Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach

Artificial neural networks (ANNs) models were developed for the analysis and prediction of the relationship between the cutting conditions and the corresponding fractal parameters of machined surfaces in face milling operation. These models can help manufacturers to determine the appropriate cutting conditions, in order to achieve specific surface roughness profile geometry, and hence achieve the desired tribological performance (e.g. friction and wear) between the contacting surfaces. The input parameters of the “ANNs” models are the cutting parameters: rotational speed, feed, depth of cut, pre-tool flank wear and vibration level. The output parameters of the model are the corresponding calculated fractal parameters: fractal dimension “D” and vertical scaling parameter “G”. The model consists of three-layered feed-forward back-propagation neural network. ANNs models were utilized successfully for modeling and predicting the fractal parameters “D” and “G” in face milling operations. Moreover, W–M fractal function was integrated with the developed ANNs models in order to generate an artificially fractal predicted profiles at different cutting conditions. The predicted profiles were found statistically similar to the actual measured profiles of test specimens.     

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。     

基于BP神经网络的铸态高熵合金成分-强度预报

对文献报道的铸态高熵合金的成分和压缩断裂强度进行统计,获得了铸态高熵合金成分(元素种类、含量)、强度(压缩断裂强度)的参数,分别以这些数据作为输入和输出,利用BP人工神经网络建立起其间的关系网络模型.研究表明:所建立的网络很好地反映出铸态高熵合金的成分-强度之间的关系并且具有较好的精度,网络模型可用来预测不同成分铸态高熵合金的压缩断裂强度.该网络对铸态高熵合金的体系设计具有有效的指导作用.     

TA15钛合金热变形工艺-组织的人工神经元预报

TA15钛合金经过不同条件的热约束变形之后进行金相观察,获得了工艺(温度、应变、应变速率、冷却方式)和组织(初生α相含量、初生α相尺寸、初生α相长径比)参数数据,分别以这些数据作为输入和输出,建立了结构为4×6×8×3的BP人工神经网络.研究结果表明:所建立的网络可以很好地反映出材料的工艺-组织之间的关系并且具有一定的精度,网络模型可以用来预测不同变形条件下TA15钛合金的组织,且对于TA15钛合金的实际生产具有有效的指导作用.     

Prediction of mechanical property of E4303 electrode using artificial neural network

Based on the method of artificial neural network, a new approach has been devised to predict the mechanical property of E4303 electrode. The outlined predication model for determining the mechanical propert) of electrode was built upon the production data. The research leverages a back propagation algorithm as the neural network‘ s learning rule. The result indicates that there are positive correlations between the predicted results and the practical production dota. Hence, using the neural network, predication of electrode property can be realized. For the first time, this research prorides a more scientific method for designing electrode.     

Prediction of weld quality in pulsed current GMAW process using artificial neural network

Abstract

Weld joint dimensions and weld metal mechanical properties are important quality characteristics of any welded joint. The success of building these characteristics in any welding situation depends on proper selection-cum-optimisation of welding process parameters. Such optimisation is critical in the pulsed current gas metal arc welding process (GMAW-P), as the heat input here is closely dictated by a host of additional pulse parameters in comparison to the conventional gas metal arc welding process. Neural network based models are excellent alternatives in such situations where a large number of input conditions govern certain outputs in a manner that is often difficult to adjudge a priori. Six individual prediction models developed using neural network methodology are presented here to estimate ultimate tensile strength, elongation, impact toughness, weld bead width, weld reinforcement height and penetration of the final weld joint as a function of four pulse parameters, e.g. peak current, base current, pulse on time and pulse frequency. The experimental data employed here are for GMAW-P welding of extruded sections of high strength Al–Zn–Mg alloy (7005). In each case, a committee of different possible network architectures is used, including the final optimum network, to assess the uncertainty in estimation. The neural network models developed here could estimate all the outputs except penetration fairly accurately.     

A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method

In the present study, artificial neural network (ANN) and regression model were developed to predict surface roughness in abrasive waterjet machining (AWJ) process. In the development of predictive models, machining parameters of traverse speed, waterjet pressure, standoff distance, abrasive grit size and abrasive flow rate were considered as model variables. For this purpose, Taguchi's design of experiments was carried out in order to collect surface roughness values. A feed forward neural network based on back propagation was made up of 13 input neurons, 22 hidden neurons and one output neuron. The 13 sets of data were randomly selected from orthogonal array for training and residuals were used to check the performance. Analysis of variance (ANOVA) and F-test were used to check the validity of regression model and to determine the significant parameter affecting the surface roughness. The statistical analysis showed that the waterjet pressure was an utmost parameter on surface roughness. The microstructures of machined surfaces were also studied by scanning electron microscopy (SEM). The SEM investigations revealed that AWJ machining produced three distinct zones along the cut surface of AA 7075 aluminium alloy and surface striations and waviness were increased significantly with jet pressure.     

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

The static recrystallization 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 static recrystallized fractions. The effects of the deformation temperature, strain rate and deformation degree, as well as initial grain sizes, on the static recrystallization behaviors in two-pass hot compressed 42CrMo steel were investigated by the experiments and ANN model. A very good correlation between the experimental and predicted results from the developed ANN model has been obtained, which indicates that the excellent capability of the developed ANN model to predict the flow stress level and static recrystallization behaviors in two-pass hot deformed 42CrMo steel. The effects of strain rate, deformation temperature and degree of deformation on the static recrystallization behaviors are significant, while those of the initial austenite grain size are slight.     

Prediction of pitting corrosion of surface treated AISI 316L stainless steel by artificial neural network

Abstract

This paper presents an artificial neural network based solution method for modelling the pitting resistance of AISI 316L stainless steel in various surface treated forms. Surface treatment is a promising technique for improving the corrosion resistance of stainless steels. In this study, cyclic polarisation tests were performed before and after surface treatment. Experimental results were modelled by the neural network. The artificial neural network model exhibited superior performance based on the fitness of the observed versus predicted data. The results showed that the predicted data from the neural network model were considerably similar to the experimental data. The model has been saved and can easily be used to predict the corrosion in different surface treatment methods.     

Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm

The present work is aimed at optimizing the surface roughness of die sinking electric discharge machining (EDM) by considering the simultaneous affect of various input parameters. The experiments are carried out on Ti6Al4V, HE15, 15CDV6 and M-250. Experiments were conducted by varying the peak current and voltage and the corresponding values of surface roughness (SR) were measured. Multiperceptron neural network models were developed using Neuro Solutions package. Genetic algorithm concept is used to optimize the weighting factors of the network. It is observed that the developed model is within the limits of the agreeable error when experimental and network model results are compared. It is further observed that the error when the network is optimized by genetic algorithm has come down to less than 2% from more than 5%. Sensitivity analysis is also done to find the relative influence of factors on the performance measures. It is observed that type of material effectively influences the performance measures.     

Corrosion damage of 316L steel surface examined using statistical methods and artificial neural network

Detailed examination of corrosion-induced changes of the 316L steel surface (immersed in 5 wt% NaCl solution) is presented and discussed. The evolution of the stable pit depth (h_av) with the immersion time (t) was established using 3D maps and statistic techniques. It was found that with n ≈ 0.5. Moreover, determination of the pit area allows estimating the curve current density (j) versus the immersion time and it was found that with m ≈ 1. A novel technique for surface corrosion degree determination is based on analysis of 2D grayscale images instead of black and white images showing that corrosion morphology was elaborated. For this purpose a three-layered, feed-forward neural network with the Levenberg–Marquardt backpropagation training algorithm was used. It was shown that a dependence corrosion degree versus immersion time (S-type curve) can be fully described by the proposed procedure.     

Prediction of density,porosity and hardness in aluminum–copper-based composite materials using artificial neural network

The potential of using feed forward backpropagation neural network in prediction of some physical properties and hardness of aluminium–copper/silicon carbide composites synthesized by compocasting method has been studied in the present work. Two input vectors were used in the construction of proposed network; namely weight percentage of the copper and volume fraction of the reinforced particles. Density, porosity and hardness were the three outputs developed from the proposed network. Effects of addition of copper as alloying element and silicon carbide as reinforcement particles to Al–4 wt.% Mg metal matrix have been investigated by using artificial neural networks. The maximum absolute relative error for predicted values does not exceed 5.99%. Therefore, by using ANN outputs, satisfactory results can be estimated rather than measured and hence reduce testing time and cost.     

数控车床主轴变频调速传动控制的优化设计

文章介绍了数控车床主轴变频调速传动控制的设计方法,通过对传动控制的负载特性与容量适配选型、变频调速控制原理及系统的接口设计、干扰的抑制、变频器参数合理设置的分析研究,从而达到优化设计的目的,掌握其运用方法,是确保数控车床主轴传动系统能够稳定、可靠运行的保证。     

人工神经网络方法预报热轧带钢的卷取温度

针对我国热轧带钢生产中普遍存在的卷取温度控制精度较低问题 ,采用神经网络与数学模型相结合的方法 ,以某热轧厂生产为例 ,构造了BP神经网络 ,使卷取温度控制精度达到了± 15℃。     

电火花线切割机丝振动神经网络主动控制的研究

以国产电火花线切割机丝振轻问题为背景,提出一种采用ＳＣＧ神经网络的分层控制系统。在国产ＤＫ３２２０Ｂ线切割机的实际加工实验表明,本文提出的控制策略是可行的,可有效地提高加工精度。     

基于神经网络的表面粗糙度智能预测系统

表面粗糙度趋势分析及预测技术是计算机集成制造系统故障诊断技术发展的迫切需要。本文在讨论神经网络非线性、多因素预测原理及其拓扑结构的基础上,基于神经网络方法设计了智能型的工件表面粗糙度监测预测系统,将非线性预测和多因素预测引入表面粗糙度预测模型中,即在进行工件表面租糙度预测时兼顾了刀具磨损,从而使本系统拥有可靠和高精度的预测效果。     

数控车床位置检测全闭环控制的设计与实现

位置检测系统对保证数控机床的加工精度和速度起着决定性的作用。文章通过位置检测全闭环控制系统的组成、位移检测与测量、光栅尺在数控车床上的结构设计及与系统相关的各种间隙补偿的适配设置和参数调整,论述了数控车床基于伺服驱动的位置检测直接测量系统全闭环控制的设计与实现。     

基于人工神经网络的铝合金铸锭裂纹倾向预测

在电磁半连续铸造条件下,针对不同工艺参数下铝合金圆铸锭的裂纹倾向,建立一种基于多层前馈神经网络的预测模型.网络的输入变量为铝合金铸锭的尺寸、成分以及工艺参数,输出变量为裂纹的量化值,采用改进后的带动量因子的BP训练算法,计算多组不同工艺条件下的裂纹预测值,并进行真实试铸实验.结果表明:裂纹预测结果的最大相对误差为13.9%,最小相对误差为0;在工艺指标控制范围内,模型的裂纹预测曲线能较好地反映铸锭裂纹的真实倾向.     

Prediction of jominy hardness profiles of steels using artificial neural networks

Jominy hardness profiles of steels were predicted from chemical composition and austenitizing temperature using an artificial neural network. The neural network was trained using some 4000 examples, covering a wide range of steel compositions. The performance of the neural network is examined as a function of the network architecture, the number of alloying elements, and the number of data sets used for training. A well-trained network predicts the Jominy hardness profile with an average error of about 2 HRC. Special attention was devoted to the effect of boron on hardenability. A network trained using data only from boron steels produced results similar to those of a network trained using all data available. The accuracy of the predictions of the model is compared with that of an analytical model for hardenability and with that of a partial least- squares model using the same set of data.     

基于人工神经网络的焊接接头力学性能预测系统

对神经元网络在焊接接头力学性能预测上的应用做了探索,训练了焊接方法包括焊条电弧焊、气体保护焊、埋弧焊和TIG焊的抗拉强度、屈服强度、断后伸长率和断面收缩率模型.并在此基础上设计完成了基于人工神经元网络的焊接接头力学性能预测系统.利用可视化界面编程技术和数据库技术制作了友好的人机用户界面.焊接接头力学性能预测系统包括添加...