An approach for the aging process optimization of Al–Zn–Mg–Cu series alloys

A new model based on least square support vector machines (LSSVM) and capable of forecasting mechanical and electrical properties of Al–Zn–Mg–Cu series alloys has been proposed for the first time. Data mining and artificial intelligence techniques of aluminum alloys are used to examine the forecasting capability of the model. In order to improve predictive accuracy and generalization ability of LSSVM model, a grid algorithm and cross-validation technique has been adopted to determine the optimal hyper-parameters of LSSVM automatically. The forecasting performance of the LSSVM model and the artificial neural network (ANN) has been compared with the experimental values. The result shows that the LSSVM model provides slightly better capability of generalized prediction compared to back propagation network (BPN) in combination with the gradient descent training algorithm. Considering its advantages of the computation speed, unique optimal solution, and generalization performance, the LSSVM model is therefore considered to be used as an alternative powerful modeling tool for the aging process optimization of aluminum alloys. Furthermore, a novel methodology hybridizing nondominated sorting-based multi-objective genetic algorithm (MOGA) and LSSVM has been proposed to make tradeoffs between the mechanical and electrical properties. A desirable nondominated solution set has been obtained and reported.     

A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters

This paper presents a hybrid optimization method for optimizing the process parameters during plastic injection molding (PIM). This proposed method combines a back propagation (BP) neural network method with an intelligence global optimization algorithm, i.e. genetic algorithm (GA). A multi-objective optimization model is established to optimize the process parameters during PIM on the basis of the finite element simulation software Moldflow, Orthogonal experiment method, BP neural network as well as Genetic algorithm. Optimization goals and design variables (process parameters during PIM) are specified by the requirement of manufacture. A BP artificial neural network model is developed to obtain the mathematical relationship between the optimization goals and process parameters. Genetic algorithm is applied to optimize the process parameters that would result in optimal solution of the optimization goals. A case study of a plastic article is presented. Warpage as well as clamp force during PIM are investigated as the optimization objectives. Mold temperature, melt temperature, packing pressure, packing time and cooling time are considered to be the design variables. The case study demonstrates that the proposed optimization method can adjust the process parameters accurately and effectively to satisfy the demand of real manufacture.     

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.     

Prediction of Properties in Thermomechanically Treated Cu-Cr-Zr Alloy by an Artificial Neural Network

A supervised artificial neural network (ANN) to model the nonlinear relationship between parameters of thermomechanical treatment processes with respect to hardness and conductivity properties was proposed for Cu-Cr-Zr alloy. The improved model was developed by the Levenberg-Marquardt training algorithm. A basic repository on the domain knowledge of thermomechanical treatment processes is established via sufficient data acquisition by the network. The results showed that the ANN system is an effective way and can be successfully used to predict and analyze the properties of Cu-Cr-Zr alloy.     

基于人工神经网络的Cu-Cr-Zr合金时效强化性能预测研究

本文首次利用神经网络对Cu—Cr—Zr合金时效温度和时间与硬度和导电率样本集进行学习，采用改进的BP网络算法——Levenberg—Marquardt算法，建立了时效强化工艺BP神经网络模型。预测结果表明：该BP神经网络可以充分挖掘样本蕴含的领域知识，可以对材料性能进行有效预测和分析。     

Back analysis of model parameters in geotechnical engineering by means of soft computing

In this paper, a parameter identification (PI) method for determination of unknown model parameters in geotechnical engineering is proposed. It is based on measurement data provided by the construction site. Model parameters for finite element (FE) analyses are identified such that the results of these calculations agree with the available measurement data as well as possible. For determination of the unknown model parameters, use of an artificial neural network (ANN) is proposed. The network is trained to approximate the results of FE simulations. A genetic algorithm (GA) uses the trained ANN to provide an estimate of optimal model parameters which, finally, has to be assessed by an additional FE analysis. The presented mode of PI renders back analysis of model parameters feasible even for large‐scale models as used in geotechnical engineering. The advantages of theoretical developments concerning both the structure and the training of the ANN are illustrated by the identification of material properties from experimental data. Finally, the performance of the proposed PI method is demonstrated by two problems taken from geotechnical engineering. The impact of back analysis on the actual construction process is outlined. Copyright © 2003 John Wiley & Sons, Ltd.     

模糊聚类和混沌自适应粒子群的神经网络色彩匹配

目的研究基于混沌理论、粒子群算法、模糊聚类和人工神经网络的色彩匹配模型。方法结合混沌理论和动态自适应策略,对粒子群算法进行改进,得到混沌自适应粒子群算法,并应用于径向基人工神经网络的基函数中心,以及扩展常数和网络权值的优化中;通过模糊聚类分类样本数据,得到混沌自适应粒子群径向基人工神经网络色彩匹配模型,并将模型与其他色彩匹配方法进行比较。结果CSAPSO RBF ANN色彩匹配模型的平均绝对误差、均方根误差和色差平均值分别为0.0106,0.000 96和1.9122。结论 CSAPSO RBF ANN色彩匹配模型具有良好的普遍性、通用性和泛化能力。     

Development of two-phase neural network-genetic algorithm hybrid model in modeling damage evolution in roll forming of aluminum sheet

In this paper a two-phase artificial neural network-genetic algorithm (ANN-GA) hybrid model has been developed for the modeling and prediction of the damage evolution in the roll forming (RF) process of aluminum sheet metal, as a function of process parameters. The multilayer perceptron is used to build the network while the genetic algorithm (GA) is employed to optimize the network structure in the modeling phase. In detail, the number of hidden layer, hidden neurons, weights and biases of the network are optimized by GA to minimize the error between predicted values and actual results. After the modeling phase the optimization of parameters is carried out in the optimization phase to minimize the damage in the aluminum sheet during the forming process. In this work the experimental data used for training and verifying the network is obtained automatically by the integration between CAD-CAE. As a result, the predicted results are validated with the actual values and a good agreement is observed. Moreover, the parametric study also is performed to find the relative influences of process parameters on the damage evolution. It is proven that the hybrid model is the powerful tool for modeling and predicting such a highly nonlinear problem as the damage evolution in RF process. The developed two-phase ANN – GA hybrid model is a new approach that can bring benefits to the forming industry by predicting and preventing the failure at the design stage, as well as improving efficiently the product's quality by optimizing the process parameters.     

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.     

A hybrid AI optimization method applied to industrial processes

A hybrid artificial intelligent optimization (HAIO) method, which combines traditional classification pattern recognition (CPR) with artificial neural networks (ANN), applied to industrial processes is presented. This new method includes the fuzzy membership function of sample class, the center cluster of class, a hopeful region, inverse mapping, independent neural network modeling of classified analogy, CPR-ANN, GA (genetic algorithm)-ANN and network-based expert system etc. Some applications of the HAIO are shown.     

ANN–GA approach for predictive modeling and optimization of NOx emission in a tangentially fired boiler

An artificial neural network (ANN) and genetic algorithm (GA) approach to predict NOx emission of a 210 MW capacity pulverized coal-fired boiler and combustion parameter optimization to reduce NOx emission in flue gas, is proposed. The effects of oxygen concentration in flue gas, coal properties, coal flow, boiler load, air distribution scheme, flue gas outlet temperature, and nozzle tilt were studied. The data collected from parametric field experiments was used to build a feed-forward back-propagation neural net. The coal combustion parameters were used as inputs and NOx emission as outputs of the model. The ANN model was developed for full load conditions and its predicted values were verified with the actual values. The algebraic equation containing weights and biases of the trained net was used as fitness function in GA. The genetic search was used to find the optimum level of input operating conditions corresponding to low NOx emission. The results proved that the proposed approach could be used for generating feasible operating conditions.     

基于神经网络遗传算法的GFRP拉挤双目标优化

为研究玻璃钢(GFRP)拉挤工艺参数对复合材料性能的影响,优化最佳拉挤工艺参数,建立了拉挤工艺过程数学模型,结合基于有限元/有限差分的间接解耦法进行求解,模拟得到了拉挤过程中GFRP内部的非稳态温度场和固化度变化情况.分别采用布拉格光栅光纤温度传感器和索氏萃取法检测拉挤GFRP内部的温度与固化度,实测温度和固化度均与模拟温度和固化度吻合,验证了数值模拟程序的正确性.以数值模拟结果为样本,建立反向传播神经网络,得到拉挤工艺参数(固化温度、拉挤速度)与GFRP固化度之间的非线性相关关系,再结合遗传算法解决拉挤过程中固化炉温度和拉挤速度双目标优化问题.优化得到的拉挤工艺参数可在保证复合材料固化度达标的情况下,提高拉挤速度降低固化炉温度,优化效果显著.神经网络遗传算法优化方法能有效解决此类具有复杂非线性关系的多目标优化问题.     

Predicting of mechanical properties of Fe–Mn–(Al, Si) TRIP/TWIP steels using neural network modeling

In this work, an artificial neural network (ANN) model was established in order to predict the mechanical properties of transformation induced plasticity/twinning induced plasticity (TRIP/TWIP) steels. The model developed in this study was consider the contents of Mn (15–30 wt%), Si (2–4 wt%) and Al (2–4 wt%) as inputs, while, the total elongation, yield strength and tensile strength are presented as outputs. The optimal ANN architecture and training algorithm were determined. Comparing the predicted values by ANN with the experimental data indicates that trained neural network model provides accurate results.     

Optimization of chemical composition for TC11 titanium alloy based on artificial neural network and genetic algorithm

It is quite difficult for materials to develop the quantitative model of chemical elements and mechanical properties, because the relationship between them presents the multivariable and non-linear. In this work, the combined approach of artificial neural network (ANN) and genetic algorithm (GA) was employed to synthesize the optimum chemical composition for satisfying mechanical properties for TC11 titanium alloy based on the large amount of experimental data. The chemical elements (Al, Mo, Zr, Si, Fe, C, O, N and H) were chosen as input parameters of the ANN model, and the output parameters are mechanical properties, including ultimate tensile strength, yield strength, elongation and reduction of area. The fitness function for GA was obtained from trained ANN model. It is found that the percentage errors between experimental and predicted are all within 5%, which suggested that the ANN model has excellent generalization capability. The results strongly indicated that the proposed optimization model offers an optimal chemical composition for TC11 titanium alloy, which implies it is a novel and effective approach for optimizing materials chemical composition.     

Autoignition Temperature Prediction Using an Artificial Neural Network with Particle Swarm Optimization

The autoignition temperatures of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) replacing a standard back-propagation algorithm with particle swarm optimization (PSO). A data set of 250 compounds was used for training the network. The optimal condition of the network was obtained by adjusting various parameters by trial-and-error. The capabilities of the designed network were tested in the prediction of the autoignition temperature of 93 compounds not considered during the training step. The proposed model is shown to be more accurate than those of other published works. The results show that the proposed GCM + ANN + PSO method represent an excellent alternative for the estimation of this property with acceptable accuracy (AARD = 1.7%; AAE = 10K).     

基于粒子群支持向量回归的原位自生工艺参数优化

综合应用激光熔覆和原位反应增强金属基复合材料,是当前金属基复合材料研究领域的一个热点,本文采用该工艺制备铁基表面复合材料,重点考虑该工艺参数的确定问题.根据在不同工艺参数下合成的铁基表面的WC体积分数实测数据集,提出建立不同工艺参数下WC体积分数的支持向量回归预测模型,并与基于人工神经网络模型(ANN)的预测结果进行比较.结果显示:对于相同的训练样本和检验样本,SVR预测模型比ANN预测模型具有更强的泛化能力.最后根据建立的预测模型,应用粒子群算法寻优得到最优工艺参数,该工艺参数在实际实验过程中的应用,验证了该方法的有效性.     

Effect of Mg and Sr-modification on the mechanical properties of 319-type aluminum cast alloys subjected to artificial aging

Aluminum-based 319-type cast alloys are commonly used in the automotive industry to manufacture cylinder heads and engine blocks. These applications require good mechanical properties and in order to achieve them through precipitation hardening, artificial aging treatments are applied to the products. The standard artificial aging treatment for alloy 319, as defined by the T6 heat treatment temper, consists in solution heat-treating the product for 8 h at 495 °C, water quenching at 60 °C, and then artificially aging at 155 °C for 2–5 h.

The present paper reports on aging heat treatments that were performed on four Al–Si–Cu–Mg 319-type alloys: 319 base alloy, Sr-modified 319 alloy, 319 alloy containing 0.4 wt% Mg, and the Sr-modified 319 + 0.4 wt% Mg alloy. This investigation was carried out in order to examine the effect of Sr-modification and additions of Mg on the microhardness, tensile strength and impact properties of 319-type alloys over a range of aging temperatures and times (150–240 °C, for periods of 2–8 h).

The results show that the best combination of properties is found in the Sr-modified alloy containing 0.4 wt% Mg (i.e. alloy 319 + Mg + Sr). Also, the optimum artificial aging temperature changes when Mg is present in the alloy.     

基于子结构和遗传神经网络的递推模型修正方法

根据实际动力响应对结构有限元模型进行修正,是实现损伤识别和健康监测的必要前提。针对基于神经网络的模型修正方法的不足,选用均匀设计法构造样本从而有效减少所需样本数量,而且计算效率高。采用遗传算法优化神经网络权值,提高了运算速度。基于上述研究,提出了基于子结构和神经网络的递推模型修正方法。该方法将结构分解成多层次的子结构,选取适当的损伤因素逐步实现逐级的修正。应用该方法对一网壳结构进行了模型修正,修正中首先采用固有频率作为损伤因素,结果表明遗传算法明显地提高了神经网络的计算速度,最后的递推修正效果令人满意;其次提出了采用小波包频带能量作为损伤因素的修正方法,该方法同样准确有效,并且不再依赖传统的模态分析技术,更为实用便捷。     

A framework for determining MIMO process parameters by a neuro-DM&ACO approach

A framework combining artificial neural network (ANN) modelling technique, data mining and ant colony optimisation (ACO) algorithm is proposed for determining multiple-input multiple-output (MIMO) process parameters from the initial chemical-mechanical planarisation (CMP) processes used in semiconductor manufacturing. Owing to the invisibility of the ANN in the solution procedures, the decision tree approach of data mining is adopted to provide the necessary information for a real-valued ACO. The simulation result demonstrates that the proposed method can be an efficient tool for selecting properly defined parameter combination with the CMP process.     

计算智能技术在陶瓷材料优化设计中的应用

综述了计算智能在陶瓷材料优化设计中的应用现状,阐明了利用人工神经网络以及遗传算法预测陶瓷材料性能和组分优化的方法,介绍了人工神经网络、遗传算法与免疫算法和模拟退火算法相结合的高效计算智能方法以及模糊神经网络在材料设计中的应用,分析了陶瓷材料优化设计中存在的问题并提出了今后的研究方向。