Narrow Results

(TiBw+TiCp)/Ti6Al4V composites were fabricated by reactive hot-pressing at the temperature range of 800~1200°C using the starting materials of TiB₂, C and Ti6Al4V powders. The XRD results suggested that the reaction between C and Ti happened 900°C and above, while the reaction between TiB₂ and Ti happen at 1100°C and above. SEM results also suggested that the reaction between C and Ti was prior to that between TiB₂ and Ti. With the increase of the sintering temperature, the size of TiC particle and TiB whisker reinforcements increased gradually. The TiC particle was formed at the boundaries of original Ti6Al4V particles, while the TiB whiskers grew toward the inside of Ti6Al4V particles, which resulted in a strong bonding between neighboring Ti6Al4V particles. The results of hardness and relative density tests showed that the (TiBw+TiCp)/Ti6Al4V composites sintered at 1100°C had the highest hardness and relative density compared with that sintered at other temperatures.

The corrosion behavior of the silicon carbide particulates (17.5 vol.%) reinforced 5A06 aluminum alloy metal matrix composite (MMC) and the 5A06 aluminum matrix alloy has been studied in 3.5% NaCl solution at 35°C by using electrochemical impedance spectroscopy (EIS). Analysis of the results, which were obtained from optical (OM) and scanning electron microscopy (SEM) attached with energy dispersion spectroscopy (EDS), indicate that the corrosion behavior of the materials studied was mainly localized corrosion due to the presence of intermetallics (Al(Mn, Fe), Al(Si, Mg)) on the surface and crevices at the interface of SiCp/matrix. More numerous, wider spread, smaller and shallower pits were noted on the composite surface relative to the unreinforced matrix alloy during immersion. However, the pitting morphology on both materials was completely different from crystallographic type of pitting. Crystallographic pitting occurred by anodically polarizing the materials above the pitting potential. Further, the occurrence of an inductive time constant at lower frequencies region and the lower impedance as compared to that of the matrix alloy indicate that the corrosion extent of the composite was more severe than that of the matrix alloy, both materials were immersed for varied periods. Two different equivalent circuits were proposed and applied to analyze the EIS measurements, which can simulate the impedance of the composite and the matrix alloy under corrosive environment.

The crystallization and growth of in situ crystals during non-equilibrium laser rapid melting/solidification process is an important research topic in the fields of both Applied Physics and Materials Science. The present paper studies the development mechanisms of in situ formed Al₄SiC₄ ceramic phase within the selective laser melted SiC/AlSi10Mg composites. Two different-structured Al₄SiC₄ having strip and particle morphologies were disclosed and their growth mechanisms were influenced by laser linear energy density (LED). An elevated LED resulted in a larger degree formation of strip-structured Al₄SiC₄ with the gradually coarsened crystal sizes in its length and thickness. The homogeneously dispersed particle-shaped Al₄SiC₄ exhibited a considerably refined nanostructure with a proper increase in LED, but showing a significant coarsening of particles at an excessive LED.

Magnesium is reinforced with three different weight percentages (5%, 10% and 15%) of SiC particles (200 mesh size) by stir casting technique to fabricate Mg/SiC_p composites. The Scanning Electron Microscope (SEM) images, micro and macro hardness of three different composites are investigated. The comparison of micro and macro hardness clearly shows that increase in the weight percentage of SiC contributed to increase in hardness. However, uniform dispersion of SiC can be achieved while adding 5% SiC in the composite. Then, the Box Behnken experimental design in response surface methodology is employed for machining 3mm diameter hole in the Mg/SiC_p samples using EDM. The second-order model for Material Removal Rate (MRR) and Tool Wear Rate (TWR) are developed with the influencing parameters of weight percentage of SiC, current, pulse on time and pulse off time. The parameter optimization yields maximum MRR and minimum TWR.

Modern technology demands have raised the popularity of aluminum metal matrix composites (AMMCs) as it best suits diverse industrial applications. The need to develop an advanced functional material for specific applications attracts global researchers. Commercial needs for cost-effectiveness, quality improvement, superior performance and high strength to low weight ratio are met by composites. Mass production of AMMCs for specific industrial applications prefer stir casting as a simple and cost-effective manufacturing method. In addition, the production of composites turn more economic by reducing the weight percentage of ceramics and adding natural fibers either in the form of fibers, milled powder or ash to achieve the targeted properties. Process parameters being a dominating factor for minimal defect composites, their effect on final cast products are discussed along with strengthening mechanisms. This paper also discusses the applications, challenges and future scope of natural fiber reinforced AMMCs.

In this study, metal matrix composite (MMC) materials were made with an aluminum matrix (AA7075 alloy) and reinforcement silicon carbide (SiC) elements using molten metal stir and indirect squeeze casting. SiC was used as a reinforcing element in the making of MMC material in different amounts (10%, 14%, and 18%) by mass. Electro Discharge Machining (EDM), cut depth (0.5 mm), three different pulse-on times, three different discharge current values, and a fixed pulse-off time (20 s) were used to machine MMC materials. The effects of machining parameters on machining time, average surface roughness, hole diameter, and material wear difference after machining were studied. As a result of the study, the composite material with 75 $\mu$s pulse-on time, 6A current value, and 10% reinforcement element had the lowest machining time, the largest hole diameter, and the smoothest average surface. These machining parameters and materials also had the shortest machining time (5 min). Based on the signal-to-noise ratios, the best parameters for average surface roughness, hole diameter, Processing time, and material wear amount (MMC, discharge current value, and impact time) were found to be $L_2{L}_1{L}_1$, $L_3{L}_1{L}_1$, $L_1{L}_3{L}_3$, and $L_1{L}_1{L}_2$, respectively. Based on the ANOVA results, the $R$² values for the average surface roughness, hole diameter, machining time, and material wear loss value were 99.3%, 98.7%, 77.8%, and 97.3%, respectively.

Machining of accurate geometrical profiles like triangles, squares or circles with good surface finish and high productivity in metal matrix composites is required for various industrial applications. Thermoelectic principle-based wire electric discharge machine (WEDM) can be conventionally used to overcome the difficulty which occurs during the machining of composites due to the presence of hard abrasive particles. Therefore, in this investigation, three geometrical profiles possessing equal perimeters were produced in 5 mm thick aluminum metal matrix composites (AMMCs) by WEDM. In order to analyze the effect of reinforcements three different AMMCs containing Al6061 as a matrix were produced by stir casting method. The fabricated composites, respectively, contain 10% alumina (${\mathrm{\text{Al}}}_2{\mathrm{\text{O}}}_3$), 10% silicon carbide (SiC) and a mixture of 5% ${\mathrm{\text{Al}}}_2{\mathrm{\text{O}}}_3$ with 5% SiC. The individual effect of various input parameters like pulse-on time ($T_{\text{on}}$ = 30-50 $\mu$s), pulse-off time ($T_{\text{off}}$ = 6–12 $\mu$s), current ($I$ = 1–5 A) and geometrical shapes (circle, triangle, and square) on cutting velocity (CV) and surface roughness (SR) were investigated. Furthermore, parametric analysis using regression models was also performed to evaluate the simultaneous effect of two interacting parameters on CV and SR. The study reveals that in the case of hybrid composite, CV decreases with $T_{\text{on}}$ and SR is always higher for triangular profiles when $T_{\text{on}}$ and $I$ change. The regression plots indicate that the interaction effect of $T_{\text{off}}$ × $I$ plays a major role in CV for all three composites. Multiobjective optimization using a composite desirability approach reveals a marginal increase in CV (8.58%) and a significant reduction (57.72%) in SR at optimal input parameters for Al6061–10% SiC. The morphological analysis at optimal input parameters indicates a significant reduction in resolidified metal drops and microcraters.

Recent developments in the field of manufacturing techniques and alloy development of light materials are reviewed. In the field of manufacturing Aluminium based components, special attention is given to casting, including liquid forging and semi-solid forming technology while for sheet metal forming technology the focus is on material properties and process technology in superplastic forming. For the manufacturing of Magnesium-based components, special attention is given to casting processes and alloy development for casting. For wrought Magnesium, material properties control is covered. For Titanium-based components, an overview of the latest additions to high strength alloys are given, including non-linear elasticity as demonstrated by materials like GUM Metal™. Advanced forming technology such as Levi Casting are also treated.

With a newly developed homogenization cyclic constitutive model of particle reinforced metal matrix composites [Guo et al. (2011)], the effects of tangent operators, i.e., continuum and algorithmic tangent operators [defined by Doghri and Ouaar (2003)] on the accuracy of the developed meso-mechanical constitutive model to predict the monotonic tensile and uniaxial ratchetting deformations of SiC_P/6061Al composites were investigated in this work. The predictions were obtained by the developed model with the choices of different tangent operators and various magnitudes of load increments. Comparison of prediction accuracy and necessary error analysis on the results obtained by different tangent operators were conducted. It is shown that: the stress or strain difference in each load increment and produced by using different tangent operators will accumulate step by step; accurate prediction should be obtained by employing a load increment small enough, especially when the algorithmic tangent operator is used in predicting the uniaxial ratchetting of the composites.

Multiscale analyses considering the stretching problem in plates composed of metal matrix composites (MMC) have been performed using a coupled BEM/FEM model, where the boundary element method (BEM) and the finite element method (FEM) models, respectively, the macrocontinuum and the material microstructure, denoted as representative volume element (RVE). The RVE matrix zone behavior is governed by the von Mises elasto-plastic model while elastic inclusions have been incorporated to the matrix to improve the material mechanical properties. To simulate the microcracks evolution at the interface zone surrounding the inclusions, a modified cohesive fracture model has been adopted, where the interface zone is modeled by means of cohesive contact finite elements to capture the effects of phase debonding. Thus, this paper investigates how this phase debonding affects the microstructure mechanical behavior and consequently affects the macrostructure response in a multiscale analysis. For that, initially, only RVEs subjected to a generic strain are analyzed. Then, multiscale analyses of plates have been performed being each macro point represented by a RVE where the macro-strain must be imposed to solve its equilibrium problem and obtain the macroscopic constitutive response given by the homogenized values of stress and constitutive tensor fields over the RVE.

The prediction of performance measures is an essential one for manufacturers to increase the service life. This paper deals with the application of Artificial Intelligence (AI) to predict the performance measures like surface roughness, material removal rate, and flank wear during the milling process from the experimental data. The milling experiments were conducted in wet conditions based on the Response Surface Methodology (RSM) design of experiments. The spindle speed, feed rate, and axial depth of cut were considered as process parameters. The experimental data were used to develop the regression model, Mamdani fuzzy inference system, Backpropagation Neural Network (BPNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The output of regression, fuzzy, neural network, and ANFIS model was compared with the experimental data, and predicted results were found to be in good conformity with the measured data. The prediction capability of the quadratic and Artificial Neural Network (ANN) model was very close to experimentally measured values and the quadratic model had an accuracy of 97.89% for surface roughness, 98.38% for material removal rate (MRR), and 95.72% for flank wear.

A reciprocating extrusion process was used to produce graphite and alumina reinforced pure aluminium composite. The graphite particles (0~5vol%), alumina particles (10 vol%) and pure aluminium particles (balanced) were dehydrated separately at 70°C in vacuum for 3 hours, and then mixed together. A round billet with 50 mm in diameter was prepared by hot pressing at 350°C with the mixed particles and then extruded to a fully-consolidated goblet-like sample at 480°C and 430MPa by reciprocating extrusion. The results showed that all reinforced particles were refined and uniformly distributed in the matrix by reciprocation extrusion severe plastic deformation. The presence of graphite particles caused the reduction in the friction coefficient and wear rate of the Gr/Al₂O₃/Al composite. Compared with the composite prepared only by alumina particles (10 vol%) and pure aluminium particles, the friction coefficient and wear rate of the Gr/Al₂O₃/Al composite, which contains 5vol% graphite and 10vol% alumina particles, decreased 45.3% and 33.5%, respectively, and thereafter it displays an excellent combination of low friction coefficient (0.37) and wear rate (2.2×10^-7mm³/(N.m)), and appears to be more promising.

In this paper, a mechanism-based strain gradient dependent constitutive equation for two-phase particle reinforced metal matrix composites is presented. By using this strain gradient dependent constitutive equation and the linear perturbation analysis, the effect of strain gradient on adiabatic shear banding in particle reinforced metal matrix composites is investigated. The results have demonstrated that the onset of adiabatic shear banding in the composite with small particles is more prone to occur than in the composite with large particles. This result also means that high strain gradient is a strong driving force for adiabatic shear banding in metal matrix composites.

Dense aluminum-lithium alloy reinforced with up to 20 vol.% SiC_p was prepared from powder mixture using spark plasma sintering process (SPS process). The process, originally developed by Sumitomo Coal Mining Co., has been found to be highly effective for the sintering of ceramic, metallic, and composite materials. Aluminum A 8090 was mixed with silicon carbide particles (SiC_p) by mechanical milling before sintered at 723 K under a pressure of 125 MPa for up to 10 minutes. Relative density of the sintered composite reinforced with 10 vol.% SiC_p was found to exceed 99% of the theoretical value. The Young modulus, yield stress, and ultimate tensile stress of the composite were 91 GPa, 256 MPa, and 332 MPa, respectively, which are, approximately, of the same values as those conventionally hot-isostatic press processed. The elongation of the composite was also found to be higher than that of the conventional one. The microstructure of the sintered composite was observed using both optical and scanning electron microscope. In the region away from the contact surface with the mould wall, the matrix powder was compressed along the vertical direction and elongated in the horizontal direction normal to the applied pressure. At the surface where the specimen was in contact with the mould and punch, the friction force controlled the deformation and thus the shape of the sintered powder. In this paper, the influences of reinforcement volume fractions, sintering temperatures, holding time, and applied pressure are also discussed.

Given the limited natural resources and the ever increasing demand for energy and materials, along with accumulating waste has forced us to think about recycling as a solution. However, a constraint is the change in physical, chemical and mechanical properties associated with recycling. Understanding the factors that influence the properties of materials after recycling presents quite a challenge. The present study is undertaken to investigate the effect of recycling on the microstructure and mechanical properties of a metal matrix composite. An aluminum-based metallic matrix was successfully reinforced with silicon carbide using an innovative disintegrated melt deposition technique. With the same technique, the composite was recycled twice. Microstructure characterization studies conducted using optical and scanning electron microscopy revealed a marginal decrease in porosity levels and SiC particulates size, and no change in distribution pattern of SiC particulates, Al-SiC interfacial integrity and matrix grain morphology. Mechanical properties characterization conducted using a servohydraulic Instron machine revealed an increase in elastic modulus, 0.2% yield strength, ultimate tensile strength and ductility of the recycled materials when compared to that of the material in the as-extruded condition. The obtained mechanical properties were then rationalised in terms of the microstructural characteristics associated with the disintegrated melt deposited composite samples. Particular emphasis is placed to study the effect of recycling on the microstructural characteristics and mechanical properties of the composites synthesized.

In the present work an attempt has been made to study the drilling characteristics of Aluminium Silicon carbide particulate composites. The composites manufactured through stir casting technique for different volume fraction of SiC was machined with solid carbide drills. The various drilling characteristics studied were drill wear, specific power, surface roughness and hole accuracy. The parameters considered for the study were volume fraction of SiC in composites, speed, feed rate and diameter of the drill. The experimental results were used to develop mathematical models for the different characteristics containing linear, quadratic and interactive effects of the parameters considered. The adequacy of the models developed models were checked using F- test and the insignificant effects were eliminated using t- test. The models developed were then subjected to optimization using non-linear programming for minimizing the drill wear. The models can be used for predicting the drilling characteristics and the optimized drilling conditions can be used for achieving better quality in drilling Al/SiCp composites.

This paper reports a study of the tensile properties of a TiB₂-containing metal matrix composite in comparison with matrix alloy (A356). A new direct pouring system was employed to produce several test strips in dry sand moulds. The test specimens were examined by tensile testing in as-cast condition. A large number of anomalous features is observed on all fracture surfaces. This observation shows that oxide films play an important role in the fracture mechanism. The skewed distribution of UTS results was described by Weibull distribution analysis. The filtered cast strips exhibited higher Weibull modulus both in TiB₂-MMC and matrix alloy.

This paper compares the research results obtained from ultra-precision turning and grinding of aluminum-based MMCs reinforced with either SiC or Al₂O₃ particles. Both polycrystalline diamond (PCD) and single crystalline diamond (SCD) tuning tools were used to ultra-precision turning the MMCs at the depths of cut ranging from 0 to 1.6 µm. PCD grinding wheels were used to ultra-precision grinding the MMCs at the depths of cut from 0.1 µm to 1 µm. At the same depth of cut, the surfaces ground with PCD grinding wheels revealed much more ductile streaks on the reinforced ceramic particles than those obtained from SCD turning. Grinding using a 3000-grit diamond wheel at depths of cut of 1 µm and 0.5 µm produced ductile streaks on the Al₂O₃ particles and the SiC particles, respectively.

Aluminum alloy - silicon carbide particulate composites are replacing the existing aluminium alloys in automotive and aerospace industries due to their excellent mechanical properties. However the difficulties in secondary processing of these materials restricts the range of their applications. The presence of hard abrasive particles pose severe problems in machining. In the present work the chip formation of Aluminium silicon carbide composites has been studied. An explosive quick stop device was used to freeze the turning process and photographs were taken using a Versamet optical microscope. The cross section of the chips was also analysed for studying the effect of various parameters like volume fraction of SiC, cutting speed, feed and depth of cut. The chip thickness ratio, chip packing ratio which is a measure of chip disposability and shear angle were determined for various cutting conditions. The mathematical models were developed for the above characteristics which can be used for prediction.

The prediction of performance measures is an essential one for manufacturers to increase the service life. This paper deals with the application of Artificial Intelligence (AI) to predict the performance measures like surface roughness, material removal rate, and flank wear during the milling process from the experimental data. The milling experiments were conducted in wet conditions based on the Response Surface Methodology (RSM) design of experiments. The spindle speed, feed rate, and axial depth of cut were considered as process parameters. The experimental data were used to develop the regression model, Mamdani fuzzy inference system, Backpropagation Neural Network (BPNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The output of regression, fuzzy, neural network, and ANFIS model was compared with the experimental data, and predicted results were found to be in good conformity with the measured data. The prediction capability of the quadratic and Artificial Neural Network (ANN) model was very close to experimentally measured values and the quadratic model had an accuracy of 97.89% for surface roughness, 98.38% for material removal rate (MRR), and 95.72% for flank wear.