Minimum Quantity Lubrication Conditions Research Articles

Effect of cutting process using cutting insert of grade UTi20T

In the metal-cutting industry, the precision of the metal-cutting process is of paramount importance. Errors in the metal-cutting process not only lead to damage to the cutting tool but also result in the production of low-quality materials. The incorporation of insert materials in the cutting process is aimed at maintaining cutting precision and achieving superior results. This research seeks to investigate the impact of the cutting process utilizing grade KC5410 cutting insert under minimum quantity lubrication (MQL) Conditions. In this study, machining tests were conducted using the ALPHA 1350S 2-axis computer numerical control (CNC) lathe machine under MQL conditions, employing cutting tool inserts UTi20T supplied by Mitsubishi. Two types of tools were utilized in the cutting process, namely UTi20T. Critical aspects such as cutting force, total power consumption, surface roughness, and tool life were analyzed to provide comprehensive insights into the efficiency of the cutting process. The findings of this study significantly contribute to the understanding of how the integration of Grade KC5410 cutting inserts under MQL conditions can enhance the overall efficiency of metal-cutting operations. The successful machinability assessment was conducted by implementing sustainable machining practices.

Investigating the Wear Mechanism of TiAlN/TiN PVD-Coated WC Inserts in End Milling of Incoloy 925 Under Synergy of Bio-Degradable Fatty Acids and Nano-Metallic Solid Lubricant

Abstract Rapid tool wear in machining difficult-to-cut material is a significant challenge since it is related to tool cost, surface integrity of machined components, and power consumption. The reduction in tool wear may be accomplished by cutting fluids, especially in modern biodegradable fatty acids. Because of its high lubricity, coconut oil is the best choice for lubricating fatty acids. However, this oil has poor heat transfer properties. Hence, to further improve its properties, copper (Cu) nanoparticles (NPs), which have a high thermal conductivity to cost ratio compared to other nanoparticles in addition to anti-frictional and anti-wear properties (due to self-repairing properties caused by their low shear strength and easy deposition on contacting surfaces), have been dispersed in coconut oil, thus creating a nano-lubricant (nanofluid). It is found that synthesized nano-lubricants perform better in thermo-physics and tribology than pure coconut oil. Further, research has been carried out on the combined effects of coconut oil and 0.3 vol% Cu-NPs on Incoloy 925's end milling performance and tool wear mechanism (viz., coating delamination, adhesion, abrasion, and oxidation). It is found that tool wear-rate is reduced, which in turn increases tool life, due to effective cooling and lubrication when nanofluid-based minimum quantity lubrication (NMQL)-assisted machining is performed rather than dry and pure oil-based minimum quantity lubrication (MQL) conditions. Consequently, energy consumption in machining and surface quality of machined components are improved.

Study on Surface Characteristics and Work Hardening of SiCp/Al Composites by SCCO2-MQL Combined with Ultrasonic Vibration Milling

This study investigated the milling of SiCp/Al composite materials using Polycrystalline Diamond (PCD) tools under various machining conditions, including dry cutting conditions, supercritical carbon dioxide (SCCO2) conditions, supercritical carbon dioxide cooling with minimum quantity lubrication (SCCO2-MQL) conditions, ultrasonic vibration conditions, and supercritical carbon dioxide cooling with minimum quantity lubrication combined with ultrasonic vibration conditions. The objective was to compare the surface roughness and morphology of the materials under different machining conditions. Furthermore, under dry cutting conditions and SCCO2-MQL combined with ultrasonic vibration, the effects of different milling parameters on the surface roughness and morphology of SiCp/Al composite materials were investigated through a univariate experiment. Microhardness tests were carried out on the machined workpieces to explore the influence of process conditions and milling parameters on work hardening. The experimental results indicate that among all the tested machining conditions, the SCCO2-MQL in combination with the ultrasonic vibration process significantly reduced the surface roughness of the material. When the milling speed was increased from 40 m/min to 120 m/min, both the surface roughness and the degree of work hardening first increased and then decreased. As the feed rate or cutting depth increased, the degree of work hardening also increased. Therefore, under SCCO2-MQL combined with ultrasonic vibration conditions, it is recommended to use a milling speed of more than 60 m/min and avoid using high feed rates and cutting depths in order to optimize the machining performance.

Tool wear and surface roughness characteristics in sustainable machining of additively manufactured titanium alloys

Titanium is widely acknowledged as a challenging metal due to its inherent characteristics. Manufacturers often encounter challenges such as tool wear, reduced tool lifecycle, and increased cutting heat when working with Ti64. This research investigates the cutting characteristics of additively manufactured (AMed) Ti64 under dry cutting, Minimum Quantity Lubrication (MQL), Cryogenic Carbon dioxide (CO2), and a hybrid approach combining MQL and CO2 conditions. Surface roughness, flank wear, temperature, chip morphology, and microhardness were analyzed to assess the impact of cooling strategies. Results indicate that the hybrid approach outperforms individual methods, showing superior surface finish and reduced tool wear. Machined Surface roughness (Ra) measurements reveal a substantial improvement in the hybrid condition, reducing Ra values by 62.44–67.02%, 35.65–41.38%, and 18.68–27.59% compared to dry, MQL, and CO2. Tool wear assessments exhibit significantly lower flank wear values in the hybrid condition, emphasizing the synergistic benefits of lubrication and cryogenic cooling. This research provides valuable insights into tailoring cooling strategies for optimal precision in machining AMed-Ti64 material, which is crucial for achieving high-quality manufacturing outcomes.

Design and tribological performance of graphene nanofluids dispersed by dragging effect of bicomponent gelator

Nanofluids have excellent lubrication and high thermal conductivity. However, the agglomeration and sedimentation produced by the large surface energy of nanoparticles in base liquid threaten the long-term dispersion stability and impact the wide application of nanofluid. In this work, based on the self-assemble behavior and continuous network structure formed by low molecular weight organic gelator, the uniform clusters were formed through regulating the kinetics behavior in the gelling process. The dragging effect was demonstrated by oleic acid - sodium dodecyl sulfate (OA-SDS) bicomponent gelator and graphene oxide (GO) nanosheets. The results showed that GO nanofluids dispersed by OA-SDS were stable for more than 12 months. The well-dispersed GO nanofluid exhibited better anti-friction and anti-wear properties under both immersion and electrostatic minimum quantity lubrication conditions. Moreover, the lower contact angle, surface tension and droplet size of nanofluids after charging improved the wettability on the frictional interface. The GO adsorption film formed on the friction interface protected the tribochemical reaction film of iron oxide and prevented the occurrence of sintering of base oil.

A sustainable cooling/lubrication method focusing on energy consumption and other machining characteristics in high-speed turning of aluminum alloy

Understanding energy implications and machining performance standards is vital as industries move toward sustainability. Modern machining techniques including high-speed turning are used in the study on aluminum alloy materials. Therefore, this study focuses on the energy consumption and in-depth analysis of machinability criteria in the context of eco-friendly high-speed turning of aluminum alloy. The experiments were performed under ecofriendly cutting conditions dry and minimum quantity lubrication (MQL) conditions and the intricate relationship between machining parameters and cooling conditions was investigated in terms of energy consumption, tool wear, surface roughness and chips morphology. The result reveal that the Ra values exhibited 2.4 times increase in dry machining and an average increase of 2.3 times in MQL machining when the feed rate was increased from 0.2 to 0.4 mm/rev. Moreover, the MQL cooling is helpful in lowering the energy consumption as well as tool wear and surface roughness during the machining operations. MQL machining resulted in a 35% reduction in flank wear and 20% reduction in energy consumption compared to dry machining.

Machine learning models for prediction and classification of tool wear in sustainable milling of additively manufactured 316 stainless steel

Machine learning (ML) is a subfield of Artificial Intelligence (AI) that uses data, learns the hidden pattern from the data, and makes predictions for future instances with greater accuracy and prediction capabilities, not hallucination (overfitting) with the current data. The application of ML is quite popular in the machining sector because the ML models can be used to predict tool wear, surface roughness and other important machining aspects. With this aim, the present work evaluates the tool wear and class separation by predicting the variation of flank wear (Vb) as a test dataset. A predictive model is proposed for utilizing minimum quantity lubrication (MQL), cryogenic, and MoS2+MQL conditions. Predictions are made using several machine learning (ML) methods, including linear regression, support vector machine, random forest, and multilayer perceptron. The study's findings show that Multi-layer perceptron (MLP) is superior to other methods in classification, with a prediction accuracy of over 95% on average across both training and testing datasets. Even with limited information, the proposed method can help classify situations and recommend the best course of action for machining.

Comparison of turning process performance using modified cutting inserts under minimum quantity lubrication

ABSTRACT While machining of high-strength alloy steel materials, poor surface quality and decreased tool life are result by an increase in the cutting zone temperature close to the machining region. To overcome this, surface texture with green manufacturing environment is an alternate path to enhancing machining performance. In the present work, novel Parallel Grooved Textured Tools (PGT) was developed and studied the performance of so developed tools. Further, the Untextured Tool (UT) and PGT performance was compared while machining EN24 alloy steel under Minimum Quantity Lubrication (MQL) condition. It was perceived from result that textured tools reduced the cutting temperature (CT), rake wear (RW), flank wear (FW), surface roughness (Ra) and cutting force (CF) up to 18.96%, 18.79%, 24.31%, 12.69%, and18.96% respectively compared to UT.

Machining Performance of Ti6Al4V Nano Composites Processed at Al2O3 Nano Particles Mixed Minimum Quantity Lubrication Condition

Aim:: In this research work, an attempt was made to machine Ti6Al4V nano composites utilizing Al2O3 mixed nano fluid at minimum quantity lubrication condition, in which experiments were designed using the L16 orthogonal array, whereas Material Removal Rate, Surface Roughness, machining force and power were recorded as responses. Method:: The nano composites were fabricated using the stir casting technique and the nano particles were synthesized using the sol-gel technique. the microstructure revealed that the homogeneous dispersion of particles with dendric arms. Increased cutting speed and feed lead to more tool wear, which in turn causes a decrease in surface quality and an increase in surface roughness. Result:: Larger areas of cut are often the consequence of higher feed rates, which increases the amount of friction between the work piece and the cutting edge. The machining force increases when the feed rate is increased. A higher feed rate produces a large volume of the cut material in a given length of time in addition to having a dynamic impact on the cutting forces. Conclusion:: It also results in a corresponding increase in the typical contact stress at the tool chip interface and in the tool chip contact zone.

Effects of Machining Parameters on Total Cutting Force in Hard Turning Process of Hardened 90CrSi Steel Using Carbide Insert with MoS2 Nanofluid MQL

Minimum Quantity Lubrication (MQL) is widely used in machining, especially hard machining. However, this method has many limitations regarding cooling and lubrication capabilities. In recent years, nano-cutting oils have been researched and applied in machining to improve the lubrication and cooling efficiency of the MQL method. In this research, the machinability of 90CrSi steel with carbide inserts using nanofluid with different concentrations (1%, 1.5%, and 3%) of molybdenum disulfide (MoS2) nanoparticles in emulsion oil was evaluated by applying Box-Behnken optimization model. Carbide inserts commonly used to machine the low hardness steel (below 35HRC) have been used to cut hardened steel with high hardness (59-62HRC) by applying the MQL technology with MoS2 nanofluid. A mathematical model was also used to predict the cutting resultant force value in the hard turning process under MQL conditions using MoS2 nanofluid. The obtained results indicate that the total cutting force reached the minimum value (328 N) with the MoS2 nanoparticle concentration of 1.985%, a cutting speed of 80 m/min, and a feed rate of 0.138 mm/rev. In particular, the work has also provided technical guides for different machining conditions.

Investigation of ultrasonic vibration assisted orthogonal turning under dry and minimum quantity lubrication conditions and performing sustainability analyses

Recently, increasing pressure on industries to innovate and adopt sustainable techniques has sparked investigations into machining strategies that have minimal carbon emissions, minimal energy consumption by machine tools, and low machining costs. The primary aim of this paper is to identify the machining conditions that can fulfill the criteria of clean production and sustainable chip forming for low-alloy steel which is extensively used in the many industrial field. To achieve this goal, experiments were done under both dry and Minimum Quantity Lubrication (MQL) conditions, aided by ultrasonic vibration assisted hybrid machining conditions at various cutting speeds (100 and 140 m/min) and uncut chip thicknesses (0.1, 0.14, and 0.18 mm). The study was conducted in two stages. In the first part, machining experiments were performed afterward, cutting forces, cutting temperature, chip morphology, surface roughness, and texture were examined. The second part of this study evaluates the sustainability of various machining parameters and cutting environments. The analysis of economic feasibility or rather the total machining costs were estimated by evaluating the costs incurred due to investment of machine tools, lubricant/coolant delivery systems and ultrasonic vibration system costs, waste processing and management costs, cutting tool and labor costs, cost of cutting fluid and energy consumed. On the other hand, environmental viability was evaluated by estimating the carbon emissions (CE) due to the power consumed, material utilization (i.e., the cutting tool and cutting fluid), and the waste processing and management (i.e., disposal of cutting tool, cutting fluid and chip recycling). Results indicate that both dry and MQL ultrasonic-assisted machining processes resulted in improved chip breakability, and produced short-comma chips. Additionally, it reduced cutting force by 28% and the cutting temperature was lowered by 30 %. Besides, Minimum Quantity Lubrication + Ultrasonic Vibration Assisted Machining (MQL + UVAM) hybrid method prevented surface defects and reduced average surface roughness by 24%–35% compared to dry conditions. While utilizing the MQL + UVAM method, the overall machining cost decreased in the range of about 20–30 % as compared to dry and dry + UVAM conditions. From an environmental standpoint, the MQL + UVAM hybrid machining process was found to be a significant contribution to the overall CE due to use of cutting fluids and increased machine tool utilization. This paper contributed to the understanding of sustainability and cleaner production to mitigate CO2 emissions by using the MQL + UVAM hybrid method.

Performance of conventional and wiper CBN inserts under various cooling conditions in hard turning of AISI 52100 steel

Abstract Cryogenic cooling and minimum quantity lubrication (MQL) are regarded as effective green manufacturing techniques since they eliminate the excessive utilization of conventional cutting fluids (CCFs) that are known to have adverse impacts on both the environment and human health. Moreover, these methods have promising effects on surface integrity in machining hard-to-cut materials. This study investigates the impact of hybrid and cryogenic lubri-cooling methods on surface integrity (surface roughness, microhardness, and residual stresses) in hard turning of AISI 52100 bearing steel (62 HRC) with conventional and wiper CBN inserts. For that purpose, a precooling-based hybrid (a combination of precryogenic cooling of the workpiece and MQL) and direct cryogenic cooling techniques were used for comparison during the hard turning tests. The tests were carried out at a constant cutting speed (200 m/min), feed (0.1 mm∙rev−1), and depth of cut (0.1 mm) under dry, carbon dioxide (CO2), liquid nitrogen (LN2), CO2 + MQL, and LN2 + MQL conditions. The results show that hybrid lubri-cooling condition (CO2 + MQL) improved the surface quality and increased the compressive residual stresses at the machined surface when wiper inserts were employed. On the other hand, effective cooling under direct CO2 condition provided better surface integrity when machining with conventional inserts.

Tool wear evolution and chip formation of the Ti-6Al-4V end milling under cryogenic cooling and minimum quantity lubrication conditions

Tool wear evolution and chip formation of the Ti-6Al-4V end milling under cryogenic cooling and minimum quantity lubrication conditions

Real-time monitoring and measurement of energy characteristics in sustainable machining of titanium alloys

The development of cutting-edge monitoring technologies such as embedded devices and sensors has become necessary to ensure an industrial intelligence in modern manufacturing by recording machine, process, tool, and energy consumption conditions. Similarly, machine learning based real time systems are popular in the context of Industry 4.0 and are generally used for predicting energy needs and improving energy utilization efficiency and performance. In addition, sustainable and energy-efficient machining technologies that can reduce energy consumption and associated negative environmental effects have been the latest topic of much study in recent years. Concerning this regard, the present work firstly deals with the real time monitoring and measurement of energy characteristics while machining titanium alloys under dry, minimum quantity lubrication (MQL), liquid nitrogen (LN2) and hybrid (MQL + LN2) conditions. The energy characteristics at different stages of machine tools were monitored with the help of a high end energy analyser. Then, the energy signals from each stage of machining operation were predicted and classified with the help of different machine learning (ML) models. The experimental results showed that MQL, LN2, and hybrid conditions decreased the total energy consumption by averagely 2.6 %, 17.0 %, and 16.3 %, respectively, compared to dry condition. The ML results demonstrated that the accuracy of the random forest (RF) approach obtained higher efficacy with 96.3 % in all four conditions. In addition, it has been noticed that the hybrid cooling conditions are helpful in reducing the energy consumption values at different stages.

Prediction and classification of tool wear and its state in sustainable machining of Bohler steel with different machine learning models

Machine learning has numerous advantages, especially in the rapid digitization of the manufacturing industry that combines data from manufacturing processes and quality measures. Predictive quality allows manufacturers to make informed predictions about the quality of their products by analyzing data gathered during production. The quality of the machining, the total cost and the computation time need to be improved using contemporary production processes. With this concern, a series of experiments were carried out on Bohler steel both in dry, Minimum Quantity Lubrication (MQL) and nano-MQL conditions in varying quantities to explore the tool wear. In comparison to dry conditions, the utilization of MQL in machining processes demonstrates significantly enhanced efficacy in mitigating flank wear. The reduction in flank wear ranges from around 5% to 20% to 25%, contingent upon the application of MQL on the flank face, rake face, or both faces simultaneously. After that, the results of the tests were evaluated with the models of machine learning (ML) to determine which environment was optimal for cutting under both real and artificial circumstances.

Analysis of Grinding Flow Field under Minimum Quantity Lubrication Condition

The flow field around the grinding wheel prevents grinding fluid from entering the grinding area, which deteriorates machining conditions and reduces the quality of the workpiece. MQL (Minimum Quantity Lubrication) grinding uses compressed gas to improve the ability of the grinding fluid to pass through the flow field of the grinding wheel so that a small amount of the grinding fluid can meet the cooling and lubrication requirements and reduce the cost of using grinding fluid. In this study, we investigated the flow field of grinding wheels under MQL conditions and obtain the rules that influence factors such as grinding fluid flow rate, grinding wheel linear speed and compressed gas pressure affect the flow rate of grinding fluid. The simulation method was used to simulate the flow field of the grinding wheel and the trajectory of the grinding fluid droplets. The simulation results show that these factors are important ones affecting the effective flow rate of grinding fluid. Meanwhile, the experimental results verify that these factors have the same influence pattern on the effective flow rate and grinding temperature.

A Comparative Performance Investigation of Single- and Double-Nozzle Pulse Mode Minimum Quantity Lubrication Systems in Turning Super-Duplex Steel Using a Weighted Pugh Matrix Sustainable Approach

This study investigates the performance comparison of machining of UNS S32750 super-duplex stainless steel under single- and double-nozzle pulse mode minimum quantity lubrication (MQL) conditions. The pulse mode MQL system delivers lubricant pulses at specific intervals. The Taguchi L9 design, with three factors and their three levels, was taken to perform the CNC turning experiments under both single-nozzle and double-nozzle MQL cooling environments. The surface roughness (Ra), tool-flank wear (VB), tool-flank temperature (Tf), power consumption (Pc), and material removal rate (MRR) are evaluated and compared as performance indicators. In comparison to single-nozzle MQL, the responses of Ra, VB, Tf, and Pc were found to be decreased by 11.16%, 21.24%, 7.07%, and 3.16% under double-nozzle conditions, respectively, whereas MRR was found to be 18.37% higher under double-nozzle conditions. The MQL pulse time was found to be an important variable that affects Ra, VB, Tf, and MRR significantly. Under both cooling scenarios, common wears such as abrasion, built-up edges, adhesion, and notch wear are detected. Furthermore, the Pugh matrix-based sustainability evaluation results revealed that the double-nozzle MQL technique was superior to single-nozzle MQL, achieving improved sustainability for machining super-duplex stainless steel.

An Experimental Investigation of the Effects of Dressing and Grinding Parameters on Sustainable Grinding of Inconel 738 Used for Automated Manufacturing

The significant effect of the dressing process on the surface of the grinding wheel (GW) and the need to provide an optimal dressing condition are the requirements of reduction machining time and energy consumption in the sustainable grinding process. In this study, for the first time, the results of changes in the parameters of the dressing process and changes in the topography of the grinding surface on the surface roughness of the Inconel 738 have been presented using single-edge and four-edge diamond dressers. The use of minimum quantity lubrication (MQL) and wet condition are other variables in this study to reduce the consumption of cutting fluid and prevent its destructive effects on the environment. The results indicate that the MQL technique increases the grinding performance of Inconel 738 by reducing ground workpiece surface roughness and decreasing the coolant–lubricant consumption comparing to the conventional wet grinding process. Additionally, it has been found from the experimental results that applying a single-edge dresser generates finer topography on the grinding wheel and, consequently, has a better surface finish in the grinding process compared to the multipoint diamond dressing tool with the same dressing and grinding parameters. In other words, increasing the dressing feed rate during dressing of the grinding wheel using a multipoint dresser makes a finer wheel surface topography and as a result decreases the surface roughness of the ground workpiece compared to a single-edge dresser. With multipoint diamond tools, the grinding performance during the life of the dressing tool also tends to remain more consistent, which is a definite advantage in automated production. Therefore, application of a multipoint dresser leads to a reduction in dressing time and increased production capability.

Soft computing for sustainable drilling of AISI 316L stainless steel under formulated neem oil minimum quantity lubrication condition

Soft computing for sustainable drilling of AISI 316L stainless steel under formulated neem oil minimum quantity lubrication condition

Milling machinability analysis of GW63K rare-earth magnesium alloys based on the concept of clean cutting

GW63K rare-earth magnesium (Mg-RE) alloys have been widely used in various advanced industries for their excellent mechanical properties. However, the machinability of GW63K alloys under clean cutting conditions still needs to be better understood. The novelty of this paper lies in addressing the influences of varying cutting parameters on the milling responses of GW63K Mg-RE alloys as well as the underlying mechanisms. A full-factorial design of experiments was conducted under dry cutting and minimum quantity lubrication (MQL) conditions. A three-tooth milling cutter with a diameter of 12 mm was used during the investigation. The critical milling outputs involving cutting forces, machining temperatures, and surface quality attributes were carefully analyzed. The results indicate that the cutting force highly depends on the feed per tooth, and the cutting temperature is raised with the increased cutting speed and feed per tooth. The MQL is found capable of reducing the milling temperature by 10.8 °C on average compared with dry cutting. The MQL shows benefits in improving the cutting performances of tools and surface quality of Mg-RE alloys by reducing the tool-workpiece friction coefficient and enhancing the heat dissipation conditions of the chip contact surface. In addition, lower feed per tooth and higher cutting speeds under MQL conditions are suggested for the milling of Mg-RE alloys while ensuring processing efficiency.