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In this article, a grinding force model, which is on the basis of cutting process of single abrasive grains combined with the method of theoretical derivation and empirical formula by analyzing the formation mechanism of grinding force, was established. Three key factors have been taken into accounts in this model, such as the contact friction force between abrasive grains and materials, the plastic deformation of material in the process of abrasive plowing, and the shear strain effect of material during the process of cutting chips formation. The model was finally validated by the orthogonal grinding experiment of powder metallurgy nickel-based superalloy FGH96 by using the electroplated CBN abrasive wheel. Grinding force values of prediction and experiment were in good consistency. The errors of tangential grinding force and normal grinding force were 9.8% and 13.6%, respectively. The contributions of sliding force, plowing force and chip formation force were also analyzed. In addition, the tangential forces of sliding, plowing and chip formation are 14%, 19% and 11% of the normal forces on average, respectively. The pro-posed grinding force model is not only in favor of optimizing the grinding parameters and improving grinding efficiency, but also contributes to study some other grinding subjects (e.g. abrasive wheel wear, grinding heat, residual stress).

Corundum abrasives with good chemical stability can be fabricated into various free abrasives and bonded abrasive tools that are widely used in the precision machining of various parts. However, these abrasives cannot satisfy the machining requirements of difficult-to-machine materials with high hardness, high strength, and strong wearing resistance. Although superhard abrasives can machine the above-mentioned materials, their dressing and manufacturing costs are high. By contrast, ceramic corundum abrasives fabricated by sol–gel method is a cost-effective product between conventional and superhard abrasives. Ceramic corundum abrasives exhibit self-sharpening and high toughness. In this review, the optimization methods of ceramic corundum abrasive properties are introduced from three aspects: precursor synthesis, particle shaping, and sintering. Firstly, the functional mechanism of seeds and additives on the microstructural and mechanical properties of abrasives is analyzed. Specifically, seeds can reduce the phase transition temperature and improve fracture toughness. The grain size and uniformly dense structure can be controlled by applying an appropriate amount of multicomponent additives. Then, the urgent need of engineering application and machinability of special shape ceramic corundum abrasives is reviewed, and three methods of abrasive shaping are summarized. The micromold replication technique is highly advanced and can be used to prepare functional abrasives. Additionally, the influence of a new sintering method, namely, two-step sintering technique, on the microstructural and mechanical performance of ceramic corundum abrasives is summarized. Finally, the challenge and developmental trend of the optimization of ceramic corundum abrasives are prospected.

In nanofluid minimum quantity lubrication (NMQL) milling of aviation aluminum alloy, it is the bottleneck problem to adjust the position parameters (target distance, incidence angle, and elevation angle) of the nozzle to improve the surface roughness of milling, which has large and uncontrollable errors. In this paper, the influence law of milling cutter speed, helical angle, and cavity shape on the flow field around the milling cutter was studied, and the optimal nozzle profile parameters were obtained. Using 7050 aluminum alloy as the workpiece material, the milling experiment of the NMQL cavity was conducted by utilizing cottonseed oil-based Al2O3 nanofluid. Results show that the high velocity of the surrounding air flow field and the strong gas barrier could be attributed to high rotating velocities of the milling cutter. The incidence angle of the nozzle was consistent with the helical angle of the milling cutter, the target distance was appropriate at 25–30 mm, and the elevation angle was suitable at 60°–65°. The range and variance analyses of the signal-to-noise ratio of milling force and roughness were performed, and the chip morphology was observed and analyzed. The results show that the optimal combination of nozzle position parameters was the target distance of 30 mm, the incidence angle of 35°, and the elevation angle of 60°. Among these parameters, target distance had the largest impact on cutting performance with a contribution rate of more than 55%, followed by incidence angle and elevation contribution rate. Analysis by orthogonal experiment revealed that the nozzle position parameters were appropriate, and Ra (0.087 μm) was reduced by 30.4% from the maximum value (0.125 μm). Moreover, Rsm (0.05 mm) was minimum, which was 36% lower than that of the seventh group (Rsm = 0.078 mm).

The conventional flood cooling is neither economically viable nor eco-friendly in the cutting process. The vegetable oil is biodegradable and environmentally friendly used as base oil for the minimum quantity lubricant (MQL) cutting, but the temperature field in the milling zone with different vegetable oils as lubricants remains unclear. The temperature of the MQL milling of the 45 steel was studied with cottonseed, palm, castor, soybean, and peanut oils as base oils. The effects of the fatty acid composition, carbon chain length, thermal conductivity, and viscosity on the milling temperature were also considered. The temperature distribution of the milling of the 45 steel with five different vegetable oils was simulated, showing that the cottonseed and the palm oils had good cooling effect. Experimental results were consistent with the findings obtained using the temperature simulation analysis. Compared with that of the flood milling, the temperature of the MQL milling with different vegetable oils decreased, and the temperature was reduced by 67.4% when the cottonseed oil was used. The surface quality of the workpiece was improved during the MQL milling. When the cottonseed oil was used as lubricant, the surface roughness (Ra) values decreased by 41.5%, 53%, and 50.2% when the cottonseed, palm, and castor oils, respectively, were used as lubricating fluids, which indicated that the advantages of biolubricants as base oils especially cottonseed, palm, and castor oils could be used as base oils for the MQL milling.

Minimum quantity lubrication (MQL) is an emerging green and resource-saving machining technique jetting minute amount lubricants and gas after mixing and atomization. However, MQL development is restricted to mineral oils because of its undegradability and threat to the environment and human health. Vegetable oils can replace mineral oils as base oil for MQL benefitting from its biodegradability and renewable property. Nevertheless, the lubrication mechanism at the tool-workpiece interface of different vegetable oils with various physicochemical properties has not been revealed systematically. In order to verify the interfacial lubrication characteristics of different vegetable oils, MQL milling experiments of AISI 1045 based on five vegetable oils (cottonseed, palm, castor, soybean, and peanut oils) were carried out. The experimental results showed that, palm oil obtained the lowest milling force (Fx = 312 N, Fy = 156 N), friction coefficient (0.78), and surface roughness values (Ra = 0.431 μm, RSm = 0.252 mm) and the smoothest surface of workpiece. Furthermore, the physiochemical properties (composition, molecular structure, viscosity, surface tension, and contact angle) of vegetable oil were analyzed. Palm oil with high content of saturated fatty acid, high viscosity and small contact angle can form the lubricating oil film with the highest strength and the largest spreading area at the tool-workpiece interface. Therefore, palm oil can achieve the optimal lubrication effect.

The design of antenna arrays is usually imposed with lots of requirements. The design can be modeled as different kinds of optimization problems, such as single-objective optimization problems (SOPs) with or without constraints or multiobjective optimization problems with or without constraints. Then, different optimizers need to be applied to deal with the corresponding optimization problems. There are two issues to be addressed for antenna designers: one is which kind of optimization problems is the fittest to model antenna arrays; the other is a general and efficient algorithm that can solve different kinds of problems. To this end, for the first issue, constrained multiobjective optimization problems (CMOPs) are recommended as the most suitable kind in this article because CMOPs not only fully reflect the requirements of array design but also cover SOPs, constrained optimization problems (COPs), and multiobjective optimization problems (MOPs) as degeneration cases. For the second issue, a dynamic constrained multiobjective evolutionary algorithm (DCMOEA) is provided to solve CMOPs. Notably, DCMOEA is a general framework that can also work well on SOPs, COPs, and MOPs. Finally, two examples of antenna arrays are designed to verify the proposed conclusion.

Cooperative co-evolution (CC) is an efficient and practical evolutionary framework for solving large-scale optimization problems. The performance of CC is affected by the variable decomposition. An accurate variable decomposition can help to improve the performance of CC on solving an optimization problem. The variable grouping methods usually spend many computational resources obtaining an accurate variable decomposition. To reduce the computational cost on the decomposition, we propose an efficient recursive differential grouping (ERDG) method in this article. By exploiting the historical information on examining the interrelationship between the variables of an optimization problem, ERDG is able to avoid examining some interrelationship and spend much less computation than other recursive differential grouping methods. Our experimental results and analysis suggest that ERDG is a competitive method for decomposing large-scale continuous problems and improves the performance of CC for solving the large-scale optimization problems.

Aluminum alloy is the main structural material of aircraft, launch vehicle, spaceship, and space station and is processed by milling. However, tool wear and vibration are the bottlenecks in the milling process of aviation aluminum alloy. The machining accuracy and surface quality of aluminum alloy milling depend on the cutting parameters, material mechanical properties, machine tools, and other parameters. In particular, milling force is the crucial factor to determine material removal and workpiece surface integrity. However, establishing the prediction model of milling force is important and difficult because milling force is the result of multiparameter coupling of process system. The research progress of cutting force model is reviewed from three modeling methods: empirical model, finite element simulation, and instantaneous milling force model. The problems of cutting force modeling are also determined. In view of these problems, the future work direction is proposed in the following four aspects: (1) high-speed milling is adopted for the thin-walled structure of large aviation with large cutting depth, which easily produces high residual stress. The residual stress should be analyzed under this particular condition. (2) Multiple factors (e.g., eccentric swing milling parameters, lubrication conditions, tools, tool and workpiece deformation, and size effect) should be considered comprehensively when modeling instantaneous milling forces, especially for micro milling and complex surface machining. (3) The database of milling force model, including the corresponding workpiece materials, working condition, cutting tools (geometric figures and coatings), and other parameters, should be established. (4) The effect of chatter on the prediction accuracy of milling force cannot be ignored in thin-walled workpiece milling. (5) The cutting force of aviation aluminum alloy milling under the condition of minimum quantity lubrication (mql) and nanofluid mql should be predicted.

Metal cutting fluids have a huge negative impact on human health and environmental ecology, thus posing a serious threat to the sustainable development of the global manufacturing industry. Meanwhile, turning processing is an important part of metal material removal processing, and achieving sustainable lubrication is a major problem that needs to be solved urgently. Using vegetable oil to atomize and spray the tool/chip and tool/workpiece interface through the minimum quantity lubrication (MQL) supply system is a feasible way to achieve environmentally friendly turning. Moreover, the preparation of nanofluid by adding nano-reinforced phases to the degradable bio-lubricant can greatly improve the lubricating performance of the micro-droplets. However, conventional research have only focused on individual factors, such as cutting parameters, types of base oil and nanoparticles, and supply parameters. Thus, these studies are unable to provide effective guidance for the turning processing applications related to nanofluid minimum quantity lubrication (NMQL). In order to fill the gaps in the relevant literature, this paper examines the current advanced research on NMQL and explains the experimental phenomenon through the concept of lubrication mechanism. First, the advanced multi-energy field atomization technology and the influence mechanism of the physical and chemical properties of vegetable oil were reviewed. Subsequently, the influence laws governing nanofluid preparation as well as nanoparticle characteristics and concentration were summarized. Furthermore, textured tool and ultrasonic vibration-assisted nanofluid turning are proposed to increase the effective flow rate. Finally, the challenges and future trends of vegetable oil-based NMQL turning processing are proposed.

Under the threat of serious environmental pollution and resource waste, sustainable development and green manufacturing have gradually become a new development trend. A new environmentally sustainable approach, namely, cryogenic air nanofluid minimum quantity lubrication (CNMQL), is proposed considering the unfavorable lubricating characteristic of cryogenic air (CA) and the deficient cooling performance of minimum quantity lubrication (MQL). However, the heat transfer mechanism of vortex tube cold air fraction by CNMQL remains unclear. The cold air fraction of vortex tubes influences the boiling heat transfer state and cooling heat transfer performance of nanofluids during the grinding process. Thus, a convective heat transfer coefficient model was established based on the theory of boiling heat transfer and conduction, and the numerical simulation of finite difference and temperature field in the grinding zone under different vortex tube cold air fractions was conducted. Simulation results demonstrated that the highest temperature initially declines and then rises with increasing cold air fraction. Afterward, this temperature reaches the lowest peak (192.7 °C) when the cold air fraction is 0.35. Experimental verification was conducted with Ti–6Al–4V to verify the convective heat transfer coefficient model. The results concluded that the low specific grinding energy (66.03 J/mm3), high viscosity (267.8 cP), and large contact angle (54.01°) of nanofluids were obtained when the cold air fraction was 0.35. Meanwhile, the lowest temperature of the grinding zone was obtained (183.9 °C). Furthermore, the experimental results were consistent with the theoretical analysis, thereby verifying the reliability of the simulation model.

Nanofluid minimum quantity lubrication (NMQL) is an emerging cleaner and sustainable technique. However, the effective immersion of nanofluids in the grinding zone in NMQL grinding limits the extensive application of this technique. Ultrasonic vibration can exert a pumping effect that injects nanofluids into the interface between the grinding wheel and workpiece. Furthermore, technique integration benefits the infiltration state transition of micro-droplets. However, evaluation of the coupling effect and surface morphology of multi-angle 2D ultrasonic vibration integrated with NMQL has rarely been performed. This study aims to address these research limitations. A kinematics model was developed, and the grain and workpiece relative motion trails in 2D ultrasonic vibration-assisted grinding (UVAG) were simulated at different resultant vibration angles (θ). The grain’s cutting characteristics were then analyzed and found to be conducive to the full infiltration of nanofluids into the grinding zone. Moreover, the surface characterization of multi-angle 2D UVAG coupled with NMQL was evaluated experimentally. Results showed that the optimal θ was achieved at 45° due to the differential cutting action of the follow-up grain. NMQL obtained a better result than flooding at the same θ. When the gain effect of the coupling techniques was reflected in surface roughness, the Ra value decreased by 19.5 % (compared with UVAG) and 39.9 % (compared with NMQL). The autocorrelation function curves presented periodic and continuous local oscillations under the grinding conditions of 2D UVAG with θ = 45° and θ = 135°.

Nanofluid minimum quantity lubrication (NMQL) is a resource-saving, environment-friendly, and efficient green processing technology. Cottonseed oil can form a strong lubricating film owing to its abundant saturated fatty acids and monounsaturated fatty acids, which have excellent lubricating properties. The physical and chemical properties of nanofluids change when Al2O3 nanoparticles are added. However, the effects of cottonseed oil–based Al2O3 nanofluid concentrations on the milling force and workpiece surface quality remain unclear. To address this limitation, 45 steel was used to conduct NMQL milling experiments of cottonseed oil–based Al2O3 nanofluids with different mass fractions. Results showed that the minimum milling force was obtained at a concentration of 0.2 wt% (Fx = 58 N, Fy = 12 N). The minimum surface roughness value (Ra = 1.009 μm, RSm = 0.136 mm) was obtained at a mass concentration of 0.5 wt%, and the micromorphology of the workpiece/chip was optimal.

Cooperative co-evolution (CC) is an explicit means of divide-and-conquer strategy in evolutionary algorithms for solving large-scale optimization problems. The subcomponents generated by CC may have different characteristics. When optimizing the subcomponents, the settings of the subpopulations should take the characteristics into account. CCFR is a previously published CC framework which allocates computational resources among subpopulations according to the contributions of subpopulations to the improvement of the best overall objective value. In this paper, we propose an improved version of CCFR named CCFR2, which can specify unequal-sized subpopulations for optimizing different subcomponents of variables. CCFR2 computes the average improvement of the best overall objective value per fitness evaluation as the contribution of a subpopulation, which considers the subpopulation size in the contribution computation. A control parameter is adopted by CCFR2 to balance the effects of the historical and real-time improvements of the best overall objective value on the contribution computation. Compared with CCFR, CCFR2 is able to save computational resources from obtaining the best overall solution before the evolution starts and evaluating individuals in co-evolutionary cycles. Our experimental results and analysis suggest that CCFR2 improves the performance of CCFR and is a more efficient CC framework for solving large-scale optimization problems.

Breakout is one of the major accidents that often arise in the continuous casting shops of steel slabs in Bokaro Steel Plant, Jharkhand, India. Breakouts cause huge capital loss, reduced productivity, and create safety hazards. The existing system is not capable of predicting breakout accurately, as it considers only one process parameter, i.e., thermocouple temperature. The system also generates false alarms. Several other process parameters must also be considered to predict breakout accurately. This work has considered multiple process parameters (casting speed, mold level, thermocouple temperature, and taper/mold) and developed a breakout prediction system (BOPS) for continuous casting of steel slabs. The BOPS is modeled using an artificial neural network with a backpropagation algorithm, which further has been validated by using the Keras format and TensorFlow-based machine learning platforms. This work used the Adam optimizer and binary cross-entropy loss function to predict the liquid breakout in the caster and avoid operator intervention. The experimental results show that the developed model has 100% accuracy for generating an alarm during the actual breakout and thus, completely reduces the false alarm. Apart from the simulation-based validation findings, the investigators have also carried out the field application-based validation test results. This validation further unveiled that this breakout prediction method has a detection ratio of 100%, the frequency of false alarms is 0.113%, and a prediction accuracy ratio of 100%, which was found to be more effective than the existing system used in continuous casting of steel slab. Hence, this methodology enhanced the productivity and quality of the steel slabs and reduced substantial capital loss during the continuous casting of steel slabs. As a result, the presented hybrid algorithm of artificial neural network with backpropagation in breakout prediction does seem to be a more viable, efficient, and cost-effective method, which could also be utilized in the more advanced automated steel-manufacturing plants.

In constrained optimization problems (COPs), a crucial issue is that most constraint-handling evolutionary algorithms (EAs) approach the optimum either mainly from feasible regions or mainly from infeasible regions. This may result in bias in search of feasible and infeasible solutions. To address this issue, we propose a feasible-ratio control technique which controls the ratio of feasible solutions in the population. By using the control technique, an EA can maintain the search balance from feasible and infeasible regions. Based on this technique, we propose a constraint-handling EA, named FRC-CEA. It consists of two-stage optimization. In the first stage, an enhanced dynamic multi-objective evolutionary algorithm (DCMOEA) with the feasible-ratio control technique is adopted to handle constraints. In the second stage, a commonly used differential evolution (DE) is used to speed up the convergence. The performance of the proposed method is evaluated and compared with six state-of-the-art constraint-handling algorithms on two sets of benchmark test suites. Experimental results suggest that the proposed method outperforms or is highly competitive against the compared algorithms on most test problems.

To address the current bottleneck of debris formation mechanism in plastic removal for hard-brittle materials, a minimum chip thickness ( h min ) model that considers lubrication conditions (represented by frictional angle β ) is developed according to strain gradient, as well as geometry and kinematics analyses. Model results show that h min decreases with increasing β . Furthermore, grinding experiments using single diamond grain under different lubricating conditions are carried out to verify the model. With increasing β , h min values are 71.6, 57.8, 52.0, 50.7, 45.6, 39.7, and 32.4 nm, thereby verifying the trend of h min decreasing with increasing β . Furthermore, the location of size effect occurs is determined according to the variation trend of single abrasive particle specific energy and unit grinding force curves. The size effect occurs in the border area of ploughing, the cutting region, and mainly, in the ploughing region. Theoretical analysis results are consistent with experimental results with a model error of 6.06%, thereby confirming the validity of the theoretical model.

Workpiece surface integrity deterioration is a bottleneck in minimum quantity lubrication (MQL) grinding cemented carbide. However, nanofluids prepared by adding nanoparticles with excellent antifriction and antiwear properties achieve improved lubrication characteristics. In this study, a surface grinding experiment under four working conditions (i.e., dry, flood, MQL, and nanofluid minimum quantity lubrication (NMQL)) with cemented carbide YG8 is conducted to confirm the effectiveness of NMQL grinding. Results show that the minimum specific grinding force (Ft′ = 13.47 N/mm, Fn′ = 2.84 N/mm), friction coefficient (μ = 0.21), specific grinding energy (U = 17.02 J/mm3), and the largest G ratio of 6.52 are obtained using NMQL grinding. Furthermore, no evident furrow and large deformation layers are found on the surface of the workpiece. Moreover, the scanning electron microscope (SEM) images display that the debris is strip-shaped and slenderer than that under the other working conditions. Meanwhile, the blockage of the wheel pore is improved. Therefore, the validity of NMQL in grinding cemented carbide is verified.

In nanofluid minimum quantity lubrication (NMQL) grinding of titanium (Ti) alloy, existing nanoparticles cannot solve the technical bottleneck of high surface integrity. Therefore, graphene (GR) nanoparticles, which have excellent lubrication performance, were applied in NMQL. The tribological properties of GR nanofluid on wheel–workpiece interface were studied by friction and wear test. In the experiment, 0.5–3 nm thick GR nanoparticles were used to prepare 3% vol. palm oil-based nanofluid. Ball-disc experiment under grinding conditions was carried out on the friction and wear tester. Grinding balls with SiC abrasive grains (to simulate the grinding wheel) and Ti-6Al-4V disc (to simulate the workpiece) were used. Load force was set for simulation of pressure boundary condition of the grinding wheel–workpiece interface. Stratiform nanoparticles (MoS2, MoO3, and HBN) were used as the comparison group. Results demonstrated that GR nanofluid achieved smaller friction coefficient (0.295), error bars (0.0029), and area of scratches (182,940 μm2). GR nanoparticles with small gravity and large specific surface area improved the viscosity of nanofluid and consequently the lubrication performance. The plane hexagonal honeycomb structure determines the strong lubrication stability and abrasive resistance of the GR nanoparticles. The scanning electron microscope (SEM) and energy dispersive spectrometer (EDS) images of the scratch surface also verified the above conclusions.

To overcome environmental problems, nanofluid minimum quantity lubrication (NMQL) has been utilized as a burgeoning alternative technique to conventional flood machining, which consumes a large quantity of cutting fluid. Furthermore, spraying parameter optimization is a prerequisite of an effective NMQL grinding and a key to achieving a new sustainable grinding process. However, the spraying parameters of NMQL are multifarious and have varying degrees of influence on lubri-cooling performance. The main objective of this work is to first obtain good spraying parameters in NMQL grinding through the analysis of signal-to-noise ratio and analysis of variance based on orthogonal experiment results. Furthermore, an experimental verification is carried out on the basis of the relative optimization of several groups of spraying parameters from the orthogonal experiments. Power spectral density function (PSDF) and energy spectrum are used to analyze the workpiece and debris microtopography. Consequently, the optimal spraying parameter is obtained. The spraying parameter (palm oil as the base oil, 2 vol% nanoparticle concentration, 0.6 MPa air pressure, and 0.4 gas–liquid flow ratio) achieved the lowest proportionality coefficient (0.245) of PSDF in the low-frequency band and the highest (0.142) in the high-frequency band. Meanwhile, the optimal grinding performance is validated by the workpiece and debris microtopography and long-strip debris morphology.

Minimum quantity lubricant milling (MLQM) with nanofluids is one of the main processing methods used in sustainable green manufacturing. Nanoparticles can drastically enhance the heat transfer ability of metalworking fluids. Thus, nanofluids modified with different nanoparticles have important applications as milling coolants. Nevertheless, few studies have investigated the influence of different nanoparticles on the cooling mechanisms of nanofluids. An experimental study was conducted to explore the cooling performances of different nanofluids in the MQLM of Ti–6Al–4V. Cottonseed oil was used as the nanofluid base and was modified with six different nanoparticles at the mass ratio of 1.5%. Cooling performance was evaluated with several milling parameters, including milling force, instantaneous temperature, and surface roughness, which was represented as the arithmetic average height (Ra) of the workpiece surface. The microtopography of the final milled samples was also examined. Results demonstrated that the mean of the milling force peak in the X direction under milling with SiO2 nanofluid was 436 N, which was lower than that under milling with MoS2 nanofluid (445.5 N). The lowest milling temperature was obtained with SiO2 nanofluid. This result indicated that SiO2 nanoparticles had the strongest cooling capacity. Workpiece temperature rapidly decreased to a low value under milling with SiO2 and Al2O3 nanofluids. The most drastic temperature drop was observed in MQLM with SiO2 nanofluid, followed by that in MQLM with Al2O3 nanofluid. The lowest Ra value was obtained under milling with Al2O3 nanofluid (0.59 μm), followed by that obtained under milling with SiO2 nanofluid (0.61 μm). The Ra value acquired under milling with Al2O3 nanofluid was 66.7% lower than that of acquired under milling with pure cottonseed oil. Given that Al2O3 and SiO2 nanoparticles demonstrated the best cooling performance, they are highly suitable as additives for the base oil.

CNTs are commonly used nanoparticles in NMQL for their excellent heat transfer enhancement performance. However, winding and conglobation are constraints against their tribological performance, these constraints can be solved by surfactants. However, the influences of surfactant type and dispersing mechanism on the dispersion effect of CNTs have not been studied systemically. Consequently, this study analyzed the dispersing mechanism of different surfactants and evaluated the dispersion stability and tribological performances of PPO-based CNT nanofluids. Results showed that nanofluids with APE-10 obtain the highest viscosity, lowest friction coefficient, minimum roughness value and favorable surface morphology, thus indicating their excellent dispersion stability and tribological performance. Further, different experimental evaluations confirm that APE-10 is the optimal dispersant of CNT nanofluids.

The objective of this research is to experimentally evaluate the lubrication performances of different nanofluids in milling titanium alloy Ti-6Al-4V. Six types of nanofluids, namely, Al2O3, SiO2, MoS2, CNTs, SiC, and graphite, were selected. Cottonseed oil was used as the base oil. The lubrication performance was investigated in terms of milling force, surface roughness, and morphology of workpiece surface. Experimental results demonstrated that the Al2O3 nanoparticle obtained the minimal milling force (Fx = 277.5 N, Fy = 88.3 N), followed by the SiO2 nanoparticle (Fx = 283.6 N, Fy = 86.5 N). The surface roughness obtained by the Al2O3 nanofluid was the minimum (Ra = 0.594 μm), whereas it was the maximum by using minimum quantity lubrication (Ra = 1.772 μm). The surface roughness of the six nanofluids was described by the following order: Al2O3

Nanofluid minimum quantity lubrication (NMQL) has better stability, higher thermal conductivity, and excellent lubrication performance compared with traditional flood lubrication. The heat transfer model and finite difference model were established to verify the feasibility of NMQL conditions in grinding cemented carbide. Based on them, the grinding temperature of cemented carbide is calculated numerically. Results show that the grinding zone temperatures of flood grinding and NMQL are lower, 85.9 °C and 143.2 °C, respectively. Surface grinding experiments of cemented carbide YG8 under different working conditions are carried out. Dry grinding (227.2 °C) is used as the control group. Grinding zone temperatures of flood grinding, minimum quantity lubrication, and NMQL decrease by 64.2%, 39.5%, and 20.4%, respectively. The error is 6.3% between theoretical calculation temperature and experimental measurement temperature. Based on machining process parameters (specific grinding force, force ratio) and experimental results (microstructure of grinding wheel, workpiece, and grinding debris), the effects of different working conditions on wheel wear are studied. NMQL achieves the highest G ratio of 6.45, the smallest specific grinding force, and the smallest Fn/Ft ratio of 2.84, which further proves that NMQL is suitable for grinding cemented carbide.

This chapter is proposed to solve the insufficient MQL cooling and heat transfer capability based on the heat transfer enhancement theory of solid. Adding nanoparticles into the base fluid can significantly elevate heat conductivity coefficient of the base fluid and enhance convective heat transfer capability of the grinding area. Researchers have carried out numerous experimental studies on nanofluids with different concentrations. However, the scientific nature of MQL cooling has not been explained. Degradable, nontoxic, low-carbon, and environmentally friendly green grinding fluid, palm oil taken as the base fluid, grinding force, grinding temperature and proportionality coefficient of energy transferred to workpiece of nanofluids with different volume fractions, are investigated in this chapter. Based on the analysis of the influence of physical characteristics of nanofluids on experimental results, cooling and heat transfer mechanism of NMQL grinding is studied. The experimental study can provide a certain technical guidance for industrial machining.

Given the increasing attention to environmental and health problems caused by machining, the development of an environmentally friendly grinding fluid has become an urgent task. The cooling and lubricating properties of different cooling and lubricating conditions were analyzed. The influence mechanism of nanofluids minimum quantity lubrication (NMQL) on cooling and lubricating effect was revealed with different nanoparticles (MoS2, CNT, ZrO2) and different volume concentrations of MoS2 nanofluids (1%, 2%, 3%). The experimental results showed that the temperature rise (258 °C) and grinding force (Fn=70 N, Ft=27 N) obtained under NMQL grinding were the closest to the flood grinding. The specific grinding energy of MoS2 nanofluids was the lowest, which was 47 J/mm3. When the volume concentration was 2%, the best cooling and lubricating effect was obtained. The surface roughness of the workpiece was the lowest (Ra = 0.283 μm; Rz = 0.424 μm).

This chapter will discuss in detail the various aspects of nanofluids, form preparation of nanofluids, characterization and flow and energy transportation mechanisms of nanofluids. Preparation of nanofluids has been performed using various methods where one step and two step methods are widely known. These methods are discussed in detail for various nanoparticles with stabilizing agents for stable production of nanofluids. Further, the characterization techniques of various aspects of nanofluds including thermal conductivity and factors influencing the thermal conductivity of nanofluids are introduced in depth. The chapter also sheds light on the experimental analysis of flow and heat transfer of naofluids, natural convection analysis of naofluids, and boiling heat transfer of nanofluids.

As a result of the growing need for environmental protection and the increasing number of health problems faced by workers, traditional lubricants are gradually being replaced. Nanofluids, which contain nanoparticles in the proper base fluid, can serve as a low carbon, “green” lubricant. Nanofluids show improved heat transfer capability and lubricating properties. Therefore, increasing lubricating effects is an effective way to improve machining performance. The tribological properties of grinding wheel/workpiece interface with different concentration of Al2O3 nanofluid micro-lubrication grinding were studied. The influences of the force ratio, viscosity and contact angle of Al2O3 nanofluids with different concentrations on the grinding force and the surface quality of workpieces are discussed. The best concentration of Al2O3 nanofluid with good lubrication performance in grinding zone was obtained.

An investigation into the effect of nanofluid minimum quantity lubrication (MQL) on the temperatures in surface grinding is presented and discussed. Six types of nanoparticles, namely molybdenum disulfide (MoS2), zirconium dioxide (ZrO2), carbon nanotube (CNT), polycrystalline diamond, aluminum oxide (Al2O3), and silica dioxide (SiO2), are considered to mix individually with a pollution-free palm oil in preparing the nanofluids. A commonly used Ni-based alloy was chosen as the workpiece material. It is shown that CNT nanofluid results in the lowest grinding temperature of 110.7°C and the associated energy proportionality coefficient of 40.1%. The relevant physical properties of the nanofluids such as the coefficient of thermal conductivity, viscosity, surface tension, and the contact state between the droplets and workpiece surface (contact angle) were discussed to shine a light on their effect on the cooling performance. A mathematical model for convective heat transfer coefficient was then developed based on the boundary layer theories.

This chapter introduces the application background and characteristics of five kinds of grinding processing methods, briefly describes the enhanced heat transfer mechanism and tribological properties of nanofluids, and points out that nanofluids minimum quantity lubrication (NMQL) solves the technical bottleneck, namely minimum quantity lubrication (MQL) heat transfer capacity is insufficient and opening a new path for application of MQL to grinding process. The current status of exploratory research on the mechanism of minimum quantity lubrication grinding using nanofluids as cooling lubricants is analyzed. The research characteristics of the new green NMQL grinding technology are described, and the chapter puts forward some key problems such as the heat transfer enhancement process of NMQL, the anti-friction and anti-wear tribological mechanism of nanoparticles, and the controlled transport strategies of minimal quantity of lubricating droplets. It will be of great scientific significance and pragmatic value to perfecting NMQL grinding technical system.

The grindability and sensibility analysis of surface roughness of powder metallurgy nickel-base superalloy FGH96 were studied in comparison to the wrought nickel-base superalloy GH4169. The effects of grinding parameters (such as workpiece infeed speed, depth of cut, and abrasive wheel speed) on grinding force, grinding temperature, specific grinding energy, abrasive wheel wear, and surface roughness were analyzed. The results show that the grinding force, grinding temperature, and specific grinding energy of GH4169 are usually higher than those of FGH96 under the given experimental conditions. However, the wear behavior of the brown corundum abrasive wheels when grinding these two kinds of nickel-base superalloy material is generally identical. The sensitivity of GH4169 workpiece surface roughness to depth of cut and workpiece infeed speed is higher than that of FGH96, but the sensitivity of GH4169 to abrasive wheel speed is less than that of FGH96. Finally, it is inferred that the grinding performance of FGH96 is slightly better than that of GH4169.

Nanofluid minimum quantity lubrication (NMQL) technique has many technological and economic advantages in grinding operation. NMQL can improve grinding performance in terms of cooling and lubrication and is ecofriendly because it consumes a small amount of grinding fluid. Ultrasonic machining can improve grinding performance owing to its reciprocating vibration mechanism and furrow widening. Consequently, the simultaneous utilization of these techniques is anticipated to improve the surface quality, especially for hard brittle materials. In this research, multiangle two-dimensional (2D) ultrasonic vibration is utilized in zirconia ceramic grinding. Results reveal that the adhesion and material peeling phenomenon on the workpiece surface is obviously reduced compared with dry grinding without ultrasonic vibration. The synergistic effect of multiangle 2D ultrasonic and NMQL is also studied. With increased angle, the roughness value is found to initially increase (from 45° to 90°) and then decreases (from 90° to 135°). Moreover, the lubricating effect under 90° is the poorest, with the highest Ra and RSm values of 0.703 μm and 0.106 mm, respectively; conversely, the minimum Ra value (0.585 μm) is obtained under 45°, and the lowest RSm value (0.076 mm) is obtained under 135°.

Processing of Ti alloy is often accompanied with large frictions and high temperature. Nanofluids prepared by adding nanoparticles show good lubrication performance due to the excellent antifriction and antiwear of nanoparticles. The physicochemical properties of nanoparticles are important factors that influence the lubrication performance of nanofluid minimum quantity lubrication (NMQL). An experimental study on Ti alloy (Ti–6Al–4V) milling was conducted to explore the lubrication performance of minimum quantity lubrication (MQL) using different nanofluids. Cottonseed oil was used as the base oil, and the tribological properties of several typical nanoparticles (i.e., Al2O3, MoS2, SiO2, carbon nanotubes, SiC, graphite) were studied. The lubrication performance was evaluated by milling parameters, including milling force, surface roughness (Ra, RSm, Rmr), friction coefficient, microstructures of debris and workpiece surface, and energy spectra of the workpiece surface. Results demonstrated that the milling process based on Al2O3 nanofluid achieved the minimum milling force and friction coefficient, whereas the milling process based on SiO2 nanofluid had the minimum surface roughness value (Ra). Furthermore, the physicochemical properties of nanoparticles and the viscosity of nanofluid were analyzed. Spherical Al2O3 and SiO2 nanoparticles improved the lubrication effect of base oil mostly. The Al2O3 nanoparticles exhibited high hardness, which was conducive to reducing milling force. SiO2 nanofluid demonstrated high viscosity, which could improve workpiece surface quality.

Hyperspectral remote sensing images present rich information on the characteristics of different physical materials. Utilizing the rich information, classifiers can distinguish these different materials. The minimum distance technique, which is commonly used in classification, is sensitive to the distance metric, especially in high-dimensional space. In this letter, we study the effect of the $p$ -norm distance metric on the minimum distance technique and propose a supervised-learning $p$ -norm distance metric to optimize the value of $p$ . In the experimental study, we take the minimum distance and the $k$ -nearest neighbor classifiers as examples to test the proposed supervised-learning $p$ -norm distance metric. The results suggest that the supervised-learning $p$ -norm distance metric can improve the performance of a classifier for hyperspectral remote sensing image classification.

Sustainable development and green manufacturing are becoming an international consensus in the face of the threat of severe environmental pollution and waste of resources. Cryogenic air (CA) and nanofluid minimum quantity lubrication (NMQL) are state-of-the-art green manufacturing technologies. However, the lubricating performance of cryogenic air is ineffective, and the cooling ability of nanofluids minimum quantity lubrication is unsatisfactory. To specifically address the bottlenecks in these manufacturing methods, a new green processing technology combining their advantages was proposed, namely, cryogenic air nanofluid minimum quantity lubrication (CNMQL). Compared to traditional processing modes and other green technologies, cryogenic air nanofluid minimum quantity lubrication is superior for its economic efficiency, low carbon use, high utilization efficiency, energy saving as well as excellent cooling and lubricating performances. A surface grinding experiment was conducted under three lubricating conditions (cryogenic air, minimum quantity lubrication, and cryogenic air nanofluids minimum quantity lubrication) with Ti 6Al 4V as the workpiece material. Experimental results showed that: cryogenic air nanofluids minimum quantity lubrication achieved the best lubricating effect and obtained minimum specific grinding energy (51.96 J/mm3) and friction coefficient (0.60), followed by nanofluids minimum quantity lubrication and cryogenic air. The lubricating mechanisms under cryogenic air nanofluids minimum quantity lubrication and nanofluids minimum quantity lubrication conditions were also analyzed according to the viscosity of nanofluid lubricants in grinding zone, contact angle, stability of lubricating oil film, atomization effect of droplets, and microtopography of workpiece surface. Relative to other conditions, the higher viscosity and larger contact angle of nanofluid lubricants under cryogenic air nanofluids minimum quantity lubrication condition led to higher stability and better lubricating effect of the lubricating oil film in the grinding zone. Droplets sprayed onto the grinding zone had a larger atomization angle, and the distribution density of droplets in the entire atomization spraying zone was relatively uniform. The droplets were uniformly distributed and had a larger spreading area, facilitating superior atomization effect in the grinding zone. On the other hand, workpiece surface had clear and smooth grinding pipelines, which presented minimal obstruction for the longitudinal flow and horizontal spreading effect of the micrometer pipelines, so the nanofluid lubricants achieved better spreading infiltration effect. Under the joint influence of the above factors, cryogenic air nanofluids minimum quantity lubrication achieved optimal lubricating effect, thus obtaining minimum specific grinding energy and friction coefficient.

Minimum quantity lubrication (MQL) is an efficient, green, and eco-friendly method of applying cutting fluids in machining processes. This study presents the processing characteristics of different vegetable oil-based nanofluid MQL for grinding various workpiece materials. The performance of three lubricant types (i.e., pure palm oil, MoS2 nanofluid, and Al2O3 nanofluid) of good lubrication performance and three types of materials (i.e., Inconel 718, ductile cast iron, and AISI 1045 steel) was evaluated in terms of force ratio, specific grinding energy, and G ratio. The optimal processing combination of lubricants and workpiece materials under the same experimental conditions was obtained using orthogonal experiment. Optimization results were verified by evaluating the morphology of the workpiece surface and grinding debris. Experimental results show the different processing characteristics of materials when various workpieces are processed using dissimilar MQL lubricants. MoS2 nanofluid MQL is suitable for machining soft medium carbon steels, such as 45 steel, while Al2O3 nanofluid is suitable for machining materials of high strength and hardness, such as nickel-based alloys.

Considering the poor lubricating effect of cryogenic air (CA) and inadequate cooling ability of nanofluid minimum quantity lubrication (NMQL), this work proposes a new manufacturing technique cryogenic air nanofluid minimum quantity lubrication (CNMQL). A heat transfer coefficient and a finite difference model under different grinding conditions were established based on the theory of boiling heat transfer and conduction. The temperature field in the grinding zone under different cooling conditions was simulated. Results showed that CNMQL exerts the optimal cooling effect, followed by CA and NMQL. On the basis of model simulation, experimental verification of the surface grinding temperature field under cooling conditions of CA, MQL, and CNMQL was conducted with Ti–6Al–4V as the workpiece material. Simultaneously, CNMQL exhibits the smallest specific tangential and normal grinding forces (2.17 and 2.66 N/mm, respectively). Further, the lowest grinding temperature (155.9 °C) was also obtained, which verified the excellent cooling and heat transfer capabilities of CNMQL grinding. Furthermore, the experimental results were in agreement with theoretical analysis, thereby validating the accuracy of the theoretical model.

Nanofluid minimum quantity lubrication (NMQL) is one of the main modes of sustainable manufacturing. It is an environment-friendly, energy-saving, and highly efficient lubrication method. With the use of nanoparticles, the tribological properties of debris–tool and workpiece–tool interfaces will change. However, spectrum analyses of force and power spectral density (PSD) of surface microstructures are limited. In the present work, the milling force, friction coefficient, specific energy, surface roughness, and surface microstructure of debris were evaluated in milling of 45 steel for different lubrication conditions, namely, dry, flood, minimum quantity lubrication, and Al2O3 NMQL. Results demonstrated that compared with other lubrication conditions, NMQL achieves minimum milling force peak (Fx = 270 N, Fy = 160 N, Fz = 50 N), friction coefficient (μ = 1.039), specific energy (U = 65.5 J/mm3), and surface roughness value (Ra = 2.254 μm, RSm = 0.0562 mm). Furthermore, a spectrum analysis of the milling force and PSD of the surface microstructure was conducted for validation. The spectral analysis of milling force revealed that NMQL obtained the lowest milling force and amplitude in the middle-frequency region, thereby indicating the minimum abrasion loss of the tool. Meanwhile, the PSD analysis indicated that NMQL had the lowest proportional coefficient in the low-frequency region (0.4766) and the highest proportional coefficient in the high-frequency region (0.0569). These results revealed that the workpiece surface gained by Al2O3 NMQL obtained higher wave fineness than other working conditions. By combining with the lowest Ra, NMQL contributes the best workpiece surface quality. Therefore, machining experiments using NMQL showed the best lubrication performance.

Numerous researchers made theoretical and experimental approaches for force prediction in surface grinding under dry condition. However, the combined effect of both material removal and plastic stacking on single-grain interference mechanics has not been reported yet. The main objective of the work presents to propose an single-grain interference mechanics with the consideration of material removal and plastic stacking mechanism. Interference action of a single grain and plastic/fracture deformation condition of workpiece material were analysis, critical stress was obtained. Further, scratch test of single-grain was carried out under nanofluids minimum quality lubrication. the relationship of grain-cutting efficiency (β) and cutting depth (ag) was obtained. Results indicated that the variation trend of the β presents an S-shaped trend as the ag increases. Grain states, including cutting and ploughing, are decided by cutting efficiency (β). For experiment implemented scratch tests of workpiece material (stainless steel 440 C, Ra = 0.04–0.05 μm), the plough phenomenon appeared at ag=0.023 μm, that is, the range 0~0.023 μm indicates the sliding stage. The cutting stage started at ag=2.84μm. Unlike traditional grinding theory, after the end of the ploughing stage, the β curve suddenly increased and then entered the transition stage. The transition from the ploughing stage into the cutting stage is a progressive process. After cutting stage, β gradually tended to be a constant.

As a non-edible, degradable, and environment-friendly crop product, castor oil can be used as lubricating oil in material processing. However, its high viscosity results in poor flowability, thereby limiting its industrial applications. Although experiments have revealed the excellent lubrication performance of castor/soybean oil mixture, the volume ratio of castor oil and soybean oil can significantly influence their minimum quantity lubrication (MQL) grinding performance. In the current study, seven castor/soybean oil mixtures under different volume ratios ranging between 1:0.5 and 1:4 were prepared for MQL grinding of Ni-based alloy. The mixed oils were compared with pure castor oil (1:0) in terms of viscosity and tribological behaviors. The grinding experiment involved the evaluation of specific grinding force, surface roughness, surface microtopography, and grinding debris, conluding that the preferred castor/soybean volume ratios were found to be 1:0.5, 1:1, 1:1.5, and 1:2. The surface microtopographies of selected workpieces were analyzed further in terms of their fractal dimension and scale coefficient, finding that the maximum fractal dimension (D = 1.31) and minimum scale coefficient (G = 0.30) are achieved when the volume ratio is 1:2. Hence, the optimal volume ratio is determined.

This paper proposes a new method for handling the difficulty of multi-modality for the single-objective optimization problem (SOP). The method converts a SOP to an equivalent dynamic multi-objective optimization problem (DMOP). A new dynamic multi-objective evolutionary algorithm (DMOEA) is implemented to solve the DMOP. The DMOP has two objectives: the original objective and a niche-count objective. The second objective aims to maintain the population diversity for handling the multi-modality difficulty during the search process. Experimental results show that the performance of the proposed algorithm is significantly better than the state-of-the-art competitors on a set of benchmark problems and real world antenna array problems.

Textured Abrasive Tool (TAT) offers an effective solution to alleviate thermal issues during machining processes, and laser machining has been identified as one of the most promising fabrication approaches. Laser ablation of TATs has unique process complexity thanks to not only the porous structure of abrasive tools but also the bond-abrasive multiple-material nature. Due to this complexity, most previous investigations on laser-based generation of TATs relied on the single-pass ablation strategy, and therefore most reported TATs had still 2D structures with the same ablation depth. Although the creation of depth-controlled 3D structures based on multiple-pass laser ablation strategy has been investigated in the laser machining community, few attempt applied this technique to TAT fabrication, and this paper aims to fill this gap. Special attentions were paid to the effect of both laser pass numbers and scanning speeds on the ablated structure shapes, depths, widths, and morphologies. The experimental results were well supported by the theoretical explanations, based on which the pragmatic laser parameter selection strategy was proposed. With the strategy, we successfully produced one TAT having 2D sinusoidal profiles and one with 3D staggered hemisphere micro-structures. Grinding trials were performed as well to prove the superior performances of the produced tool. This work might provide important reference for designing and manufacturing the next-generation advanced abrasive tools embedded with complex and functional 3D machining elements.

Concentrated photovoltaics (CPV) is a dawn technology in the field of photovoltaic that helps in escalating the effective use of solar energy. Nowadays, applications of photovoltaic solar cells are catching attention due to the better utilization of solar energy. A huge amount of solar energy is received by the earth from the sun, but a barrier to the large-scale use of photovoltaic solar cells is their higher initial cost and lower conversion compared to other non-renewable energy systems. Concentrated Photovoltaics (CPV) is one of the vital tools that focus solar radiation on the small area of solar cells using optical devices to maximize solar to thermal conversion. Low cost, high efficiency, and climate-friendly are the main advantages of concentrated photovoltaics. The review study presents the outlook of work conducted worldwide on the different types of concentrated photovoltaics. In addition, the effect of various performance affecting parameters, challenges, and recent progress is also part of the study. Most of the CPV have efficiency up to 15 % while some have an efficiency range of 25–28 % which is still very low. It was found that the CPV gave maximum efficiency of up to 38.5 % at optimal solar radiation. The focus of sunlight on a small area of solar cell increases the temperature of concentrated photovoltaic allegedly pernicious for electrical efficiency and the life of CPV. Factors like direct normal irradiance, high cell temperature, soiling, optical design, reliability, and durability are considered as challenges and a concise summary of various studies on these challenges is presented. In this regard, various cooling techniques have been investigated by different researchers for thermal management of CPV systems which are discussed in detail. As CPV technology is still in the development phase, various new optical designs emphasizing novel designs and materials are also summarized in the current study. Finally, some recommendations are oriented which will be very valuable for those who are working or want to work in the field of photovoltaics.

In grinding, the convective heat transfer coefficient (CHTC) determines the heat transfer ratio and the grinding temperature, and it indirectly identifies the surface integrity. No effective method for calculating CHTC is available for aerosol cooling, and it still lacks the measuring equipment that accords with the actual condition of aerosol cooling. On this basis, the atomization mechanism of nanofluid aerosol cooling (NAC) was studied, the influence of jet parameters on the spray boundary was revealed, and the probability density of droplet size in the grinding zone was statistically analyzed. A theoretical model for the CHTC of NAC was developed on the basis of the analysis of the heat transfer coefficient of a single nanofluid droplet. Furthermore, a new method for measuring the CHTC of aerosol cooling was proposed, and the measuring equipment was designed and built. The measurement error of the measurement system was calculated, and results showed that the measurement error of the new measuring equipment is 0.044 (10−2 W/mm2·K). Nanofluids were prepared by using medical nanoparticles and normal saline, and the CHTC was calculated and measured. The results showed that the theoretical model error is 7.26%, which verifies the correctness of the theoretical model.

Nanoparticle jet mist cooling (NJMC) is an effective solution to prevent heat injuries in clinical neurosurgery bone grinding. A simulation study on temperature field of microscale bone grinding wa...

Numerous researchers have developed theoretical and experimental approaches to force prediction in surface grinding under dry conditions. Nevertheless, the combined effect of material removal and plastic stacking on grinding force model has not been investigated. In addition, predominant lubricating conditions, such as flood, minimum quantity lubrication, and nanofluid minimum quantity lubrication, have not been considered in existing force models. This work presents an improved theoretical force model that considers material-removal and plastic-stacking mechanisms. Grain states, including cutting and ploughing, are determined by cutting efficiency (β). The influence of lubricating conditions is also considered in the proposed force model. Simulation is performed to obtain the cutting depth (ag) of each “dynamic active grain.” Parameter β is introduced to represent the plastic-stacking rate and determine the force algorithms of each grain. The aggregate force is derived through the synthesis of each single-grain force. Finally, pilot experiments are conducted to test the theoretical model. Findings show that the model's predictions are consistent with the experimental results, with average errors of 4.19% and 4.31% for the normal and tangential force components, respectively.

A study of microscale skull bone grinding temperature field under different cooling conditions is presented and discussed. Micro-grinding model, heat flux density model, convective heat transfer coefficient model and intra-workpiece heat conduction model are developed, based on which, a simulation study on microscale bone grinding temperature field under dry grinding, drip cooling, mist cooling, and nanoparticle jet mist cooling (NJMC) is performed. Simulation results show that NJMC displayed the lowest temperature peak (27.5 °C), followed by mist cooling, drip cooling, and dry grinding successively. A verification experiment using fresh bovine femur bone is performed under different cooling conditions. Results find that the temperature peak of dry grinding is 41.6 °C, which is 10.1%, 29.3%, and 37% higher than those of drip cooling, mist cooling, and NJMC, respectively, verifying the optimal cooling effect of NJMC. The experimental results are consistent with the theoretical analysis results, confirming the validity of the theoretical models.

This study investigates the critical maximum undeformed equivalent chip thickness for ductile-brittle transition (DBhmax-e) of zirconia ceramics under different lubrication conditions. A DBhmax-e model is developed through geometry and kinematics analyses of ductile-mode grinding. Result shows that DBhmax-e decreases with increasing friction coefficient (μ). An experimental investigation is then conducted to validate the model and determine the effect of dry lubrication, minimum quantity lubrication (MQL), and nanoparticle jet minimum quantity lubrication (NJMQL) conditions on DBhmax-e. According to different formation mechanisms of debris, the grinding behavior of zirconia ceramics is categorized into elastic sliding friction, plastic removal, powder removal, and brittle removal. Grinding forces per unit undeformed chip thickness (Fn/h and Ft/h) are obtained. The lubrication condition affects the normal force and ultimately influences the resultant force on workpiece. In comparison with dry grinding (DBhmax-e = 0.8 μm), MQL and NJMQL grinding processes increase DBhmax-e by 0.99 and 1.79 μm respectively; this finding is similar to model result. The theoretical model is then assessed by different volume fractions of nanofluids under NJMQL condition with an average percentage error of less than 8.6%.

Nanofluid minimum quantity lubrication is an environmental-friendly, resource-saving, and sustainable process compared with traditional flood lubrication. Especially, it is widely applied in diffic...

Vegetable oil is a low toxic, excellent biodegradable and renewable energy source used as an ideal lubricating base oil in machining. Castor oil exhibits good lubrication performance but poor mobility, which limits its application especially in precision grinding. The main objective of the work presented to obtain optimal mixed vegetable based-oil and optimal nanoparticles adding concentration in grinding Ni-based alloy with minimum quantity lubrication. An experimental investigation is carried out first to study the different vegetable oils with excellent mobility mixed with castor oil. The lubrication property of the oil was evaluated in terms of grinding force, force ratio, specific grinding energy, and surface roughness. Based on the test conditions, it is found that soybean/castor mixed oil obtained the optimal results ( μ = 0.379, U = 83.27 J/mm 3 and R a = 0.325 μm) and lubricating effect compared with castor oil and other mixed base oils. To further explore the lubricating capability of soybean/castor mixed oil, MoS 2 nanoparticles which have excellent lubricating property were added into the soybean/castor mixed oil to prepare different concentrations nanofluids. From the present study, it can be concluded that 8% mass fraction of the oil mixture should be added to obtain the optimal machining results, with the lowest force ratio (0.329), specific energy (58.60 J/mm 3 ), and average grinding temperature (182.6 °C). Meanwhile, better surface microtopography of ground parts and grinding debris morphologies were also observed for the machining conditions.

This study investigated the grinding temperature of minimum quantity lubricant cooling (MQLC) for heat transfer. Three typical workpiece materials, namely, 45 steel, Ni-based alloy, and nodular cast iron, were surface grinded. These materials are the most frequently used for mechanical processing. Palm oil with good lubrication and heat transfer performance was chosen as the base oil for the nanofluids. Carbon nanotube (CNT) nanofluids with volume fractions of 2 and 2.5%, as well as excellent heat transfer performance, were prepared for the MQLC fluid. Results showed that the 45 steel grinding had the highest temperature (363.9 °C), and the grinding temperature of the 2% nanofluid (363.9 °C) was slightly higher than that of the 2.5% nanofluid (352.9 °C). A numerical simulation heat transfer model conducting the finite difference method was established for the numerical analysis of the MQLC grinding temperature. Results indicated that the model predictions and experimental results are in good agreement, with 4.8% average model error. The heat transfer mechanism of the nanofluids was also analyzed. This study confirmed that nucleate boiling heat transfer was achieved when grinding the Ni-based alloy.