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.     

Air pollution prediction by using an artificial neural network model

Clean Technologies and Environmental Policy - Air pollutants impact public health, socioeconomics, politics, agriculture, and the environment. The objective of this study was to evaluate the...     

Modeling of rotary vane compressor applying artificial neural network

The thermal modeling of rotary vane compressor (RVC) was performed in this paper by applying Artificial Neural Network (ANN) method. In the first step, appropriate tests were designed and experimental data were collected during steady state operating condition of RVC in the experimental setup. Then parameters including refrigerant suction temperature and pressure, compressor rotating speed as well as refrigerant discharge pressure were adjusted.With these input values, the operating output parameters such as refrigerant mass flow rate and refrigerant discharge temperature were measured. In the second step, the experimental results were used to train ANN model for predicting RVC operating parameters such as refrigerant mass flow rate and compressor power consumption. These predicted operating parameters by ANN model agreed well with the experimental values with correlation coefficient in the range of 0.962-0.998, mean relative errors in the range of 2.79-7.36% as well as root mean square error (RMSE) 10.59 kg h⁻¹ and 12 K for refrigerant mass flow rate and refrigerant discharge temperature, respectively. Results showed closer predictions with experimental results for ANN model in comparison with nolinear regression model.     

Enhancing methane production by anaerobic co-digestion of extruded organic wastes from slaughterhouse and vegetable market in batch and continuous processes

Clean Technologies and Environmental Policy - The organic wastes generated from centralized wholesale markets from urban centres are predominantly disposed in dumpsites/landfills. Although...     

Quantitative measurement of texture orientation in biomedical images using an artificial neural network

Texture orientation is one of the most important attributes used in biomedical and clinical image interpretation. It provides critical clues of continuity and connectivity useful in relating adjacent image areas. We report a novel approach in which image data are convolved with directional convolution masks and the results are used as input to an artificial neural network for classification of image areas into a number of discrete texture orientation classes. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 351–355, 1998     

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.     

Determination of the diameter distribution of single-wall carbon nanotubes from the Raman G-band using an artificial neural network

A novel, artificial neural network-based method is now available for obtaining the mean diameter of single wall carbon nanotube (SWCNT) samples from the diameter dispersive features of their Raman G-band. The method is demonstrated here for six different diameter SWCNT samples and 14 different excitation wavelengths. With an adequately large pool of standard nanotube samples, the suggested method is a useful complementary technique for SWCNT diameter analysis as it is capable of rapid diameter evaluation without prior knowledge of the relevant phonon dispersion relations.     

Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network

A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000 mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372 kg COD/(m³ day)) and cyclic time (12, 24, and 48 h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O&G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44 kg COD/(m³ day), TDS of 78,000 mg/L and reaction time (RT) of 40 h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100 mg/L and met the discharge limits.     

Modeling and optimization of reactive cotton dyeing using response surface methodology combined with artificial neural network and particle swarm techniques

Clean Technologies and Environmental Policy - This work explores the modeling and optimization of the conditions to obtain blue color intensities in the dyeing cotton process with Reactive Black 5...     

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.     

Erosion wear response of ramie-epoxy composites using artificial neural network integrated with Taguchi technique

This paper aims on evaluating the erosion wear behavior of epoxy composites reinforced with ramie fibers. The possibility of reinforcing ramie fiber to improvise the wear resistance of epoxy is investigated in this study. Composites are fabricated by reinforcing multiple layers of woven ramie fiber mats into epoxy resin using conventional wet lay-up technique and erosion wear trials are conducted using solid particle erosion test setup. Taguchi analysis is done to assess the relative significance of each of the factors influencing the erosion rate using L₁₆ orthogonal array. The analysis reveals that the impact velocity followed by impingement angle are the most significant control factors affecting the erosion wear rate of ramie-epoxy composites. Steady state erosion analysis is done to ascertain the effect of each of the significant factors while keeping other factors fixed. Further, an analytical and predictive model based on the principle of neural computation is used to predict the rate of erosion wear of the composites and the obtained results are compared with the experimental outcomes. The worn morphologies of the eroded surfaces of the composites are studied and analyzed to identify possible mechanisms causing wear.     

A wire-EDM maintenance and fault-diagnosis expert system integrated with an artificial neural network

In wire electrical discharge machining (Wire-EDM), some faults such as wirebreaking and unsatisfactory accuracy may still occur due to improper operations or inappropriate machine maintenance. A maintenance-schedule and fault-diagnosis system that integrates an artificial neural network (ANN) and an expert system (ES) is developed. It is time-saving in knowledge acquisition, is easy to maintain and is capable of self-learning. The occasions which call for machine maintenance are advised automatically. Suggestions to eliminate faults are proposed sequentially according to the inferred priority once a fault is taking place. Moreover, it can provide explanations.     

Prediction of die casting process parameters by using an artificial neural network model for zinc alloys

Pressure die casting is an important production process. In pressure die casting, the first setting of process parameters is established through guess work. Experts use their previous experience and knowledge to develop a solution for a new application. Due to rapid expansion in the die casting process to produce better quality products in a short period of time, there is ever increasing demand to replace the time-consuming and expert-reliant traditional trial and error methods of establishing process parameters. A neural network system is developed to generate the process parameters for the pressure die casting process. The system aims to replace the existing high-cost, time-consuming and expertdependent trial and error approach for determining the process parameters. The scope of this work includes analysing a physical model of the pressure die casting filling stage based on governing equations of die cavity filling and the collection of feasible casting data for the training of the network. The training data were generated by using ZN-DA3 material on a hot chamber die casting machine with a plunger diameter of 60 mm. The present network was developed using the MATLAB application toolbox. In this work, the neural network was developed by comparing three different training algorithms: i.e. error backpropagation algorithm; momentum and adaptive learning algorithm; and Levenberg-Marquardt approximation algorithm. It was found that the Levenberg-Marquardt approximation algorithm was the preferred method for this application as it reduced the sum-squared error to a small value. The accuracy of the developed network was tested by comparing the data generated from the network with those of an expert from a local die casting industry. It was established that by using this network the selection of process parameters becomes much easier, so that it can be used by a novice user without prior knowledge of the die casting process or optimization techniques.     

Modeling slump flow of concrete using second-order regressions and artificial neural networks

High-performance concrete (HPC) is a highly complex material, which makes modeling its behavior a very difficult task. Several studies have independently shown that the slump flow of HPC is not only determined by the water content and maximum size of coarse aggregate, but that is also influenced by the contents of other concrete ingredients. In this paper, the methods for modeling the slump flow of concrete using second-order regression and artificial neural network (ANN) are described. This study led to the following conclusions: (1) The slump flow model based on ANN is much more accurate than that based on regression analysis. (2) It has become convenient and easy to use ANN models for numerical experiments to review the effects of mix proportions on concrete flow properties.     

Prediction of compressive properties of closed-cell aluminum foam using artificial neural network

The design of artificial neural network (ANN) is motivated by analogy of highly complex, non-linear and parallel computing power of the brain. Once a neural network is significantly trained it can predict the output results in the same knowledge domain. In the present work, ANN models are developed for the simulation of compressive properties of closed-cell aluminum foam: plateau stress, Young’s modulus and energy absorption capacity. The input variables for these models are relative density, average pore diameter and cell anisotropy ratio. Database of these properties are the results of the compression tests carried out on aluminum foams at a constant strain rate of 1 × 10⁻³ s⁻¹. The prediction accuracy of all the three models is found to be satisfactory. This work has shown the excellent capability of artificial neural network approach for the simulation of the compressive properties of closed-cell aluminum foam.     

Optimization and evaluation of time-dependent tablets comprising an immediate and sustained release profile using artificial neural network

The aim of this work was to optimize time-dependent tablets using artificial neural network (ANN). The time-dependent tablet consisted of a tablet core, which contained sustained release pellets (70% isosorbide-5-mononitrate [5-ISMN]), immediate release granules (30% 5-ISMN), superdisintegrating agent (sodium carboxymethylstarch, CMS-Na), and other excipients, surrounded by a coating layer composed of a water-insoluble ethylcellulose and a water-soluble channeling agent. The chosen independent variables, i.e., X₁ coating level of tablets, X₂ coating level of pellets, and X₃ CMS-Na level, were optimized with a three-factor, three-level Box-Behnken design. Data were analyzed for modeling and optimizing the release profile using ANN. Response surface plots were used to relate the dependent and the independent variables. The optimized values for the factors X₁-X₃ were 4.1, 14.1, and 29.8%, respectively. Optimized formulations were prepared according to the factor combinations dictated by ANN. In each case, the observed drug release data of the optimized formulations were close to the predicted release pattern. An in vitro model for predicting the effect of food on release behavior of optimized products was used in this study. It was concluded that neural network technique could be particularly suitable in the pharmaceutical technology of time-dependent dosage forms where systems were complex and nonlinear relationships often existed between the independent and the dependent variables.     

Optimization redundancy allocation problem with nonexponential repairable components using simulation approach and artificial neural network

Redundancy allocation is one of the adopted approaches that is used by system designers to improve the performance of systems. In this article, a new model and a novel‐solving method are provided to address the nonexponential redundancy allocation problem in series‐parallel systems with repairable components based on optimization via simulation approach and artificial neural network technique. Despite the previous researches, in this model the failure and repair times of the each component were considered to have nonnegative exponential distributions. This assumption makes the model closer to the reality where most of used components have greater chance to face a breakdown in comparison to new ones. The main aim of this research is the optimization of mean time to the first failure of the system via allocating the best redundant components for each subsystem. Since this objective function of the problem could not be explicitly mentioned, the simulation technique and artificial neural network were applied to model the problem, and different experimental designs were produced using design of experiment methods. To solve the problem, some metaheuristic algorithms were integrated with the simulation method. Several experiments were performed to test the proposed approach, and as the results show, the proposed approach is much more real than previous models, and also the near optimum solutions are promising.     

Prediction of MOR and MOE of structural plywood board using an artificial neural network and comparison with a multivariate regression model

The structural application of plywood boards has increased considerably in recent years. In this context, determining plywood mechanical properties such as bending strength and modulus of elasticity through predictive models using more-easily obtained properties is a very useful tool for in-factory quality control. Artificial neural networks have demonstrated their high capacity for modelling complex relations between variables, considerably improving on results obtained through regression techniques. Four neural networks were developed to obtain these mechanical properties by determining board thickness, moisture content, specific gravity, bending strength and modulus of elasticity of test pieces of small dimensions. The results were compared with those of a regression model and in all cases the results of the present study were better.     

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%。