Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel

In order to study the workability and establish the optimum hot forming processing parameters for 42CrMo steel, the compressive deformation behavior of 42CrMo steel was investigated at the temperatures from 850 °C to 1150 °C and strain rates from 0.01 s⁻¹ to 50 s⁻¹ on Gleeble-1500 thermo-simulation machine. Based on these experimental results, an artificial neural network (ANN) model is developed to predict the constitutive flow behaviors of 42CrMo steel during hot deformation. The inputs of the neural network are deformation temperature, log strain rate and strain whereas flow stress is the output. A three layer feed forward network with 12 neurons in a single hidden layer and back propagation (BP) learning algorithm has been employed. The effect of deformation temperature, strain rate and strain on the flow behavior of 42CrMo steel has been investigated by comparing the experimental and predicted results using the developed ANN model. A very good correlation between experimental and predicted result has been obtained, and the predicted results are consistent with what is expected from fundamental theory of hot compression deformation, which indicates that the excellent capability of the developed ANN model to predict the flow stress level, the strain hardening and flow softening stages is well evidenced.     

Effective parameters modeling in compression of an austenitic stainless steel using artificial neural network

In this study, the prediction of flow stress in 304 stainless steel using artificial neural networks (ANN) has been investigated. Experimental data earlier deduced—by [S. Venugopal et al., Optimization of cold and warm workability in 304 stainless steel using instability maps, Metall. Trans. A 27A (1996) 126–199]—were collected to obtain training and test data. Temperature, strain-rate and strain were used as input layer, while the output was flow stress. The back propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. The results of this investigation shows that the R² values for the test and training data set are about 0.9791 and 0.9871, respectively, and the smallest mean absolute error is 14.235. With these results, we believe that the ANN can be used for prediction of flow stress as an accurate method in 304 stainless steel.     

Cr-Mo-B系机械工程用钢高温流变行为及热加工图

在变形温度为850~1150℃、应变速率为0.1~10s -1 的条件下,对Cr-Mo-B系机械工程用钢进行高温热压缩实验。基于真应力-应变曲线,建立输入参数为温度、变形速率、应变和输出参数为流变应力的人工神经网络(ANN)模型。结果表明:神经网络模型的预测精度高,其预测流变应力的均方根误差为1.3858。根据动态材料模型理论(DMM),构建并分析材料在真应变为0.5和0.7时的热加工图,确定了最佳热变形工艺参数:当真应变ε=0.5时,变形温度为1050~1150℃、应变速率为0.1~0.4s -1 区域的功率耗散因子η≥37.20%;当真应变ε=0.7时,变形温度为1000~1150℃、应变速率为0.1~0.6s -1 区域的功率耗散因子η≥35.80%。     

Neural network modelling of flow stress in Ti–6Al–4V alloy with equiaxed and Widmanstätten microstructures

Abstract

In the present study, artificial neural networks (ANNs) were used to model flow stress in Ti–6Al–4V alloy with equiaxed and Widmanstätten microstructures as initial microstructures. Continuous compression tests were performed on a Gleeble 3500 thermomechanical simulator over a wide range of temperatures (700–1100°C) with strain rates of 0˙001–100 s^–1 and true strains of 0˙1–0˙6. These tests have been focused on obtaining flow stress data under varying conditions of strain, strain rate, temperature, and initial microstructure to train ANN model. The feed forward neural network consisted of two hidden layers with a sigmoid activation function and backpropagation training algorithm was used. The architecture of the network includes four input parameters: strain rate ?, Temperature T, true strain ? and initial microstructure and one output parameter: the flow stress. The initial microstructure was considered qualitatively. The ANN model was successfully trained across (α+β) to β phase regimes and across different deformation domains for both of the microstructures. Results show that the ANN model can correctly reproduce the flow stress in the sampled data and it can predict well with the nonsampled data. A graphical user interface was designed for easy use of the model.     

Prediction of the fatigue life of natural rubber composites by artificial neural network approaches

A back-propagation artificial neural network (BP-ANN) model was established to predict fatigue property of natural rubber (NR) composites. The mechanical properties (stress at 100%, tensile strength, elongation at break) and viscoelasticity property (tan δ at 7% strain) of natural rubber composites were utilized as the input vectors while fatigue property (tensile fatigue life) as the output vector of the BP-ANN. The average prediction accuracy of the established ANN was 97.3%. Moreover, the sensitivity matrixes of the input vectors were calculated to analyze the varied affecting degrees of mechanical properties and viscoelasticity on fatigue property. Sensitivity analysis indicated that stress at 100% is the most important factor, and tan δ at 7% strain, elongation at break almost the same affecting degree on fatigue life, while tensile strength contributes least.     

Modeling constitutive relationship of Ti40 alloy using artificial neural network

Constitutive relationship equation reflects the highly non-linear relationship of flow stress as function of strain, strain rate and temperature. It is a necessary mathematical model that describes basic information of materials deformation and finite element simulation. In this paper, based on the experimental data obtained from Gleeble-1500 Thermal Simulator, the constitutive relationship model for Ti40 alloy has been developed using back propagation (BP) neural network. The predicted flow stress values were compared with the experimental values. It was found that the absolute relative error between predicted and experimental data is less than 8.0%, which shows that predicted flow stress by artificial neural network (ANN) model is in good agreement with experimental results. Moreover, the ANN model could describe the whole deforming process better, indicating that the present model can provide a convenient and effective way to establish the constitutive relationship for Ti40 alloy.     

An Artificial Neural Network (ANN) Model for Predicting Instability Regimes in Copper-Aluminum Alloys

Materials workability is one of the important aspects for any process design to achieve quality products. Identifying optimum process parameters like temperature, strain rate, and strain are normally done by trial and error. In recent years, processing maps are used in choosing these parameters for hot working of materials. Identification of these parameters requires certain high-level expertise as well as detailed microstructural evidences. In this study, using the available copper-aluminum alloy data, an Artificial Neural Network (ANN) model has been developed to classify the hot-working process parameters, like temperature, strain rate, flow stress for instability regime, directly from the corrected flow stress data without applying the Dynamic Materials Model (DMM). This model uses four compositions of Cu-Al system, ranging from 0.5% to 6% Aluminum. Details about the ANN architecture, and the training and testing of these models are explained. The results obtained using the ANN model are compared and validated with those obtained from the processing maps using DMM. It is further shown that even with smaller data set the development of an ANN model is possible as long as the data has some pattern in it.     

Biaxial cyclic deformation of an epoxy resin: Experiments and constitutive modeling

Biaxial (proportional and non-proportional) cyclic tests were conducted on thin-walled tubular specimens to investigate deformation behavior of an epoxy resin, Epon 826/Epi-Cure Curing Agent 9551. The focus was placed on the biaxial stress-strain response and their dependency on the load control mode, stress or strain range and loading path. Experimental results indicated that under strain-controlled equi-biaxial (proportional) cyclic loading, mean stress relaxation occurred in both axial and hoop directions, whereas under stress-controlled equi-biaxial cyclic loading, ratcheting strains accumulated in both principal directions. Under strain- or stress-controlled non-proportional cyclic loading, anisotropy in stress-strain responses was induced in both axial and hoop directions, and the axial and hoop hysteresis loops rotated in opposite directions. This was particularly evident at high stress or strain levels. The experimental results were further used to evaluate the predictive capabilities of a nonlinear viscoelastic constitutive model. Qualitative and quantitative comparison with the test data indicated a good agreement in predicting the complex stress-strain response under biaxial cyclic loading with various loading paths, applied stress or strain ranges and loading control modes.     

Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers

In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.     

Double Glow Plasma Surface Alloying Process Modeling Using Artificial Neural Networks

A model is developed for predicting the correlation between processing parameters and the technical target of double glow by applying artificial neural network (ANN). The input parameters of the neural network (NN) are source voltage, workpiece voltage, working pressure and distance between source electrode and workpiece. The output of the NN model is three important technical targets, namely the gross element content, the thickness of surface alloying layer and the absorpticm rate (the ratio of the mass loss of source materials to the increasing mass of workpiece) in the processing of double glow plasma surface alloying. The processing parameters and technical target are then used as a training set for an artificial neural network. The model is based on multiplayer feedforward neural network. A very good performance of the neural network is achieved and the calculated results are in good agreement with the experimental ones.     

神经网络预测搅拌摩擦加工TC4超塑性行为

目的 研究搅拌摩擦加工工艺改性的Ti–6Al–4V双相钛合金的超塑性变形行为。方法 对360 r/min、30mm/min工艺条件下搅拌摩擦加工处理的TC4钛合金在不同的变形条件下进行超塑性拉伸实验,在实验数据的基础上构建以变形温度、应变速率和晶粒尺寸为输入参数且以峰值应力为输出参数的3–16–1结构的BP人工神经网络模型。应用所构建的BP人工神经网络模型对不同变形条件的Ti–6Al–4V钛合金的超塑性行为进行预测。结果 BP人工神经网络预测的精准度较高,实验应力值与预测应力值吻合度较高,相关系数R=0.991 3,相对误差为1.91%～12.48%,平均相对误差为5.92%。结论 该模型预测的准确性较高,能够客观真实地描述Ti–6Al–4V合金的超塑性变形行为。     

Prediction of metadynamic softening in a multi-pass hot deformed low alloy steel using artificial neural network

The metadynamic softening behaviors in 42CrMo steel were investigated by isothermal interrupted hot compression tests. Based on the experimental results, an efficient artificial neural network (ANN) model was developed to predict the flow stress and metadynamic softening fractions. The effects of deformation parameters on metadynamic softening behaviors in the hot deformed 42CrMo steel have been investigated by the experimental and predicted results from the developed ANN model. Results show that the effects of deformation parameters, such as strain rate and deformation temperature, on the softening fractions of metadynamic recrystallization are significant. However, the strain (beyond the peak strain) has little influence. A very good correlation between experimental and predicted results indicates that the excellent capability of the developed ANN model to predict the flow stress level and metadynamic softening, the metadynamic recrystallization behaviors were well evidenced.     

Prediction of flow stress in dynamic strain aging regime of austenitic stainless steel 316 using artificial neural network

Flow stress during hot deformation depends mainly on the strain, strain rate and temperature, and shows a complex nonlinear relationship with them. A number of semi empirical models were reported by others to predict the flow stress during deformation. In this work, an artificial neural network is used for the estimation of flow stress of austenitic stainless steel 316 particularly in dynamic strain aging regime that occurs at certain strain rates and certain temperatures and varies flow stress behavior of metal being deformed. Based on the input variables strain, strain rate and temperature, this work attempts to develop a back propagation neural network model to predict the flow stress as output. In the first stage, the appearance and terminal of dynamic strain aging are determined with the aid of tensile testing at various temperatures and strain rates and subsequently for the serrated flow domain an artificial neural network is constructed. The whole experimental data is randomly divided in two parts: 90% data as training data and 10% data as testing data. The artificial neural network is successfully trained based on the training data and employed to predict the flow stress values for the testing data, which were compared with the experimental values. It was found that the maximum percentage error between predicted and experimental data is less than 8.67% and the correlation coefficient between them is 0.9955, which shows that predicted flow stress by artificial neural network is in good agreement with experimental results. The comparison between the two sets of results indicates the reliability of the predictions.     

Fatigue life estimation of non‐penetrated butt weldments in ligth metals by artificial neural network approach

This study presents a model for estimating the fatigue life of magnesium and aluminium non‐penetrated butt‐welded joints using Artificial Neural Network (ANN). The input parameters for the network are stress concentration factor K_t and nominal stress amplitude s_a,n. The output parameter is the endurable number of load cycles N. Fatigue data were collected from the literature from three different sources. The experimental tests, on which the fatigue data are based, were carried out at the Fraunhofer Institute for Structural Durability and System Reliability (LBF), Darmstadt – Germany. The results determined with use of artificial neural network for welded magnesium and aluminium joints are displayed in the same scatter bands of SN‐lines. It is observed that the trained results are in good agreement with the tested data and artificial neural network is applicable for estimating the SN‐lines for non‐penetrated welded magnesium and aluminium joints under cyclic loading.     

Energy-based fatigue life prediction of fiberglass/epoxy composites using modular neural networks

The strain energy has successfully been used in the past as a fatigue failure criterion for unidirectional fiber reinforced laminae. This approach has the ability to unify the macro- and microscopic behavior and can allow for extending the failure criterion to incorporate the multiaxial case. In this work, the strain energy will be used as an input to the artificial neural network (ANN) to predict fatigue failure. The results obtained will be compared to those obtained using the maximum applied stress, the fiber orientation angle and the stress ratio as inputs to the ANN.     

Surface roughness modelling in hard turning operation of AISI 4140 using CBN cutting tool

In this study, the influence of hardness (H) and spindle speed (N) on surface roughness (Ra) in hard turning operation of AISI 4140 using CBN cutting tool has been studied. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental values and to show the effect of hardness and spindle speed on the surface roughness. Artificial neural network (ANN) and regression methods have been used for modelling of surface roughness in hard turning operation of AISI 4140 using CBN cutting tool. The input parameters are selected to be as hardness and spindle speed and the output is the surface roughness. Regression and artificial neural network optimum models have been presented for predicting surface roughness. The predicted surface roughness by the employed models has been compared with the experimental data which shows the preference of ANN in prediction of surface roughness during hard turning operation. Finally, a reverse ANN model is constructed to estimate the hardness and spindle speed from surface roughness values. The results indicate that the reverse ANN model can predict hardness for the train data and spindle speed for the test data with a good accuracy but the predicted spindle speed for the train data and the predicted hardness for the test data don’t have acceptable accuracy.     

Application of artificial neural network for prediction of the oxidation behavior of aluminized nano-crystalline nickel

In this paper, the applicability of artificial neural network (ANN) for the prediction of the oxidation kinetics of aluminized coating is presented. For developing the model, a consistent set of experimental data i.e. nanocrystalline Ni samples were aluminized by two steps aluminizing process and oxidized at 800, 900 and 1000 °C for various times are used. The exposure time and temperature of oxidation were used as the inputs of the model and the resulting mass gain of oxidized samples as the output of the model. Multi-layer perceptron neural network structure and back-propagation algorithm are used for the training of the model. After testing many different ANN architectures an optimal structure of the model i.e. 2-5-6-1 is obtained. Comparison of experimental and predicted values using the proposed ANN model shows that there is a good agreement between them with mean relative error less than 1.2%. This shows that the ANN model is an accurate and reliable approach to predict the oxidation behavior of aluminized nanocrystalline coatings.     

Artificial Neural Network Model for Predicting Stable and Unstable Regions in Cu-Zn Alloys

Processing maps are developed using the Dynamic Materials Model (DMM) and instability criterion, which help in choosing optimum process parameters for hot-working of materials. Certain high-level expertise is required to interpret and extract the information on instability regimes to be avoided during processing. In recent years, Artificial Neural Network (ANN) models have been developed to predict flow stress by using the input vector; namely, temperature, strain rate and strain. In this study, using the available Cu-Zn alloy data, ANN model has been developed to classify the hot-working process parameters, such as temperature, strain rate and flow stress for instability regime, directly from the corrected flow stress data without applying the DMM. This model uses 10 compositions of Cu-Zn system, ranging from 3% Zn to 51% Zn. The developed ANN model has been able to learn the nonlinear classifier, which separates unstable region from the stable region in the Cu-Zn alloy system with zinc content less than 40%.     

基于人工神经网络的Mg-Zn-Zr-Ce合金热压缩变形研究

为研究含稀土元素铈的镁合金中高温流变行为,利用热模拟试验机对Mg-6Zn-0.5Zr-1.5Ce合金在变形温度523～673 K、应变速率0.001～1 s-1范围内进行热压缩实验.基于真应力真应变实验数据构建了单隐层前馈误差反向传播人工神经网络模型,利用该模型对ZK60-1.5Ce合金的流变应力行为进行预测,并分析了变形温度、应变速率与真应变对流变应力的影响.研究表明:Ce添加可显著细化晶粒;该镁合金的流变应力随变形温度降低和应变速率升高而增加;其流变应力行为可用双曲正弦函数进行描述,依据峰值应力拟合求得该合金的表观激活能为161.13 kJ/mol;变形温度和应变速率对流变应力的影响高于真应变.所建立的人工神经网络模型可以很好地描述该镁合金的流变应力,其预测值与实验数值吻合良好.     

Modelling beta transus temperature of titanium alloys using artificial neural network

An artificial neural network (ANN) model is developed to simulate the non-linear relationship between the beta transus (β_tr) temperature of titanium alloys and the alloy chemistry. The input parameters to the model consist of the concentration of nine elements, i.e. Al, Cr, Fe, Mo, Sn, Si, V, Zr and O, whereas the model output is the β_tr temperature. Good performance of the ANN model was achieved. The interactions between the alloying elements were estimated based on the obtained ANN model. The results showed good agreement with experimental data. The influence of the database scale on ANN model performance was also discussed. Estimation of β_tr temperature through thermodynamic calculation was carried out as a comparison.