Predictions of toughness and hardness by using chemical composition and tensile properties in microalloyed line pipe steels

Artificial neural networks with multilayer feed forward topology and back propagation algorithm containing two hidden layers are implemented to predict the effect of chemical composition and tensile properties on the both impact toughness and hardness of microalloyed API X70 line pipe steels. The chemical compositions in the forms of “carbon equivalent based on the International Institute of Welding equation (CE_IIW)”, “carbon equivalent based on the Ito-Bessyo equation (CE_Pcm)”, “the sum of niobium, vanadium and titanium concentrations (VTiNb)”, “the sum of niobium and vanadium concentrations (NbV)” and “the sum of chromium, molybdenum, nickel and copper concentrations (CrMoNiCu)”, as well as, tensile properties of “yield strength (YS)”, “ultimate tensile strength (UTS)” and “elongation (El)” are considered together as input parameters of networks while Vickers microhardness with 10 kgf applied load (HV10) and Charpy impact energy at ?10 °C (CVN ?10 °C) are assumed as the outputs of constructed models. For the purpose of constructing the models, 104 different measurements are performed and gathered data from examinations are randomly divided into training, testing and validating sets. Scatter plots and statistical criteria of “absolute fraction of variance (R²)”, and “mean relative error (MRE)” are used to evaluate the prediction performance and universality of the developed models. Based on analyses, the proposed models can be further used in practical applications and thermo-mechanical manufacturing processes of microalloyed steels.     

An artificial neural-network model for impact properties in X70 pipeline steels

An artificial neural-network (ANN) model has been developed for the analysis and simulation of the correlation between the mechanical properties and composition and thermomechanical treatment parameters of high strength, low alloy steels. The input parameters of the model consist of alloy compositions (C, Si, Mn, P, S, Cu, Ni, Cr, Mo, Ti, V, Nb, Ca, Al, B) and tensile test results (yield strength, ultimate tensile strength, percentage elongation). The outputs of the ANN model include impact energy (?10 °C). The model can be used to calculate the properties of low alloy steels as a function of alloy composition and thermomechanical treatment variables. The current study achieved a good performance of the ANN model, and the results are in agreement with experimental knowledge.     

ANN model to predict the effects of composition and heat treatment parameters on transformation start temperature of microalloyed steels

The paper presents some results of the research connected with the development of new approach based on the artificial neural network (ANN) of predicting the transformation start temperature of the phase constituents occurring in five steels after continuous cooling. The independent variables in the model are chemical compositions (C, Mn, Nb, Mo, Ti, N, Cu, P, S, Si, Al, V), austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. For purpose of constructing these models, 138 different experimental data were gathered from the literature. The data used in the ANN model are arranged in a format of fourteen input parameters that cover the chemical compositions, initial austenite grain size and cooling rate, and output parameter which is transformation start temperature. In this model, the training and testing results in the ANN have shown strong potential for prediction of effects of chemical compositions and heat treatments on phase transformation of microalloyed steels.     

RETRACTED ARTICLE: Prediction water absorption resistance of lightweight geopolymers by artificial neural networks

In the present study, the Charpy impact energy of ferritic and austenitic functionally graded steel produced by electroslag remelting has been modeled in crack divider configuration. To produce functionally graded steels, two slices of plain carbon steel and austenitic stainless steels were spot welded and used as electroslag remelting electrode. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during remelting stage. Vickers microhardness profile of the specimen has been obtained experimentally and modeled with artificial neural networks. To build the model for graded ferritic and austenitic steels, training, testing and validation using respectively 174 and 120 experimental data were conducted. A good fit equation that correlates the Vickers microhardness of each layer to its corresponding chemical composition was achieved by the optimized network for both ferritic and austenitic graded steels. Afterward, the Vickers microhardness of each layer in functionally graded steels was related to the Charpy impact energy of the corresponding layer. Finally, by applying the rule of mixtures, Charpy impact energy of functionally graded steels in crack divider configuration was found through numerical method. The obtained results from the proposed model are in good agreement with those acquired from the experiments.

     

Prediction of the moment capacity of reinforced concrete slabs in fire using artificial neural networks

In this study, the application of artificial neural networks (ANN) to predict the ultimate moment capacity of reinforced concrete (RC) slabs in fire is investigated. An ANN model is built, trained and tested using 294 data for slabs exposed to fire. The data used in the ANN model consists of seven input parameters, which are the distance from the extreme fiber in tension to the centroid of the steel on the tension side of the slab (d′), the effective depth (d), the ratio of previous parameters (d′/d), the area of reinforcement on the tension face of the slab (A_s), the fire exposure time (t), the compressive strength of the concrete (f_cd), and the yield strength of the reinforcement (f_yd). It is shown that ANN model predicts the ultimate moment capacity (M_u) of RC slabs in fire with high degree of accuracy within the range of input parameters considered. The moment capacities predicted by ANN are in line with the results provided by the ultimate moment capacity equation. These results are important as ANN model alleviates the problem of computational complexity in determining M_u.     

Predicting flow strength of austenitic steels with an IPANN model using different training strategies

Model construction and training strategies of an IPANN were developed to improve the prediction accuracy of the hot strength of a series of austenitic steels with different carbon content deformed under a wide range of conditions. The prediction accuracy is largely dependent on the training schemes and model structure because the flow strength varies with deformation conditions and chemical compositions in a very complex way. The scheme for selecting training data of every independent input was optimised, so that a generalised model could be achieved with less training data. With the strategies introduced in this work, the effect of the carbon content and deformation was accurately presented in both the work hardening and dynamic recrystallisation regimes.     

Application of artificial neural networks for analytical modeling of Charpy impact energy of functionally graded steels

In the present study, the Charpy impact energy of ferritic and austenitic functionally graded steel produced by electroslag remelting has been modeled in crack divider configuration. To produce functionally graded steels, two slices of plain carbon steel and austenitic stainless steels were spot welded and used as electroslag remelting electrode. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during remelting stage. Vickers microhardness profile of the specimen has been obtained experimentally and modeled with artificial neural networks. To build the model for graded ferritic and austenitic steels, training, testing and validation using, respectively, 174 and 120 experimental data were conducted. According to the input parameters, in the neural networks model, the Vickers microhardness of each layer was predicted. A good fit equation that correlates the Vickers microhardness of each layer to its corresponding chemical composition was achieved by the optimized network for both ferritic and austenitic graded steels. Afterward, the Vickers microhardness of each layer in functionally graded steels was related to the yield stress of the corresponding layer and by assuming Holloman relation for stress–strain curve of each layer, the area under each stress–strain curve was acquired. Finally, by applying the rule of mixtures, Charpy impact energy of functionally graded steels in crack divider configuration was found through numerical method. The obtained results from the proposed model are in good agreement with those acquired from the experiments.     

Artificial neural network to predict the effects of coating parameters on layer thickness of chromium carbonitride coating on pre-nitrided steels

Chromium carbonitride coatings were formed on plain carbon and alloy steels by pre-nitrocarburizing, followed by thermoreactive deposition and diffusion in a salt bath below 700 °C. In the present study, an artificial neural network-based model (ANNs) was developed to predict the layer thickness of pre-nitrided steels. Seventeen parameters affecting the layer thickness were considered as inputs, including the pre-nitriding time, salt bath compositions ratio, salt bath aging time, ferrochromium particle size, ferrochromium weight percent, salt bath temperature, coating time, and different chemical compositions of steels. The network was then trained to predict the layer thickness amounts as outputs. A 2-feed-forward back-propagation network was developed and trained using experimental data form literatures. Five steels were investigated. The effects of coating parameters on the layer thickness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones indicating that the developed model is very accurate and has the great ability for predicting the layer thickness.     

Precipitate/austenite equilibrium in a titanium-niobium ultra low carbon steel

The variation in the equilibrium compositions of austenite and titanium-niobium-carbonitride precipitates in Fe-Ti-Nb-C-N-Mn-Si-Mo is considered over a range of temperatures between 1050 and 1500K and for an alloy composition which is typical of ultra-low carbon titanium-niobium microalloyed steels. The Kohler temperature dependent-subregular solution model is used to describe the austenite phase. The (Ti,Nb)(C,N)_x precipitate phase is described by the Hillert-Staffansson sublattice model for a four-component solution, with components mixed in pairs. The equilibrium between the two phases is explored while allowing for the nonstoichiometry of the precipitate phase. The results are shown to be in good agreement with experimental data on ultra-low carbon microalloyed steels.     

Using Artificial Neural Networks to Investigate the Influence of Temperature on Hot Extrusion of AZ61 Magnesium Alloy

The hot extrusion process of magnesium alloy involves many processing parameters, billet temperature is one of the parameters that directly affect the tensile strength of finished product. Hot extrusion experiments of involving rectangular tubes are conducted at selected billet temperatures of 320, 350, 380 and 400 °C. Artificial neural networks (ANN) analysis then is performed at increments of 10 °C each time between the temperature 320 and 400 °C. Consequently, the magnesium alloy product can be obtained at the optimum tensile strength, as well as the most suitable temperature range for billet heating during hot extrusion process. This study mainly explores the relationship between the billet temperature and product tensile strength of the hot extrusion of magnesium alloy, and obtains the optimum temperature range through ANN analysis, and analyzes the relationship between the temperature and the tensile strength of a rectangular tube for various extrusion speeds and extrusion ratios. Subsequently, experiments are performed to confirm the accuracy of the results by using ANN analysis at different extrusion speeds and extrusion ratios. Finally, observing the microstructure enables researchers to acquire the relationship between the sizes of the crystalline grain of the magnesium alloy product at the different formation temperature.     

On the mechanical strength of biocompatible semi-IPNs of polyvinyl alcohol and polyacrylamide

Semi-interpenetrating polymer networks (semi-IPNs) based on polyvinyl alcohol (PVA) and crosslinked polyacrylamide (PAM) were prepared by redox polymerization. Various specimens were prepared by varying concentration of PVA, acrylamide (AM) and crosslinker MBA to study the effect of different compositions on the structural and mechanical property of semi-IPNs. The structural and morphological characterizations of prepared semi-IPNs were obtained from the studies of FTIR spectroscopy, X-ray diffraction (XRD) and environmental scanning electron microscopy (ESEM). The mechanical properties of pure PVA and semi-IPNs like tensile strength, percentage elongation and deformation under stress were obtained by load-displacement curves. A similar profile for deformation is obtained for all the semi-IPNs. It was found that the elastic modulus, necking behaviour and ultimate failure are largely affected by the chemical composition of the semi-IPNs.     

CALPHAD-based alloy design for advanced automotive steels - Part I: Development of bearing steels with enhanced strength and optimized microstructure

Alloy design is of prime importance for automotive steels to achieve desired properties, such as strength, hardenability and wear resistance. In the present study, CALPHAD-based computational techniques have been successfully utilized to develop advanced steels for automotive applications. The first part of this series describes an integrated computational approach for the compositional modification of bearing steels. A conventional 100CrMn6 bearing steel has been precisely redesigned to achieve strength enhancement with optimized cementite size distribution. The strength of the modified bearing steel was further improved by the addition of 0.2 wt% V using fine vanadium carbide precipitates. Experimental verification of the calculated results confirmed the reliability of the computational method employed in this study.     

Microstructure and mechanical properties of micro tensile specimens made of CuAl10Ni5Fe4 produced by micro casting

In this paper the results of micro tensile tests of specimens produced by vacuum pressure casting (VPC) and centrifugal casting (CC) with varied mould temperatures are described. The microstructure of VPC-micro specimens is clearly different from CC-micro specimens. Due to the large tolerance range of the chemical composition of the aluminum bronze CuAl10Ni5Fe4 and process instabilities the microstructure of batches of VPC-micro specimens can be very fine grained as well as very coarse grained even if the process parameters were nominally the same. As a consequence the mechanical properties of micro specimen show a wide range, e.g. the ultimate tensile strength (VPC, mould temperature T _m = 1,000°C) is between 670 and 1,000 MPa. Within one batch (ca. 15 specimens) the scatter of microstructure and mechanical properties is clearly smaller. If the chemical composition of the material is nearly constant for a larger number of batches, then the production process of vacuum pressure casting leads reproducibly to nearly identical microstructure and mechanical properties.     

Application of integrated soft computing techniques for optimisation of hybrid CO2 laser–MIG welding process

In this paper, artificial neural networks (ANNs), genetic algorithm (GA), simulated annealing (SA) and Quasi Newton line search techniques have been combined to develop three integrated soft computing based models such as ANN–GA, ANN–SA and ANN–Quasi Newton for prediction modelling and optimisation of welding strength for hybrid CO₂ laser–MIG welded joints of aluminium alloy. Experimental dataset employed for the purpose has been generated through full factorial experimental design. Laser power, welding speeds and wires feed rate are considered as controllable input parameters. These soft computing models employ a trained ANN for calculation of objective function value and thereby eliminate the need of closed form objective function. Among 11 tested networks, the ANN with best prediction performance produces maximum percentage error of only 3.21%. During optimisation ANN–GA is found to show best performance with absolute percentage error of only 0.09% during experimental validation. Low value of percentage error indicates efficacy of models. Welding speed has been found as most influencing factor for welding strength.     

Prediction of compressive strength of heavyweight concrete by ANN and FL models

The compressive strength of heavyweight concrete which is produced using baryte aggregates has been predicted by artificial neural network (ANN) and fuzzy logic (FL) models. For these models 45 experimental results were used and trained. Cement rate, water rate, periods (7–28–90 days) and baryte (BaSO₄) rate (%) were used as inputs and compressive strength (MPa) was used as output while developing both ANN and FL models. In the models, training and testing results have shown that ANN and FL systems have strong potential for predicting compressive strength of concretes containing baryte (BaSO₄).     

Deviation-tolerant floating gate structures as a way to design an on-chip learning neural networks

 Hardware implementation of artificial neural networks (ANN) based on MOS transistors with floating gate (Neuron MOS or νMOS) is discussed. Choosing analog approach as a weight storage rather than digital improves learning accuracy, minimizes chip area and power dissipation. However, since weight value can be represented by any voltage in the range of supplied voltage (e.g. from 0 to 3.3 V), minimum difference of two values is very small, especially in the case of using neuron with large sum of weights. This implies that ANN using analog hardware approach is weak against V _dd deviation. The purpose of this paper is to investigate main parts of analog ANN circuits (synapse and neuron) that can compensate all kinds of deviation and to develop their design methodologies.     

Analytical modeling of tensile strength of functionally graded steels

In the present study, the tensile strength of ferritic and austenitic functionally graded steel produced by electroslag remelting has been modeled by artificial neural networks. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during remelting stage. Vickers microhardness profile of the specimen has been obtained experimentally and modeled with artificial neural networks. To build the model for graded ferritic and austenitic steels, training, testing and validation using respectively 174 and 120 experimental data were conducted. According to the input parameters, in the neural networks model, the Vickers microhardness of each layer was predicted. A good-fit equation that correlates the Vickers microhardness of each layer to its corresponding chemical composition was achieved by the optimized network for both ferritic- and austenitic-graded steels. Afterward, the Vickers microhardness of each layer in functionally graded steels was related to the yield stress of the corresponding layer and by assuming Holloman relation for stress–strain curve of each layer, they were acquired. Finally, by applying the rule of mixtures, tensile strength of functionally graded steels configuration was found through a numerical method. The obtained results from the proposed model are in good agreement with those acquired from the experiments.     

An expert system based on artificial neural network for predicting the tensile behavior of tailor welded blanks

The forming behavior of tailor welded blanks (TWB) is influenced by thickness ratio, strength ratio, and weld conditions in a synergistic fashion. In most of the cases, these parameters deteriorate the forming behavior of TWB. It is necessary to predict suitable TWB conditions for achieving better-stamped product made of welded blanks. This is quite difficult and resource intensive, requiring lot of simulations or experiments to be performed under varied base material and weld conditions. Automotive sheet part designers will be greatly benefited if an ‘expert system’ is available that can deliver forming behavior of TWB for varied weld and blank conditions. This work primarily aims at developing an artificial neural network (ANN) model to predict the tensile behavior of welded blanks made of steel grade and aluminium alloy base materials. The important tensile characteristics of TWB are predicted within chosen range of varied blank and weld condition. Through out the work, PAM STAMP 2G^® finite element (FE) code is used to simulate the tensile behavior and to generate output data required for training the ANN. Predicted results from ANN model are compared and validated with FE simulation for two different intermediate TWB conditions. It is observed that the results obtained from ANN are encouraging with acceptable prediction errors. An expert system framework is proposed using the trained ANN for designing TWB conditions that will deliver better formed TWB products.     

Neural networks modeling of shear strength of SFRC corbels without stirrups

Based on developed semi-empirical characteristic equations an artificial neural network (ANN) model is presented to measure the ultimate shear strength of steel fibrous reinforced concrete (SFRC) corbels without shear reinforcement and tested under vertical loading. Backpropagation networks with Lavenberg–Marquardt algorithm is chosen for the proposed network, which is implemented using the programming package MATLAB. The model gives satisfactory predictions of the ultimate shear strength when compared with available test results and some existing models. Using the proposed networks results, a parametric study is also carried out to determine the influence of each parameter affecting the failure shear strength of SFRC corbels with wide range of variables. This shows the versatility of ANNs in constructing relationship among multiple variables of complex physical relationship.