Assessment of Lubrication Property and Machining Performance of Nanofluid Composite Electrostatic Spraying(NCES)Using Different Types of Vegetable Oils as Base Fluids of External Fluid

The current study of minimum quantity lubrication(MQL)concentrates on its performance improvement.By contrast with nanofluid MQL and electrostatic atomization(EA),the proposed nanofluid composite electrostatic spraying(NCES)can enhance the performance of MQL more comprehensively.However,it is largely influenced by the base fluid of external fluid.In this paper,the lubrication property and machining performance of NCES with different types of vegetable oils(castor,palm,soybean,rapeseed,and LB2000 oil)as the base fluids of external fluid were compared and evaluated by friction and milling tests under different flow ratios of external and internal fluids.The spraying current and electrowetting angle were tested to analyze the influence of vegetable oil type as the base fluid of external fluid on NCES performances.The friction test results show that relative to NCES with other vegetable oils as the base fluids of external fluid,NCES with LB2000 as the base fluid of external fluid reduced the friction coefficient and wear loss by 9.4%-27.7%and 7.6%-26.5%,respectively.The milling test results display that the milling force and milling temperature for NCES with LB2000 as the base fluid of external fluid were 1.4%-13.2%and 3.6%-11.2%lower than those for NCES with other vegetable oils as the base fluids of external fluid,respectively.When LB2000/multi-walled carbon nanotube(MWCNT)water-based nanofluid was used as the external/internal fluid and the flow ratio of external and internal fluids was 2:1,NCES showed the best milling performance.This study provides theoretical and technical support for the selection of the base fluid of NCES external fluid.

MACHINING OF METAL MATRIX COMPOSITES

Two types of aluminium-based composites reinforced respectively with 20 vol short fibre alumina and with a hybrid of 15 vol SiC particle and 5 vol short alumina fibre are machined with different tool materials:cemented carbide,ceramic,cubic boron nitride(CBN)and polycrystalline diamond(PCD).The analysis on tool wear shows that the various tool materials exhibite different tool wear behaviours,and the tool wear mechanisma are discussed.Apparently,PCD tools do not necessarily guarantee dimensional stability but they can provide the most economic means for machining all sorts of composites.Consequently,a suitable tool material is suggested for machining each metal matrix composite(MMC) from the standpoints of tool wear and machined surface finish.

Machine learning-based stiffness optimization of digital composite metamaterials with desired positive or negative Poisson's ratio

Mechanical metamaterials such as auxetic materials have attracted great interest due to their unusual properties that are dictated by their architectures.However,these architected materials usually have low stiffness because of the bending or rotation deformation mechanisms in the microstructures.In this work,a convolutional neural network(CNN)based self-learning multi-objective optimization is performed to design digital composite materials.The CNN models have undergone rigorous training using randomly generated two-phase digital composite materials,along with their corresponding Poisson's ratios and stiffness values.Then the CNN models are used for designing composite material structures with the minimum Poisson's ratio at a given volume fraction constraint.Furthermore,we have designed composite materials with optimized stiffness while exhibiting a desired Poisson's ratio(negative,zero,or positive).The optimized designs have been successfully and efficiently obtained,and their validity has been confirmed through finite element analysis results.This self-learning multi-objective optimization model offers a promising approach for achieving comprehensive multi-objective optimization.

An Investigation of Laser Assisted Machining of Al_2O_3 Particle Reinforced Aluminum Matrix Composite

The Al 2O 3 particles reinforced aluminum matrix composite (Al 2O 3p/Al) are more and more widely used for their excellent physical and chemical properties. However, their poor machinability leads to severe tool wear and bad machined surface. In this paper laser assisted machining is adopted in machining Al 2O 3p/Al composite and good result was obtained. The result of experiment shows in machining Al 2O 3p/Al composites the cutting force is reduced in 30%～50%, the tool wear is reduced in 20%～30% and machined surface quality is improved in laser assisted machining as compared with conventional cutting. The physical model of the cutting process is set up and explains the reason why the cutting forced are reduced. The state of the particles is the main influence of the change. When the material of cutting zone is heating by laser, the aluminum matrix becomes softer and easier in plastic deformation, which leads to the reduction of the pushing force from the tool to the machined surface. The soften aluminum matrix is more easy to be squeezed out from the machined surface, and it leads the concentration of the Al 2O 3 particles in the surface layer of machined surface. The softening effect of laser heating on aluminum matrix reduces the pushing forces of the Al 2O 3 particles on the clearance face of cutting tool, which is just the reason for the severe cutting tool wear in conventional machining of Al 2O 3p/Al composite. Because the Al 2O 3 particles were pushed in during the cutting process, the particles increased in the surface layer. Because of the difference in thermal conductivity and thermal expansion between the Al-matrix and Al 2O 3 particle, residual stress is changed in the matrix after machining due to the extrusion of the tool, deformation of the matrix and displacement of the Al 2O 3 particle in the matrix. Temperature gradient comes into the cutting zone and the work-piece surface layer, it will lead to the increase of thermal stress and misfit dislocation in the matrix. The residual stress is compressive in the laser assisted hot cutting surface, the compressive stress is nearly triple times than that in the conventional cutting surface. Some analysis on the mechanism of laser heat assisted machining of Al 2O 3p/Al composite is given in the paper too.

Micro Model of Carbon Fiber/Cyanate Ester Composites and Analysis of Machining Damage Mechanism

Machining damage occurs on the surface of carbon fiber reinforced polymer (CFRP) composites during processing. In the current simulation model of CFRP, the initial defects on the carbon fiber and the periodic random distribution of the reinforcement phase in the matrix are not considered in detail, which makes the characteristics of the cutting model significantly different from the actual processing conditions. In this paper, a novel three-phase model of carbon fiber/cyanate ester composites is proposed to simulate the machining damage of the composites. The periodic random distribution of the carbon fiber reinforced phase in the matrix was realized using a double perturbation algorithm. To achieve the stochastic distribution of the strength of a single carbon fiber, a novel method that combines the Weibull intensity distribution theory with the Monte Carlo method is presented. The mechanical properties of the cyanate matrix were characterized by fitting the stress-strain curves, and the cohesive zone model was employed to simulate the interface. Based on the model, the machining damage mechanism of the composites was revealed using finite element simulations and by conducting a theoretical analysis. Furthermore, the milling surfaces of the composites were observed using a scanning electron microscope, to verify the accuracy of the simulation results. In this study, the simulations and theoretical analysis of the carbon fiber/cyanate ester composite processing were carried out based on a novel three-phase model, which revealed the material failure and machining damage mechanism more accurately.

Simulation and Verification of Machining Deformation for Composite Materials

Prepreg properties including cure kinetics, cure shrinkage, and coefficient of thermal expansion were analyzed. A simulation method based on ＂element birth and death＂ method of Finite element analysis （FEA） was presented to simulate the cutting process and predict the machining deformation for composite laminates and stiffened panels. The comparisons between the simulation results and experimental data showed good agreement. It is found that residual stresses are the main source of machining deformation for composites and machining deformation is expected to happen only if there are stress gradients along the machining direction. There is no machining deformation for composite laminates due to its uniform stresses distribution in plane, while machining deformation can be observed obviously for T-shape stiffened composite panels. Attention should be paid to machining deformation to avoid the mismatch during assembly.

Study of machining induced surface defects and its effect on fatigue performance of AZ91/15%SiCp metal matrix composite

The quality of surface generated in a peripheral milling of AZ91/SiCp/15%for varying machining conditions and its effect on the fatigue performance are investigated in this study.The machined surface quality was evaluated through roughness measurements and SEM micrographs of ine machined surface.Tensile iesis were pcifumicu io iiieasure the mechanical properties of the composite.Subsequently,fatigue life of milled specimens was measured through axial fatigue tests at four loading conditions.Optical and SEM/EDS micrographs of the fractured surface were studied to identify the crack initiation site and propagation mechanism.Specimens machined at a lower feed rate of 0.1 mm/rev was found to have excellent surface finish and consequently higher fatigue life.At 0.3 mm/rev,the presence of feed marks and other surface defects resulted in a drastic decrease in fatigue life.Five distinct regions were identified on the fractured surface,particle fracture along and perpendicular to the surface,voids in the matrix due to particle debonding and pull out and typical ductile failure of matrix with embedded SiC particles.

Joining and machining of(ZrB2-SiC)and(Cf-SiC)based composites

Filler materials of(ZrB_2-SiC-B_4C-YAG) composite were developed for gas tungsten arc welding(GTAW) of the ZrB_2-SiC and Cf-SiC based composites to themselves and to each other. Reaction with filler material,porosity and cracks were not observed at weld interfaces of all the joints. Penetration of filler material in to voids and pores existing in the Cf-SiC composites was observed. Average shear strength of 25.7 MPa was achieved for joints of Cf-SiC composites. By incorporation of Cf-SiC(CVD) ground short fibre reinforcement the(ZrB_2-SiC-B_4C-YAG) composite was machinable with tungsten carbide tool. The joint and machined composites were resistance to oxidation and thermal shock when exposed to the oxy-propane flame at 2300℃ for 300s. The combination of(ZrB_2-SiC-B_4C-YAG) and Cf-SiC based composites can be used for making parts like thermal protection system or nozzles for high temperature applications.

Analysis of the Machining Characteristics on Surface Roughness of a Hybrid Aluminium Metal Matrix Composite (Al6061-SiC-Al₂O₃)

Aluminium metal matrix composites are finding increased applications in many areas. Adding of the third element to the metal matrix make the composite hybrid. This paper presents the study on the surface roughness characteristics of a hybrid aluminium metal matrix (Al6061-SiC-Al2O3) composites. The experimental studies were carried out on a lathe. The composites were prepared using the liquid metallurgy technique, in which 3, 6 and 9 wt % of particulates SiC and Al2O3 were dispersed in the base matrix. The obtained cast composites were carefully machined. The characteristics that influence the surface roughness such as feed rate, depth of cut and cutting speed were studied, which made the analysis come to a conclusion that the surface roughness is increases with the increase of feed rate and it reduces the surface roughness with the increase of cutting speed.

A review on machinability of carbon fiber reinforced polymer(CFRP)and glass fiber reinforced polymer(GFRP)composite materials

Fiber reinforced polymer(FRP) composite materials are heterogeneous and anisotropic materials that do not exhibit plastic deformation. They have been used in a wide range of contemporary applications particularly in space and aviation,automotive,maritime and manufacturing of sports equipment. Carbon fiber reinforced polymer(CFRP) and glass fiber reinforced polymer(GFRP) composite materials,among other fiber reinforced materials,have been increasingly replacing conventional materials with their excellent strength and low specific weight properties. Their manufacturability in varying combinations with customized strength properties,also their high fatigue,toughness and high temperature wear and oxidation resistance capabilities render these materials an excellent choice in engineering applications.In the present review study,a literature survey was conducted on the machinability properties and related approaches for CFRP and GFRP composite materials. As in the machining of all anisotropic and heterogeneous materials,failure mechanisms were also reported in the machining of CFRP and GFRP materials with both conventional and modern manufacturing methods and the results of these studies were obtained by use of variance analysis(ANOVA),artificial neural networks(ANN) model,fuzzy inference system(FIS),harmony search(HS) algorithm,genetic algorithm(GA),Taguchi's optimization technique,multi-criteria optimization,analytical modeling,stress analysis,finite elements method(FEM),data analysis,and linear regression technique. Failure mechanisms and surface quality is discussed with the help of optical and scanning electron microscopy,and profilometry. ANOVA,GA,FEM,etc. are used to analyze and generate predictive models.

Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites

The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.

CUTTING REGULARITY AND DISCHARGE CHARACTERISTICS BY USING COMPOSITE COOLING LIQUID IN WIRE CUT ELECTRICAL DISCHARGE MACHINE WITH HIGH WIRE TRAVELING SPEED

The analysis of cutting regularity is provided through using and comparing two typical cooling liquids. It is proved that cutting regularity is greatly affected by cooling liquid＇s washing ability. Discharge characteristics and theoretic analysis between two electrodes are also discussed based on discharge waveform. By using composite cooling liquid which has strong washing ability, the efficiency in the first stable cutting phase has reached more than 200 mm^2/min, and the roughness of the surface has reached Ra〈0.8 μm after the fourth cutting with more than 50 mm^2/min average cutting efficiency. It is pointed out that cutting situation of the wire cut electrical discharge machine with high wire traveling speed （HSWEDM） is better than the wire cut electrical discharge machine with low wire traveling speed （LSWEDM） in the condition of improving the cooling liquid washing ability. The machining indices of HSWEDM will be increased remarkably by using the composite cooling liquid.

Experimental Study of Machinability in Millgrinding of SiCp/Al Composites

An attempt was made to investigate the machinability of Si Cp/Al composites based on the experimental study using mill-grinding processing method. The experiments were carried out on a high-speed CNC machining center using integrated abrasive cutting tool. The effects of combined machining parameters, e g, cutting speed（vs）, feed rate（vf）, and depth of cut（ap）, with the same change of material removal rate（MRR） on the mill-grinding force and surface roughness（Ra） were investigated. The formation mechanism of typical machined surface defects was analyzed by SEM. The experimental results reveal that with the same change of material removal rate, lower mill-grinding force values can be gained by increasing depth of cut and feed rate simultaneously at higher cutting speed. With the same change of MRR value, lower surface roughness values can be gained by increasing the feed rate at higher cutting speed, rather than just increasing the depth of cut, or increasing the feed rate and depth of cut simultaneously. The machined surface of Si Cp/Al composites reveals typical defects which can influence surface integrity.

Milling Machinability of TiC Particle and TiB Whisker Hybrid Reinforced Titanium Matrix Composites

The milling machinabilities of titanium matrix composites were comprehensively evaluated to provide a theoretical basis for cutting parameter determination. Polycrystalline diamond （PCD） tools with different grain sizes and geometries, and carbide tools with and without coatings were used in the experiments. Milling forces, milling temperatures, tool lifetimes, tool wear, and machined surface integrities were investigated. The PCD tool required a primary cutting force 15 % smaller than that of the carbide tool, while the uncoated carbide tool required a primary cutting force 10% higher than that of the TiA1N-eoated tool. A cutting force of 300 N per millimeter of the cutting edge （300 N/mm） was measured. This caused excessive tool chipping. The cutting temperature of the PCD tool was 20%-30% lower than that of the carbide tool, while that of the TiA1N-coated tool was 12% lower than that of the uncoated carbide tool. The cutting temperatures produced when using water-based cooling and minimal quantity lubrication （MQL） were reduced by 100 ~C and 200 ~C, compared with those recorded with dry cutting, respectively. In general, the PCD tool lifetimes were 2--3 times longer than the carbide tool lifetimes. The roughness Ra of the machined surface was less than 0.6μm, and the depth of the machined surface hardened layer was in the range of 0.15-0.25 mm for all of the PCD tools before a flank wear land of 0.2 mm was reached. The PCD tool with a 0.8 mm tool nose radius, 0% rake angle, 10% flank angle, and grain size of （30＋2） μm exhibited the best cutting performance. For this specific tool, a lifetime of 16 rain can be expected.

Analysis of Vibrations of Roller of the Polymer Composite Coating Equipment on Stitches of Tarpaulin Materials

The article presents a structural diagram and the principle of operation of the installation of a sewing machine for applying a polymer composition to the stitch lines of tarpaulin materials. The calculation schemes and the mathematical model of oscillations of the axis of the composite roller during the application of the polymer composition along the lines of tarpaulin materials are presented. Based on the numerical solution of the problem, the regularities of roller oscillations are presented. The main parameters of the system are substantiated.

Rapid, accurate and serotype independent pipeline for in silico epitope mapping of SARS-CoV-2 antigens: a combined machine learning and Chou’s pseudo amino acid composition method

Here,a new integrated machine learning and Chou’s pseudo amino acid composition method has been proposed for in silico epitope mapping of severe acute respiratorysyndrome-like coronavirus antigens.For this,a training dataset including 266 linear B-cell epitopes,1,267 T-cell epitopes and 1,280 non-epitopes were prepared.The epitope sequences were then converted to numerical vectors using Chou’s pseudo amino acid composition method.The vectors were then introduced to the support vector machine,random forest,artificial neural network,and K-nearest neighbor algorithms for the classification process.The algorithm with the highest performance was selected for the epitope mapping procedure.Based on the obtained results,the random forest algorithm was the most accurate classifier with an accuracy of 0.934 followed by K-nearest neighbor,artificial neural network,and support vector machine respectively.Furthermore,the efficacies of predicted epitopes by the trained random forest algorithm were assessed through their antigenicity potential as well as affinity to human B cell receptor and MHC-I/II alleles using the VaxiJen score and molecular docking,respectively.It was also clear that the predicted epitopes especially the B-cell epitopes had high antigenicity potentials and good affinities to the protein targets.According to the results,the suggested method can be considered for developing specific epitope predictor software as well as an accelerator pipeline for designing serotype independent vaccine against the virus.

Hybrid Intelligent Damage Identification of Composite Plate Based on Ensemble Empirical Mode Decomposition and Support Vector Machine

In this paper,a novel method based on strain distribution is presented to determine the presence of damage and its location in composite plate.By building a damage monitoring experimental platform with Fiber Bragg Gratings(FBGs)sensors,impact experiments are made respectively to gain the strain distribution both in heath and damage state.EEMD is used to process the data and IMFs energy feature is evaluated.Then,support vector machine is applied to identify the damage and the testing classification accuracy reaches 92.86%.Finally,by using the influence of the damage position and the propagation path on energy,the damage location is predicted.The experimental results indicate that the proposed method can accurately identify the presence and position of damage.The effectiveness and reliability of the proposed method is verified.

Machinability of Polymeric Composites and Future Reinforcements—A Review

This paper reviews the machinability and mechanical properties of natural fiber-reinforced composites. Coupling agents, operating parameters, as well as chemical treatment effects on natural fiber-reinforced composites’ machinability are also reviewed. Moreover, the impacts of fibers’ physical properties on the machinability of the composite are mentioned. Fiber volume fraction (Vf), fiber orientation as well as chemical treatment effects on mechanical properties are also defined. Conclusively, the effect of fibers’ physical properties as well as mechanical properties is described. It was discovered that chemical treatment of natural fibers improved their compatibility with the matrix by removing their surface tissues, increasing the roughness average (Ra), and reducing moisture absorption. Also, the Orientation of the fiber plays an important role in controlling the mechanical properties of the composite. Moreover, some physical properties of the fibers, including quality of fiber distributed in the matrix;fiber size, length, and diameter;moisture absorption;porosity and the way fibers break during compounding with the matrix, were found to affect the mechanical properties of the composites formed.

Comprehensive Study on Machinability of Titanium Composite

Metal framework composites have higher mechanical properties in examination to metals over an extensive variety of working conditions. This makes them an alluring alternative in swapping metals for different building applications. The present review is a study on the influence of composite titanium on the cutting parameters, mechanical behavior, reinforcements, structure and nanostructure. This review will provide an understanding into selecting the optimum machining parameters for machining titanium composites. It’s also an attempt to give brief explanation by suitably machining the titanium composite which can be made reasonable.

Tribological properties and wear prediction model of TiC particles reinforced Ni-base alloy composite coatings

TiC particles reinforced Ni-based alloy composite coatings were prepared on 7005 aluminum alloy by plasma spray. The effects of load, speed and temperature on the tribological behavior and mechanisms of the composite coatings under dry friction were researched. The wear prediction model of the composite coatings was established based on the least square support vector machine (LS-SVM). The results show that the composite coatings exhibit smaller friction coefficients and wear losses than the Ni-based alloy coatings under different friction conditions. The predicting time of the LS-SVM model is only 12.93%of that of the BP-ANN model, and the predicting accuracies on friction coefficients and wear losses of the former are increased by 58.74%and 41.87%compared with the latter. The LS-SVM model can effectively predict the tribological behavior of the TiCP/Ni-base alloy composite coatings under dry friction.