system enables machining at the optimum temperature, enhancing tool life, surface quality, and energy efficiency while significantly lowering environmental and health impacts associated with cutting fluids. The integration of machine learning also supports automated decision-making in smart manufacturing environments, reducing operator dependency and contributing to sustainable and economically efficient production. Full article

The formation of rivet holes is a critical step in aeronautical assembly, as hole quality directly influences the fatigue resistance and structural reliability of riveted joints. Nickel-based alloys, such as Inconel 625, present additional challenges due to their poor machinability and the stringent surface integrity requirements imposed by the aerospace sector. This study investigates innovative and sustainable drilling strategies for rivet hole preparation, focusing on the comparative performance of two environmentally friendly cooling and lubrication methods: minimum quantity lubrication with an eco-friendly fluid (MQL-Eco) and cold compressed air (CCA). A comprehensive experimental campaign was carried out to analyze the combined effects of spindle speed, S, feed rate, f, and cooling method, R, on hole surface roughness parameters (Ra and Rz). These values are measured inside the drilled hole using optical scanner 3D equipment. Statistical tools, including analysis of variance (ANOVA) and response surface methodology (RSM), were employed to identify the most significant factors and optimize cutting conditions. The results reveal that the interaction between spindle speed and coolant type is the dominant contributor to surface roughness variability, with MQL-Eco consistently achieving values within the aeronautical standard range (Ra = 0.8–1.6 µm), and the coolant factor is the second cause of variability in both roughness Ra and Rz. Moreover, correlations between roughness parameters and tool wear confirm the relevance of sustainable cooling methods in extending tool life while maintaining compliance with aerospace quality requirements. The findings demonstrate that innovative eco-friendly drilling approaches can effectively replace conventional lubrication, offering a viable pathway towards greener manufacturing practices in metal forming and joining technologies. Full article

Because of their exceptional strength, corrosion resistance, and low weight, materials such as titanium, aluminum, and others are becoming increasingly popular. The application scope of additive manufacturing (AM) in the aerospace sector continues to expand. Because of its high performance and low coefficient of thermal expansion, AlSi10Mg processed by laser-based powder bed fusion (LPBF) is becoming increasingly popular in lightweight aerospace component design. Nonetheless, the AM technique has a number of benefits; poor surface quality is the only drawback, necessitating post-processing. This study aims to focus on the machinability of AlSi10Mg under three distinct environmental conditions (dry, minimum quantity lubrication (MQL), and SL-MQL). The experimental investigations were centered on chip morphology, flank wear (Vb), surface roughness (Ra), and cutting temperature (Tc). SL-MQL reduced the roughness by 53–57% over dry machining and 23–29% over MQL condition, and in a similar way lessened the flank wear by 36–40% over dry machining and 12–15% over MQL condition. In addition, to check the predictive accuracy and optimize machining parameters, four machine learning models were used: Gaussian Process Regression (GPR), Bagging, Multilayer Perceptron (MLP), and Random Forest (RF). In both the training and testing stages, MLP consistently demonstrated superior performance across all parameters in comparison to other algorithms, achieving high levels of accuracy and low error rates. Full article

The field of hard milling has recently witnessed growing interest in environmentally sustainable machining practices. Among these, Minimum Quantity Lubrication (MQL) has emerged as an effective strategy, offering not only reduced environmental impact but also economic benefits and enhanced cooling performance compared to conventional flood cooling methods. In hard milling operations, cutting temperature is a critical factor that significantly influences the quality of the finished component. Proper control of this parameter is essential for producing high-precision workpieces, yet measuring cutting temperature is often complex, time-consuming, and costly. These challenges can be effectively addressed by predicting cutting temperature using advanced Machine Learning (ML) models, which offer a faster and more efficient alternative to direct measurement. In this context, the present study investigates and compares the performance of Conventional Minimum Quantity Lubrication (CMQL) and Graphene-Enhanced MQL (GEMQL), with sesame oil serving as the base fluid, in terms of their effect on cutting temperature. The experiments are structured using a Taguchi L36 orthogonal array, with key variables including cutting speed, feed rate, MQL jet pressure, and the type of cooling applied. Additionally, the study explores the predictive capabilities of various advanced ML models, including Decision Tree, XGBoost Regressor, K-Nearest Neighbor, Random Forest Regressor, and CatBoost Regressor, along with a Hybrid Stacking Machine Learning Model (HSMLM) for estimating cutting temperature. The results demonstrate that the GEMQL setup reduced cutting temperature by 36.8% compared to the CMQL environment. Among all the ML models tested, HSMLM exhibited superior predictive performance, achieving the best evaluation metrics with a mean absolute error of 3.15, root mean squared error (RMSE) of 5.3, mean absolute percentage error of 3.9, coefficient of determination (R²) of 0.91, and an overall accuracy of 96%. Full article

Electron beam melting (EBM) is an additive manufacturing method that enables the manufacturing of metallic parts. EBM-printed parts require post-processing to meet the surface quality and dimensional accuracy requirements. Machining is one approach that is beneficial for achieving these requirements. However, during machining, particles are emitted and can affect the environment and the operator’s health. This study aims to investigate the concentration of particles emitted during the milling of 3D-printed Ti6Al4V alloy produced by EBM. First, the influence of machining speed and cutting fluids, namely flood and minimum quantity lubricant (MQL), on particle emissions was statistically investigated. Then, the standby time required for the operator to safely open the machine door and interact with the machine within the machining area was studied. In this regard, two scenarios were proposed. In the first scenario, the machine door is open immediately after machining, and the operator waits until the particle concentration is acceptable. In the second, the machine door will be opened only when the particle concentration is acceptable. Statistical findings revealed that cutting fluids have a significant impact on particle emissions, exhibiting distinct patterns for both fine and coarse particles. Irrespective of the scenario, MQL results in higher particle concentration peaks and larger particle sizes, and the operator needs a longer standby time before interacting with the machine. For instance, the standby time in MQL is 328% more than that of the flood system. This study provides insight into sustainable manufacturing by taking into account social factors such as worker health and safety. Full article

Achieving optimal lubrication during machining processes, particularly turning of stainless steel, remains a significant challenge due to dynamic variations in cutting conditions that affect tool life, surface quality, and environmental impact. Conventional Minimum Quantity Lubrication (MQL) systems provide fixed flow rates and often fail to adapt to changing process parameters, limiting their effectiveness under fluctuating thermal and mechanical loads. To address these limitations, this study proposes an ambient-aware adaptive Auto-Tuned MQL (ATM) system that intelligently controls both nanofluid concentration and lubricant flow rate in real time. The system employs embedded sensors to monitor cutting zone temperature, surface roughness, and ambient conditions, linked through a feedback-driven control algorithm designed to optimize lubrication delivery dynamically. A Taguchi L9 design was used for experimental validation on AISI 304 stainless steel turning, investigating feed rate, cutting speed, and nanofluid concentration. Results demonstrate that the ATM system substantially improves machining outcomes, reducing surface roughness by more than 50% and cutting force by approximately 20% compared to conventional MQL. Regression models achieved high predictive accuracy, with R-squared values exceeding 99%, and surface analyses confirmed reduced adhesion and wear under adaptive lubrication. The proposed system offers a robust approach to enhancing machining performance and sustainability through intelligent, real-time lubrication control. Full article

Inconel 625 is a nickel-based superalloy widely applied in aerospace and energy sectors due to its high strength and corrosion resistance. However, its poor machinability remains a significant challenge in precision manufacturing. This study investigates the influence of tool geometry and cutting parameters on surface roughness of Inconel 625 during turning operations under the minimum quantity lubrication (MQL) conditions. Experiments were carried out using three types of cutting inserts with distinct chip breaker geometries while systematically varying the cutting speed, feed rate, and depth of cut. The results were statistically analyzed using analysis of variance (ANOVA) to determine the significance of individual factors. The findings reveal that both the type of cutting insert and the process parameters have a considerable effect on surface roughness, which is the key output examined in this study. Cutting forces and chip type were examined to provide complementary insights and improve understanding of the observed relationships. Based on the results, an optimal set of cutting data was proposed to achieve a required surface roughness during the turning of Inconel 625 with MQL. Furthermore, a practical algorithm was developed to support the selection of cutting parameters in industrial applications. Analysis of the results showed that a cutting insert with a 0.4 mm corner radius achieved the required surface finish (Rz ≤ 0.4 µm). Furthermore, the analysis revealed a significant effect of the thermal properties of Inconel 625 on machining results and chip geometry. Full article

The move toward environmentally friendly methods in the global manufacturing sector has led to the use of minimum quantity lubrication (MQL) as an eco-friendly alternative to traditional flood cooling. However, the natural limits of MQL in high-performance settings have led to the use of nanotechnology, which has resulted in the creation of nanofluids, engineered colloidal suspensions that significantly improve the thermophysical and tribological properties of base fluids. This paper gives a complete overview of the latest developments in nanofluid technology for use in machining. It starts with the basics of MQL and the rules for making, describing, and keeping nanofluids stable. The review examines the application and effectiveness of single and hybrid nanofluids in various machining processes. It goes into detail about how they improve tool life, surface integrity, and overall efficiency. It also examines the benefits of integrating nanofluid-assisted MQL (NMQL) with more advanced and hybrid systems, including cryogenic cooling (cryo-NMQL), ultrasonic atomization, electrostatic–magnetic assistance, and multi-nozzle delivery systems. The paper also gives a critical look at the main problems that these technologies face, such as the long-term stability of nanoparticle suspensions, their environmental and economic viability as measured by life cycle assessment (LCA), and the important issues of safety, toxicology, and disposal. This review gives a full picture of the current state and future potential of nanofluid-assisted sustainable manufacturing by pointing out important research gaps, like the need for real-time LCA data, cost-effective scalability, and the use of artificial intelligence (AI) to improve processes, and by outlining future research directions. Full article

The presence of a minimum quantity lubrication (MQL) under the conditions of a burnishing process can contribute to an improvement in the process performance by reducing the heights of the resulting surface asperities, by decreasing the temperature values, and by diminishing the size of the burnishing force components. On the other hand, there are situations in which it is possible to increase the service life of the parts made of EN AW-2007 aluminum alloy by applying a burnishing process. To verify how the results of applying a burnishing process applied to cylindrical specimens in the aluminum alloy when using and not using a minimum quantity lubrication, an experimental research based on a planned variation between certain limits of the values of the peripheral speed and the feed rate has been conceived and materialized. The experimental results were processed mathematically. It has been found that by using the minimum quantity of mineral oil type Valona MS7023 HC, it was possible to reduce the value of the Sa roughness parameter by up to 18%, a decrease in temperature by about 20 °C, and the size of the burnishing force by up to 45%. Full article

The increasing use of carbon fiber reinforced polymer (CFRP) composites in industries such as aerospace, due to its high strength-to-weight ratio, durability, and resistance to corrosion has led to a growing demand for more efficient machining processes. However, the multilayered structure of CFRP composites, composed of densely packed fibers, presents significant challenges during machining. Additionally, when cutting fluids are used to improve effective cooling and lubrication, the material tends to absorb the fluid, causing damage and leading to problem of weaking of composite structure. To address these issues, this study compares ultrasonic-assisted drilling (UAD) and minimum quantity lubrication (MQL) techniques with conventional drilling (CD) and dry cutting to improve the performance of CFRP composite drilling. The results show that using UAD and MQL together reduced thrust force by up to 27%, improved surface roughness inside the holes by up to 31%, reduced improved hole diameter, cylindricity, roundness, and delamination. Full article

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