Strain hardening behavior of a Fe–18Mn–0.6C–1.5Al TWIP steel

The strain hardening behavior of a Fe–18Mn–0.6C–1.5Al TWIP steel was investigated through the modified Crussard–Jaoul (C–J) analysis and microstructural observations. The strain hardening rate obtained by modified C–J analysis was high up to the critical strain of 37% and then greatly decreased with further strain. The electron backscatter diffraction (EBSD) observation showed that the deformation twinning rate is greatly decreased beyond about 34% strain, indicating that the reduced strain hardening rate at the large strain region is attributed to the deceleration of deformation twinning rate. The volume fraction of twinned region was increased with tensile strain due to the increase in the number of deformation twins not to the lateral growth of each deformation twin.     

Predicting the bake hardenability of steels using neural network modeling

In this work, an artificial neural network (ANN) model for prediction of mechanical properties of baked steels was established. The model introduced here considers the content of carbon, the prestrain amount, the initial yield stress and the baking temperature as inputs. While, the bake hardenability, work hardening values and yield stresses after steel baking are presented as outputs. The network was trained using the data from experimental work and back-propagation algorithm. The results show that the predicted values by the model are much more accurate than the experimental ones. The model suggested a two-stage strengthening for baking of ultra low carbon (ULC) steels, whereas, in the case of low carbon steels only one increment step in strength was reported. Comparing the predicted amounts by ANN model with the experimental ones indicates that well-trained neural network model provides very accurate results.     

航天光学遥感器信噪比的人工神经网络评价

提出基于人工神经网络进行航天光学遥感器信噪比评价的方法,首先对航天遥感图像进行分析,从图像中将与景物结构和噪声有关的特征向量分别提取出来,作为ANN的输入。网络通过对大量信噪比已知的图像样本训练后,可完成对航天光学遥感器传输下来的任意一幅地面景物图像进行系统的信噪比测试,从而避免了采用特定景物目标进行测量中的诸多弊端,测量平均误差低于10%。     

Solidification microstructures and mechanical properties of high-vanadium Fe–C–V and Fe–C–V–Si alloys

Fe–C–V and Fe–C–V–Si alloys of various C, V and Si compositions were investigated in this work. It was found that the phases present in both of these alloy systems were alloyed ferrite, alloyed cementite, and VC_x carbides. Depending on the alloy composition the solidified microstructural constituents were granular pearlite-like, lamellar pearlite, or mixtures of alloyed ferrite + granular pearlite-like or granular pearlite-like + lamellar pearlite. In addition, it is shown that in Fe–C–V alloys the C/V ratio influences (a) the type of matrix, (b) the fraction of vanadium carbides, f_v and (c) the eutectic cell count, N_F. In Fe–C–V alloys, a relationship between the alloy content corresponding to the eutectic line was experimentally determined and can be described by where C_e and V_e are the carbon and vanadium composition of the eutectic. Moreover, in the Fe–C–V alloys (depending on the alloy chemistry), the primary VC_x carbides crystallize with non-faceted or non-faceted/faceted interfaces, while the eutectic morphology is non-faceted/non-faceted with regular fiber-like structures, or it possesses a dual morphology (non-faceted/non-faceted with regular fiber-like structures + non-faceted/faceted with complex regular structures). In the Fe–C–V–Si system, the primary VC_x carbides solidify with a non-faceted/faceted interface, while the eutectic is non-faceted/faceted with complex regular structures. In particular, spiral eutectic growth is observed when Si is present in the Fe–C–V alloys. In general, it is found that as the matrix constituent shifts from predominantly ferrite to lamellar pearlite, the hardness, yield and tensile strengths exhibit substantial increases at expenses of ductility. Moreover, Si additions lead to alloy strengthening by solid solution hardening of the ferrite phase and/or through a reduction in the eutectic fiber spacings with a decrease in the alloy ductility.     

Using GA–ANN algorithm to optimize soft magnetic properties of nanocrystalline mechanically alloyed Fe–Si powders

In this investigation a theoretical model based on artificial neural network (ANN) and genetic algorithm (GA) has been developed to optimize the magnetic softness in nanocrystalline Fe–Si powders prepared by mechanical alloying (MA). The ANN model was used to correlate the milling time, chemical composition, milling speed, and ball to powders ratio (BPR) to coercivity and crystallite size of nanocrystalline Fe–Si powders. The GA–ANN combined algorithm was incorporated to find the optimal conditions for achieving the minimum coercivity. By comparing the predicted values with the experimental data it is demonstrated that the combined GA–ANN algorithm is a useful, efficient and strong method to find the optimal milling conditions and chemical composition for producing nanocrystalline Fe–Si powders with minimum coercivity.     

Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra

In this paper we have compared the abilities of two types of artificial neural networks (ANN): multilayer perceptron (MLP) and wavelet neural network (WNN) — for prediction of three gasoline properties (density, benzene content and ethanol content). Three sets of near infrared (NIR) spectra (285, 285 and 375 gasoline spectra) were used for calibration models building. Cross-validation errors and structures of optimized MLP and WNN were compared for each sample set. Four different transfer functions (Morlet wavelet and Gaussian derivative – for WNN; logistic and hyperbolic tangent – for MLP) were also compared. Wavelet neural network was found to be more effective and robust than multilayer perceptron.     

Application of artificial neural network to study the performance of multi-gravity separator (MGS) treating iron ore fines

The present article describes an attempt made to study the possibility of beneficiating low-grade iron ore fines of Barbil Area of Orissa state, India, using multi-gravity separator (MGS) after grinding the ?10 mm fines to < 75 micron size and prepare a pellet feed of 65% Fe content. For the performance analysis, an artificial neural network (ANN) mathematical modeling approach was attempted. A three-layer feedforward neural network with a backpropagation method has been adopted, considering the three significant parameters of MGS, mainly drum inclination, drum speed, and shake amplitude, were varied and the results were evaluated for grade, recovery, and separation efficiency. The results of beneficiation studies showed that good recovery of hematite is possible with simultaneous increase in Fe(T) grade from 50.74% to 65.26% with 71.25% recovery. The predicted value obtained by ANN shows good agreement with the experimental values.     

Alloy design for Al addition on microstructure and mechanical properties of Ni3(Si,Ti) alloy

The effect of Al addition on the microstructure and tensile properties of Ni₃(Si,Ti) alloys with an L1₂ ordered structure, which were fabricated through thermomechanical processing from arc-melted ingots, was investigated. Al was added to a Ni₃(Si,Ti) alloy by using two methods such that Al substituted for (1) only Ti and (2) both Ni and Ti along a Ni₃(Si,Ti)-Ni₃Al pseudo-binary line. In the case of the alloys prepared by the former method, the addition of more than 4 at.% Al resulted in a two-phase microstructure consisting of disordered fcc Ni solid solution dispersions in the L1₂ matrix, while in the case of the alloys prepared by the latter method, the addition of 4 at.% Al retained the L1₂ single-phase microstructure. In the case of the 4 at.% Al-added alloys, the room-temperature tensile properties were similar and independent of the alloying methods, whereas the high-temperature yield stress was higher in the alloys prepared by the latter method than in the case of the alloys prepared by the former method. These results suggest that a single-phase microstructure consisting of an entire L1₂ structure is favorable for obtaining high-temperature tensile properties.     

Microstructures, densification and mechanical properties of TiC–Al2O3–Al composite by field-activated combustion synthesis

Dense TiC–Al₂O₃–Al composite was prepared with Al, C and TiO₂ powders by means of electric field-activated combustion synthesis and infiltration of the molten metal (here Al) into the synthesized TiC–Al₂O₃ ceramic. An external electric field can effectively improve the adiabatic combustion temperature of the reactive system and overcome the thermodynamic limitation of reaction with x < 10 mol. Thereby, it can induce a self-sustaining combustion synthesis process. During the formation of Al₂O₃–TiC–Al composite, Al is molten first, and reacted with TiO₂ to form Al₂O₃, followed by the formation of TiC through the reaction between the displaced Ti and C. Highly dense TiC–Al₂O₃–Al with relative density of up to 92.5% was directly fabricated with the application of a 14 mol excess Al content and a 25 V cm⁻¹ field strength, in which TiC and Al₂O₃ particles possess fine-structured sizes of 0.2–1.0 μm, with uniform distribution in metal Al. The hardness, bending strength and fracture toughness of the synthesized TiC–Al₂O₃–Al composite are 56.5 GPa, 531 MPa and 10.96 MPa m^1/2, respectively.     

Analysis of workability behavior of Al–SiC P/M composites using backpropagation neural network model and statistical technique

This paper presents an artificial neural network (ANN) model for predicting and analyzing the workability behavior during cold upsetting of sintered Al–SiC powder metallurgy (P/M) metal matrix composites (MMCs) under triaxial stress state condition which is the multifaceted technological concept, depending upon the ductility of the material and the process parameters. The input parameters of the ANN model are the preform density, the particle size, the percentage of reinforcement and the applied load. The output parameters of the model are the axial stress, the hoop stress, the axial strain, the hoop strain, the instantaneous strain hardening index, and the instantaneous strength coefficient. This model is a feed forward backpropagation neural network and is trained and tested with pairs of input/output data. A very good performance of the neural network, in terms of good agreement with the experimental data has been achieved. As a secondary objective, quantitative and statistical analyses were performed in order to evaluate the effect of the process parameters on the workability and the plastic deformation behavior of the composites.     

Prediction of mechanical properties of waste polypropylene/waste ground rubber tire powder blends using artificial neural networks

Recycling represents a valid alternative to the disposal of post-consumer materials if it is possible to obtain new materials with good properties. In this work, waste polypropylene (WPP)/waste ground rubber tire (WGRT) powder blends were studied with respect to the effect of bitumen and maleic anhydride-grafted styrene–ethylene–butylene–styrene (SEBS-g-MA) content by using the design of experiments (DOE) approach, whereby the effect of the four polymers content on the final mechanical properties were predicted. Uniform design method was especially adopted for its advantages. Optimization was done using hybrid artificial neural network–genetic algorithm (ANN–GA) technique. The results indicated that the blends show fairly good ductibility provided that it had a relatively higher concentration of bitumen and SEBS-g-MA under the studied condition. A quantitative relationship was presented between the material concentration and the mechanical properties as a set of contour plots, which were confirmed experimentally by testing the optimum ratio.     

Evaluation of microstructure and mechanical properties of Al/Al2O3/SiC hybrid composite fabricated by accumulative roll bonding process

In this investigation, a new kind of metal matrix composites with a matrix of pure aluminum and hybrid reinforcement of Al₂O₃ and SiC particles was fabricated for the first time by anodizing followed by eight cycles accumulative roll bonding (ARB). The resulting microstructures and the corresponding mechanical properties of composites within different stages of ARB process were studied. It was found that with increasing the ARB cycles, alumina layers were fractured, resulting in homogenous distribution of Al₂O₃ particles in the aluminum matrix. Also, the distribution of SiC particles was improved and the porosity between particles and the matrix was decreased. It was observed that the tensile strength of composites improved by increasing the ARB passes, i.e. the tensile strength of the Al/1.6 vol.% Al₂O₃/1 vol.% SiC composite was measured to be about 3.1 times higher than as-received material. In addition, tensile strength of composites decreased by increasing volume fraction of SiC particles to more than 1 vol.%. Scanning electron microscopy (SEM) observation of fractured surfaces showed that the failure mechanism of broken hybrid composite was shear ductile rupture.     

Microstructural,mechanical and tribological properties of Al–7Si–(0–5)Zn alloys

A series of Al–7Si–(0–5)Zn alloys were produced by permanent mould casting and their microstructure, mechanical and tribological properties were investigated in as-cast state. The microstructure of Al–7Si alloy consisted of α-Al dendrites surrounded by eutectic Al–Si mixture and a small amount of primary silicon particles. Addition of zinc into Al–7Si alloy resulted in the formation of α-solid solution and an increase in size and volume fraction of primary silicon particles. Moreover, these particles gathered inside interdendritic regions of the ternary Al–7Si–Zn alloys. The density, strength and hardness of Al–7Si–Zn alloys increased continuously with increasing zinc content, but their elongation to fracture and impact energy showed a reverse trend. It was also observed that zinc had no significant effect on the friction coefficient of the alloys, but their wear volume decreased with increasing zinc content up to 4%, above which the trend reversed. The wear surfaces of the alloys were characterized mainly by smearing layer with some degree of oxidation. In addition, delamination and fine scratches were observed on the worn surface. It was concluded that the addition of zinc up to 4% improves both mechanical and wear behaviour of Al–7Si alloy.     

Microstructure and mechanical properties of nanocrystalline high strength Al–Mg–Si (AA6061) alloy by high energy ball milling and spark plasma sintering

In the present paper, the microstructure and mechanical properties of nanostructured Al–Mg–Si based AA6061 alloy obtained by high energy ball milling and spark plasma sintering were reported. Gas atomized microcrystalline powder of AA6061 alloy was ball milled under wet condition at room temperature to obtain nanocrystalline powder with grain size of 30 nm. The nanocrystalline powder was consolidated to fully dense compacts by spark plasma sintering (SPS) at 500 °C. The grain size after SPS consolidation was found to be 85 nm. The resultant SPS compacts exhibited microhardness of 190–200 HV_100 g, compressive strength of 800 MPa and strain to fracture of 15%.     

Microstructure and mechanical properties of Al–20Si–5Fe–2X (X = Cu, Ni, Cr) alloys produced by melt-spinning

Al–20Si–5Fe–2X (X = Cu, Ni and Cr) ribbons were produced by melt-spinning and consolidated by hot pressing at 400 °C for 60 min. The microstructure of the ribbons and the consolidated alloys was investigated using optical microscopy (OM), scanning electron microscopy (SEM), X-ray diffractometry (XRD) method, and transmission electron microscopy (TEM). The hardness and compressive strength of the specimens at ambient and elevated temperatures were examined. The microstructure of the ribbons exhibited featureless and dendritic zones. Results of XRD and TEM showed formation of spherically shaped Si particles with an average diameter of 20 nm. Ultrafine Si (110–150 nm) and iron-containing intermetallic particles were noticed in the microstructure of the consolidated ribbons. An improved strength was achieved by alloying of Al–20Si–5Fe with Cu, Ni, and Cr. Nickel was found to be the most effective element in increasing the maximum stress, particularly at elevated temperatures.     

Effect of Al2O3 particles on mechanical properties of directionally solidified intermetallic Ti–46Al–2W–0.5Si alloy

The effect of Al₂O₃ particles on microhardness and room-temperature compression properties of directionally solidified (DS) intermetallic Ti–46Al–2W–0.5Si (at.%) alloy was studied. The ingots with various volume fractions of Al₂O₃ particles and mean ₂–₂ interlamellar spacings were prepared by directional solidification at constant growth rates ranging from 2.78×10⁻⁶ to 1.18×10⁻⁴ ms⁻¹ in alumina moulds. The ingots with constant volume fraction of Al₂O₃ particles and various mean interlamellar spacings were prepared by directional solidification at a growth rate of 1.18×10⁻⁴ ms⁻¹ and subsequent solution annealing followed by cooling at constant rates varying between 0.078 and 1.889 K s⁻¹. The mean ₂–₂ interlamellar spacing λ for both DS and heat-treated (HT) ingots decreased with increasing cooling rate according to the relationship λ∝ ^−0.46. In DS ingots, microhardness, ultimate compression strength, yield strength and plastic deformation to fracture increased with increasing cooling rate. In HT ingots, microhardness and yield strength increased and ultimate compression strength and plastic deformation to fracture decreased with increasing cooling rate. The yield stress increased with decreasing interlamellar spacing and increasing volume fraction of Al₂O₃ particles. A linear relationship between the Vickers microhardness and yield stress was found for both DS and HT ingots. A simple model including the effect of interlamellar spacing and increasing volume fraction of Al₂O₃ particles was proposed for the prediction of the yield stress.     

Mechanical properties and flame-retardant of PP/MRP/Mg(OH)2/Al(OH)3 composites

The flame retardant and mechanical properties of polypropylene (PP) composites filled with microencapsulated red phosphorus (MRP) and magnesium hydrate (Mg(OH)₂)/aluminum hydrate (Al(OH)₃) were measured. It was found that the synergistic effects between the MRP and Mg(OH)₂/Al(OH)₃ on the flame retardant and tensile properties of the composites were significant. The limit oxygen index and smoke density rank of the composites increased nonlinearly while the horizontal combustibility rate decreased nonlinearly with increasing the MRP weight fraction. The Young modulus and the tensile elongation at break increased while the tensile yield strength and tensile fracture strength decreased slightly with increasing the MRP weight fraction. Both the V-notched Izod and Charpy impact strength increased with increasing the MRP weight fraction. Moreover, the tensile yield strength of the composites estimated using an equation published previously was roughly close to the measured data.