Modeling mechanical properties of GTAW welds of commercial titanium alloys with artificial neural network

Artificial neural networks (ANN) were used to model the strength, ductility and hardness of multi-pass welds deposited by gas tungsten arc welding (GTAW) in plates of commercial titanium alloys. The input parameters of the ANN are the alloy composition and heat treatment conditions and its output is one of the mechanical properties of the weld metal of titanium alloys, namely ultimate tensile strength (UTS), yield strength, elongation, reduction of the area (ROA) and hardness. The titanium alloys used in the work include commercially pure titanium, alpha or near-alpha titanium, alpha-beta titanium and beta or near-beta titanium.

Characterization and Modeling of Mechanical Properties of Additively Manufactured Coconut Fiber-Reinforced Polypropylene Composites

In the face of the increased global campaign to minimize the emission of greenhouse gases and the need for sustainability in manufacturing, there is a great deal of research focusing on environmentally benign and renewable materials as a substitute for synthetic and petroleum-based products. Natural fiber-reinforced polymeric composites have recently been proposed as a viable alternative to synthetic materials. The current work investigates the suitability of coconut fiber-reinforced polypropylene as a structural material. The coconut fiber-reinforced polypropylene composites were developed. Samples of coconut fiber/polypropylene (PP) composites were prepared using Fused Filament Fabrication (FFF). Tests were then conducted on the mechanical properties of the composites for different proportions of coconut fibers. The results obtained indicate that the composites loaded with 2 wt% exhibited the highest tensile and flexural strength, while the ones loaded with 3 wt% had the highest compression strength. The ultimate tensile and flexural strength at 2 wt% were determined to be 34.13 MPa and 70.47 MPa respectively. The compression strength at 3 wt% was found to be 37.88 MPa. Compared to pure polypropylene, the addition of coconut fibers increased the tensile, flexural, and compression strength of the composite. In the study, an artificial neural network model was proposed to predict the mechanical properties of polymeric composites based on the proportion of fibers. The model was found to predict data with high accuracy.

Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium

In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).

Mechanical Property Prediction of Commercially Pure Titanium Welds with Artificial Neural Network

Factors that affect weld mechanical properties of commercially pure titanium have been investigated using artificial neural networks. Input data were obtained from mechanical testing of single-pass, autogenous welds, and neural network models were used to predict the ultimate tensile strength, yield strength, elongation, reduction of area, Vickers hardness and Rockwell B hardness. The results show that both oxygen and nitrogen have the most significant effects on the strength while hydrogen has the least effect over the range investigated. Predictions of the mechanical properties are shown and agree well with those obtained using the 'oxygen equivalent' (OE) equations.

Predicting uniaxial compressive strength of serpentinites through physical,dynamic and mechanical properties using neural networks

The uniaxial compressive strength(UCS)of intact rock is one of the most important parameters required and determined for rock mechanics studies in engineering projects.The limitations and difficulty of conducting tests on rocks,specifically on thinly bedded,highly fractured,highly porous and weak rocks,as well as the fact that these tests are destructive,expensive and time-consuming,lead to development of soft computing-based techniques.Application of artificial neural networks(ANNs)for predicting UCS has become an attractive alternative for geotechnical engineering scientists.In this study,an ANN was designed with the aim of indirectly predicting UCS through the serpentinization percentage,and physical,dynamic and mechanical characteristics of serpentinites.For this purpose,data obtained in earlier experimental work from central Greece were used.The ANN-based results were compared with the experimental ones and those obtained from previous analysis.The proposed ANN-based formula was found to be very efficient in predicting UCS values and the samples could be classified with simple physical,dynamic and mechanical tests,thus the expensive,difficult,time-consuming and destructive mechanical tests could be avoided.

Modeling of mechanical properties of as-cast Mg-Li-Al alloys based on PSO-BP algorithm

Artificial neural networks have been widely used to predict the mechanical properties of alloys in material research.This study aims to investigate the implicit relationship between the compositions and mechanical properties of as-cast Mg-Li-Al alloys.Based on the experimental collection of the tensile strength and the elongation of representative Mg-Li-Al alloys,a momentum back-propagation(BP)neural network with a single hidden layer was established.Particle swarm optimization(PSO)was applied to optimize the BP model.In the neural network,the input variables were the contents of Mg,Li and Al,and the output variables were the tensile strength and the elongation. The results show that the proposed PSO-BP model can describe the quantitative relationship between the Mg-Li-Al alloy's composition and its mechanical properties.It is possible that the mechanical properties to be predicted without experiment by inputting the alloy composition into the trained network model.The prediction of the influence of Al addition on the mechanical properties of as-cast Mg-Li-Al alloys is consistent with the related research results.

Prediction of Mechanical Properties of 50CrVA Tempered Steel Strip for Horn Diaphragm Based on BPANN

50CrVA cold-rolled spring steel strip was used to fabricate the diaphragm of the automotive horn. The material parameters which were taken into account in the design of the dia-phragm involve elongation, elastic limit, Young＇s modulus, yield strength and tensile strength. The tempering process was carried out in order to enable the diaphragm to possess the excellent mechanical properties, such as high elastic limit, high fatigue strength and perfect stress relaxation resistance. As a nonlinear information processing system, the backpropagation artificial neural network （BPANN） was applied to predict and simulate the relationship between the mechanical properties of the diaphragm and the tempering process parameters. Experimental results show that a BPANN with 3-8-5 architec-ture is capable of predicting the relationship between the mechanical properties of the diaphragm and the tempering temperature successfully and lays the profound foundations for optimizing the design of the diaphragm. BPANN simulation results show that the tempering temperature ranging from 380 to 420 ℃ contributes to enhancing the comprehensive mechanical properties of the diaphragm including high Young＇s modulus, high elastic limit and high fatigue strength.

THE INFLUENCE OF CAVITY DAMAGE ON MECHANICAL PROPERTIES OF SUPERPLASTICALLY-DEFORMED MATERIALS AND ITS PREDICTION

Aluminium alloy LY12CZ sheet without any pre-treatment has been used to study the influence of cavity damage on the mechanical properties of the superplastically-deformed materials room temper- ature. The experimental results show that: (1) the lower the rate of cavitation as the superplastic strain increases, the higher the superplasticity is; (2) the mechanical properties of superplastically- deformed materials at room temperature decrease as the superplastically-deformed strain increases, especially, a noteworthy decrease in elasticity modulus, yield strenth and ultimate strength at room temperature begins to appear as the level of cavitation by area comes up to about 4%, while at the same level the reduction in area drops down a very large quantity. Then, a three-layer back- propagation neural network has been developed to predict the machanichl properties of the superplasti- cally-deformed material.The results acquired from the neural network are very inspiring

Modeling of the Prediction of Densification Behavior of Powder Metallurgy Al–Cu–Mg/B_4C Composites Using Artificial Neural Networks

Al-Cu-Mg/B4Cp metal matrix composites with reinforcement of up to 20 wt% were produced using the powder metallurgy technique. The effects of reinforcement ratio, reinforcement size, milling time, and compact pressure on the density and porosity of the composites reinforced with 0, 5, 10, and 20 wt% B4C particles were studied. Moreover, an artificial neural network model has been developed for the prediction of the effects of the manufacturing parameters on the density and porosity of powder metallurgy Al-Cu-Mg/B4Cp composites. This model can be used for predicting the densification behavior of Al-Cu-Mg/B4Cp composites produced under reinforcement of different sizes and amounts with various milling times and compact pressures. The mean absolute percentage error for the predicted values did not exceed 1.6%.

An improved neural network model for prediction of mechanical properties of magnesium alloys

An improved neural network model was developed for prediction of mechanical properties in the de-sign and development of new types of magnesium alloys by refining the types of input variables and using a more reasonable algorithm. The results showed that the improved model apparently decreased the prediction errors, and raised the accuracy of the prediction results. Better preprocessing parame-ters were found to be [0.15, 0.90] for the tensile strength, [0.1, 0.9] for the yield strength, and [0.15, 0.90] for the elongation. When the above parameters were used, the relativity for predicition of strength was bigger than 0.95. By using improved ANN analysis, more reasonable process parameters and compo- sition could be obtained in some magnesium alloys without addition of strontoum.

Artificial Neural Networks for Hardness Prediction of HAZ with Chemical Composition and Tensile Test of X70 Pipeline Steels

A neural network with feed-forward topology and back propagation algorithm was used to predict the effects of chemical composition and tensile test parameters on hardness of heat affected zone （HAZ） in X70 pipeline steels. The mass percent of chemical compositions （i. e. carbon equivalent based upon the International Institute of Welding equation （CEIIw）, the carbon equivalent based upon the chemical portion of the ho-Bessyo carbon equivalent equation （CEecm）, the sum of the niobium, vanadium and titanium concentrations （CvTaNb）, the sum of the niobium and vanadium concentrations （CNbv）, the sum of the chromium, molybdenum, nickel and copper concentrations （CcrMoNiCu））, yield strength （YS） at 0. 005 offset, ultimate tensile strength （UTS） and percent elongation （El） were considered as input parameters to the network, while Vickers microhardness with 10 N load was considered as its output. For the purpose of constructing this model, 104 different data were gathered from the experimental re- sul.ts. Scatter diagrams and two statistical criteria, i.e. absolute fraction of variance （R2 ） and mean relative error （MRE）, were used to evaluate the prediction performance of the developed model. The developed model can be fur- ther used in practical applications of alloy and thermo-mechanical schedule design in manufacturing process of pipe line steels.

人工神经元网络模型预测3D打印部件力学性能的研究

熔融沉积成型(FDM)是一种高效的增材制造技术。将响应面模型与人工神经元网络(ANN)模型相结合,研究了FDM工艺的喷嘴温度、层高和层积角度对尼龙12(PA12)丝材制造部件力学性能的影响。当喷嘴温度、层高和层积角度分别在220~260℃、0.2~0.4 mm、0°~90°之间变化时,部件拉伸强度和缺口冲击强度分别在35.69~60.89 MPa和5.48~19.83 kJ/m^(2)之间。喷嘴温度、层高、层积角度以及层积角度的二阶效应是影响部件拉伸强度的显著因素;喷嘴温度、层积角度以及层积角度的二阶效应是影响缺口冲击强度的显著因素。ANN模型预测拉伸强度和缺口冲击强度的最优结构分别是3-10-5-1和3-25-24-1,预测的拉伸强度和缺口冲击强度均方误差函数(MSE)最低分别为2.54×10^(-4)和2.07×10^(-4),回归系数均在0.97以上。与响应面的二次回归模型相比,ANN模型预测的拉伸强度和缺口冲击强度与实验值的标准偏差分别为0.46和0.32,远低于二次回归模型的2.43和1.58,更适合于优化非线性的FDM工艺。

基于神经网络的混杂SiC颗粒增强铝基复合材料力学性能预测

目的提高混杂SiC颗粒增强铝基复合材料的韧性,利用卷积神经网络预测其力学性能,以得到力学性能关键因素的影响规律。方法首先,通过实验得到了铝基复合材料的力学性能数据。其次,基于相场裂纹扩展本构,采用Python代码批量生成了不同构型参数的代表性体积单元,并利用Abaqus软件进行了有限元仿真(FEM)。通过代码实现了建模与仿真的一体化构建,利用得到的仿真数据,建立了神经网络模型,并实现了对复合材料力学性能的预测。建模前,对数据进行预处理和筛选,以提高数据质量并降低模型复杂度。最后,建立卷积神经网络,并优化模型的超参数。结果通过建立的神经网络模型,实现了对复合材料力学性能的有效预测。极限强度的预测误差保持在−7%~8.5%,能耗的预测误差保持在−5%~6%,预测精度较高。结论通过结合实验、仿真和卷积神经网络模型,可以更有效地预测混杂SiC颗粒增强铝基复合材料的力学性能,从而为材料设计和制备提供指导。

喷射成形TiC_(p)/ZA35复合材料热挤压工艺的ANN优化和组织研究

采用人工神经网络(ANN)的方法,研究挤压比、挤压比压、挤压温度和挤压速率对喷射成形TiC_(p)/ZA35复合材料力学性能的影响,建立了TiC_(p)/ZA35复合材料热挤压的人工神经网络模型。模型的输入参数为挤压比、挤压比压、挤压温度和挤压速率,输出参数为复合材料的抗拉强度。该模型可以仿真TiC_(p)/ZA35复合材料在不同热挤压工艺参数下的力学性能,也可以优化热挤压工艺参数,模型结果与实验结果误差小于1.8%,拟合率为0.986。推荐热挤压工艺优化参数为:挤压比22,挤压比压415 MPa,挤压温度315℃,挤压速率8 mm·s^(-1),此工艺条件下复合材料的抗拉强度为486.7 MPa。热挤压间接对复合材料进行了时效处理,材料晶内析出晶须状和颗粒状的MnAl6强化相。弥散强化和位错强化作用使热挤压喷射沉积TiCp/ZA35复合材料较未挤压复合材料抗拉强度提高38.3%。

利用人工神经网络模型预测SS400热轧板带力学性能

针对传统的回归方法的某些不足 ,采用了人工神经网络的方法预测力学性能。从宝钢 2 0 5 0热轧管理机中随机抽取数据 ,用人工神经网络中的BP网络建立原始化学成分和热轧生产的主要工艺参数与产品力学性能之间的关系。离线仿真表明 ,产品力学性能的预报值与实际值拟合良好 ,预报结果的相对误差很小 ,屈服强度相对误差 88%在± 4 %以内 ,抗拉强度的相对误差 86 %在± 2 %以内 ,伸长率的相对误差 78%在± 6%以内。

人工神经网络在纤维增强复合材料力学性能研究中的应用

介绍了人工神经网络的基本工作原理和常用的几种模型。

应用人工神经网络模型预测Ti-10V-2Fe-3Al合金的力学性能

采用人工神经网络方法建立了Ti-10V-2Fe-3Al合金机械性能预测的神经网络模型。模型的输入参数包括变形温度、变形程度、固溶温度、时效温度等热加工工艺参数和热处理制度。模型的输出为钛合金最重要的5个机械性能指标,即抗拉强度、屈服强度、延伸率、断面收缩率和断裂韧性。与传统回归拟合公式相比,该模型具有容错性好、通用性强等优点。该模型可以预测Ti-10V-2Fe-3Al合金在不同热加工工艺参数和热处理制度下的机械性能,也可以用于优化热加工参数和热处理制度。

基于人工神经网络的A357合金力学性能预测(英文)

A357铝合金零件一般都需要经过热处理(T6状态)以获得优异的力学性能。这类零件的性能取决于固溶温度、固溶时间、人工时效温度及人工时效时间。在本研究中,建立了基于反向传播(BP)算法的人工神经网络(ANN)模型,对A357合金的力学性能进行预测,研究了热处理工艺对该合金性能的影响。结果表明,所建立的BP模型能够对A357合金的力学性能进行有效且精度高的预测。良好的神经网络预测能力能够直观地反映A357合金的热处理工艺参数对其力学性能的影响。绘制抗拉强度和伸长率的等值线图形有助于清晰地找到抗拉强度和伸长率之间的关系,可为实际生产中热处理工艺参数的选择提供技术支持。

热轧带钢力学性能预测模型及其应用

为实现对热轧带钢的屈服强度、抗拉强度、断裂延伸率等力学性能的预测及控制,利用人工神经网络技术,分别建立了根据生产工艺参数预测力学性能的质量模型,以及根据力学性能要求对生产工艺参数进行优化的逆质量控制模型.利用质量预测模型,分析得出屈服强度随卷取温度的上升而下降的变化规律,进而可以对组织性能进行在线调整,实现在线应用.

人工神经网络在复合材料研究中的应用

人工神经网络技术广泛应用于复杂系统的建模中 ,已成为材料科学研究中常用的建模方法 ;介绍了BP神经网络及其建模的重要特征 ,综述了神经网络在复合材料性能预测、工艺设计与优化。