Simulation and experiment of curve irradiated laser bending process of titanium alloy sheet

Abstract

Laser bending along a curve scanning path is a complex 3D forming process. To analyse the deformation behaviour of the process and the effect of process parameters on the bending angle of sheets, numerical simulation becomes indispensable. In this paper, non-linear transient heat transfer and dynamic structural computational models including thermal mechanical coupling analysis, in which the temperature dependence of the thermal and mechanical properties of the material are taken into account, have been developed. The temperature, stress, and strain fields as well as the effect of process parameters on the bending angle were calculated on the basis of models using the finite element method. Meanwhile, an experimental parameter investigation was also carried out. The results showed that, compared with the linear laser bending, curve irradiated laser bending has a significant reduction of the bending angle and the bending angle decreases with increasing path curvature. The bending angle increases with increasing laser power and decreasing spot diameter, and decreases roughly with decreasing scanning velocity. Good correlation was found between the numerically simulated results and the experimental data.     

Fuzzy logic model for bending angle in laser forming

Abstract

Laser forming is a thermoelastoplastic and complicated process. Finite element model simulation is time consuming and the analytical model is cumbersome for computing the bending angle. A model for bending angle in laser forming is constructed using adaptive fuzzy logic called adaptive network fuzzy inference system (ANFIS). The ANFIS model is trained with the published experiment data, in which the laser forming process parameters include thickness of the plate, laser power, laser beam diameter and scanning velocity. Trained ANFIS model is also tested on experiments not pertaining to previous training data. The performance of ANSIF model is optimised as a function of a type of membership function and number of membership functions. Optimised ANFIS model well predicts the results compared with the experiment data. Based on the established model, the analysis results of the process parameters show that a maximum bending angle can be achieved by choosing a reasonable laser beam diameter given other laser forming parameters.     

An ANFIS model for the prediction of flow stress of Ti600 alloy during hot deformation process

In this paper, an adaptive network-based fuzzy inference system (ANFIS) model has been established to predict the flow stress of Ti600 alloy during hot deformation process. This network integrates the fuzzy inference system with a back-propagation learning algorithm of neural network. The experimental results were obtained from Gleeble-1500 thermal-simulator at deformation temperatures of 800–1100 °C, strain rates of 0.001–10 s^?1, and height reduction of 70%. In establishing this ANFIS model, strain rate, deformation temperature and the strain are entered as input parameters while the flow stress are used as output parameter. After the training process, the fuzzy membership functions and the weight coefficient of the network can be optimized. A comparative evaluation of the predicted and the experimental results has shown that the ANFIS model used to predict the flow stress of Ti600 titanium alloy has a high accuracy and with absolute relative error is less than 17.39%. Moreover, the predicted accuracy of flow stress during hot deformation process of Ti600 titanium alloy using ANFIS model is higher than using traditional regression method, indicating that the ANFIS model was an easy and practical method to predict flow stress for Ti600 titanium alloy.     

单道激光熔覆Inconel 625熔覆层尺寸预测

目的 准确预测激光熔覆Inconel 625熔覆层尺寸。方法 以送粉速率、扫描速度和激光功率为试验变量,以熔覆层的宽度和高度为评价指标,结合中心复合试验设计方法进行试验设计,开展单道激光熔覆试验,探究工艺参数对单道熔覆层尺寸的影响规律,并建立以工艺参数为输入、熔覆层尺寸为输出的BP神经网络模型,利用粒子群算法对BP神经网络模型进行优化,对比分析优化前后模型的预测效果。结果 激光功率对熔覆层宽度的影响最显著,其次是扫描速度,最后是送粉速率;扫描速度对熔覆层高度的影响最显著,其次是激光功率,最后是送粉速率;粒子群算法优化BP神经网络预测模型对熔覆层尺寸的预测精度较高,熔覆层宽度和高度的测量值和预测值之间的平均相对误差分别为4.238%和2.910%。结论 研究成果可以为激光熔覆Inconel 625熔覆层尺寸的调控和预测提供参考。     

Modeling of constitutive relationship of Ti–25V–15Cr–0.2Si alloy during hot deformation process by fuzzy-neural network

In this paper, an adaptive fuzzy-neural network model has been established to model the constitutive relationship of Ti–25V–15Cr–0.2Si alloy during high temperature deformation. The network integrates the fuzzy inference system with a back-propagation learning algorithm of neural network. The experimental results were obtained at deformation temperatures of 900–1100 °C, strain rates of 0.01–10 s⁻¹, and height reduction of 50%. After the training process, the fuzzy membership functions and the weight coefficient of the network can be optimized. It has shown that the predicted values are in satisfactory agreement with the experimental results and the maximum relative error is less than 10%. It proved that the fuzzy-neural network was an easy and practical method to optimize deformation process parameters.     

U形高筋整体壁板激光诱导精密成形实验研究

目的 提高U形高筋整体壁板激光诱导成形精度。方法 根据U形高筋整体壁板特点,首先初步确定成形扫描策略,然后采用几何分析方法计算不同扫描位置成形后的理论变形量,并根据理论变形量对扫描策略和成形工艺参数进行优化。随后开展试验研究。同时利用在线可视化测量手段,对U形高筋壁板的变形量进行测量。结果 扫描道次数和激光器功率大小对U形高筋整体壁板成形精度影响很大,随着扫描次数和激光器功率的增加,变形量随之增大。结论 成形后板材的尺寸误差不大于0.4%,实现了U形高筋整体壁板的高精密成形。     

Modeling and optimization of spring-back in bending process using multiple regression analysis and neural computation

The current work involves both modeling and optimization approaches to achieve minimum spring-back in V-die bending process of heat treated CK67 sheets. Number of 36 experimental tests have been conducted with various levels of sheet orientation, punch tip radius and sheet thickness. Firstly, various predictive models based on statistical analysis, back-propagation neural network (BPNN), counter propagation neural network (CPNN) and radial basis function network (RBFNN) have been developed using experimental observations. Then the accuracy of the developed models has been compared based on values of mean absolute error (MAE), and root mean square error (RMSE). Secondly, the model with lowest values of MAE, and RMSE has been applied as objective function for optimization of process using imperialist competitive algorithm (ICA). After selection of optimal bending parameters, a confirmation test has been conducted to prove the optimal solutions. Results indicated that the radial basis network fulfills precise prediction of process rather than the other developed models. Also, confirmation tests proved that both RBFNN and ICA could predict and optimize the process vigorously.     

Finite Element Analysis of sheet metal bending process to predict the springback

Springback remains a major concern in sheet metal forming process. Springback, shape discrepancy between fully loaded and unloaded configuration due to elastic recovery of material, is mainly affected by geometrical parameters, material properties of sheet and lubrication condition between the blank and the tool. A total-elastic–incremental-plastic (TEIP) algorithm, for large deformation and large rotational problems, was incorporated in indigenous Finite Element software. This software was used to predict the springback in a typical sheet metal bending process and to investigate the influence of these parameters on springback. The results of simulation are validated with own experiments and published experimental results.     

Three-dimensional Numerical Study on Temperature Field of Laser Bending of Sheet Metal

Laser bending of sheet metal is a flexible forming technique by using laser scanning. Based of temperature gradsmechanism, the temperature field of sheet metal bending process by using single laser scanning is studied with theANSYS soft. A finite element     

Influence of beam polarisation in laser bending process

Abstract

In this paper, the influence of beam polarisation in the laser bending process was experimentally studied. Based on the laser beam polarisation, the laser absorption of the metallic specimen could be enhanced by increasing the incident angle. The bending angles are achieved in stainless foil specimens without absorptive coatings, and larger bending angles can be produced by single laser scanning with higher incident angles. Multipass laser bending experiments were also carried out on the same material with graphite coatings. The results show that the bending angle per pass is affected obviously by the beam polarisation at lower laser line energy; and the bending angle per pass increases significantly when the total bending angle is large enough. When using polarised light, the laser beam distortion and laser absorption change induced by the variation of the absorptive coatings and laser incident angle are the two main factors that affect the coupled laser energy. The bending rule is the synthesis effect of the two factors.     

基于弯曲实验的DP980力学性能研究

目的 针对仅通过单向拉伸实验无法准确表征金属板材在弯曲成形过程中的力学性能变化的问题,研究通过弯曲实验获取材料力学性能参数.方法 对高强钢DP980展开力学性能测试研究,主要通过弯曲实验对材料弯曲变形过程中形成的弯矩曲率进行测试,将得到的弯矩曲率转化为应力-应变.分别将弯曲和拉伸得到的应力-应变数据导入到三点弯和辊弯成形有限元仿真中,预测板材的成形角度.结果 DP980弯曲变形时的屈服强度要大于轴向拉伸时的屈服强度;分别利用弯曲和拉伸实验测得的应力-应变数据进行仿真,与三点弯实验结果对比发现,采用弯曲实验得到的应力-应变数据对回弹量的预测偏大,而利用拉伸实验得到的应力-应变数据进行仿真,仿真得到的回弹量则偏小,弯曲实验下变形过程中的应变变化数据更加符合真实过程,与辊弯实验对比发现,利用弯曲实验数据进行仿真可以更准确地预测V形板的最终成形角度.结论 相较于单向拉伸实验,通过弯曲实验获取的材料力学性能参数可以更准确地描述材料在三点弯、辊弯成形过程中的力学性能变化.     

Effects of package geometry on thermal fatigue life and microstructure of Sn–Ag–Cu solder joint

Abstract

Soldering experiments of chip scale package devices were carried out by means of diode laser soldering system with Sn–Ag–Cu solders. In addition, pull tests and a scanning electron microscope were used to analyse the effect of processing parameters on mechanical strength of solder joints. Viscoplastic finite element simulation was utilised to predict solder joint reliability for different package geometry under accelerated temperature cycling conditions. The results indicate that under the conditions of laser continuous scanning mode as well as the fixed soldering time, an optimal power and package geometry exists, while the optimal mechanical properties of microjoints are gained.     

Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel

In order to study the workability and establish the optimum hot forming processing parameters for 42CrMo steel, the compressive deformation behavior of 42CrMo steel was investigated at the temperatures from 850 °C to 1150 °C and strain rates from 0.01 s⁻¹ to 50 s⁻¹ on Gleeble-1500 thermo-simulation machine. Based on these experimental results, an artificial neural network (ANN) model is developed to predict the constitutive flow behaviors of 42CrMo steel during hot deformation. The inputs of the neural network are deformation temperature, log strain rate and strain whereas flow stress is the output. A three layer feed forward network with 12 neurons in a single hidden layer and back propagation (BP) learning algorithm has been employed. The effect of deformation temperature, strain rate and strain on the flow behavior of 42CrMo steel has been investigated by comparing the experimental and predicted results using the developed ANN model. A very good correlation between experimental and predicted result has been obtained, and the predicted results are consistent with what is expected from fundamental theory of hot compression deformation, which indicates that the excellent capability of the developed ANN model to predict the flow stress level, the strain hardening and flow softening stages is well evidenced.     

316L不锈钢选区激光熔化单道熔池几何尺寸演变规律

目的 探究激光功率(P)和扫描速度(v)对单熔道熔池几何特征尺寸的影响规律,以及P–v组合工艺参数对熔池从成形到稳定状态经历的扫描距离的影响规律。方法 以316L为材料,通过单熔道数值仿真分析,建立P–v变量与研究目标之间的影响关系。结果 不同P–v参数组合对熔池几何尺寸的影响规律明显,熔池几何参数达到稳定状态需要经历一定的激光扫描距离(小于1 mm)。随着激光功率增大,熔池长度达到稳定状态所经历的激光扫描距离随之增大,而熔池深度尺寸随之减小。扫描速度增大到400 mm/s时,熔池达到稳定经历的扫描长度缩短了6.7%,扫描速度对熔池稳定性的影响效果不显著。结论 在SLM单道成形过程中,激光功率、扫描速度越大,成形熔池平均长度尺寸也越大;激光功率越大、扫描速度越小,成形熔池深度及平均宽度越大。模拟试验获得重熔效果较好的P–v参数组合为P=200 W、v=800 mm/s,重熔率达到94%。在熔池成形过程中,激光功率对熔池稳定性的影响起主导作用。为了减少成形件的边界翘曲,在打印试件初始成形阶段应在合理激光功率范围内选择较高的激光功率。     

Analysis and prediction of printable bridge length in fused deposition modelling based on back propagation neural network

ABSTRACT

In recent years, additive manufacturing has been developing rapidly mainly due to the ease of fabricating complex components. However, complex structures with overhangs inevitably require support materials to prevent collapse and reduce warping of the part. In this paper, the effects of process parameters on printable bridge length (PBL) are investigated. An optimisation is conducted to maximise the distance between support points, thus minimising the support usage. The orthogonal design method is employed for designing the experiments. The samples are then used to train a neural network for predicting the nonlinear relationships between PBL and process parameters. The results show that the established neural network can correctly predict the longest PBL which can be integrated into support generation process in additive manufacturing for maximising the distance between support points, thus reducing support usage. A framework for integrating the findings of this paper into support generation process is proposed.     

Parametric and non-parametric modelling of earing and hardness of deep drawn cups

Abstract

This study compares different approaches in modelling the earing phenomenon and hardness of cups in deep drawing process. The blank holder force (BHF), annealing temperature and annealing time of blanks before deep drawing process have been chosen as the three influential parameters on the earing and hardness. To obtain mathematical models for the earing and hardness of the deep drawn cups, the methodology of artificial neural networks have been used. Bayesian network, radial basis function network, Gaussian processes and multilayer perceptron are four different ANN approaches that have been used for the modelling. The research has been conducted on a cold rolled Al–Fe–Si (AA8011A) aluminium sheet. After obtaining the mathematical models describing the influence of BHF and annealing on hardness and earing, a comparison of the proposed models has been performed. A search for the optimal parameters of deep drawing process has been carried out.     

神经网络预测搅拌摩擦加工TC4超塑性行为

目的 研究搅拌摩擦加工工艺改性的Ti–6Al–4V双相钛合金的超塑性变形行为。方法 对360 r/min、30mm/min工艺条件下搅拌摩擦加工处理的TC4钛合金在不同的变形条件下进行超塑性拉伸实验,在实验数据的基础上构建以变形温度、应变速率和晶粒尺寸为输入参数且以峰值应力为输出参数的3–16–1结构的BP人工神经网络模型。应用所构建的BP人工神经网络模型对不同变形条件的Ti–6Al–4V钛合金的超塑性行为进行预测。结果 BP人工神经网络预测的精准度较高,实验应力值与预测应力值吻合度较高,相关系数R=0.991 3,相对误差为1.91%～12.48%,平均相对误差为5.92%。结论 该模型预测的准确性较高,能够客观真实地描述Ti–6Al–4V合金的超塑性变形行为。     

铝合金-聚合物复合层板弯曲回弹理论分析

针对铝合金-聚合物复合层板弯曲回弹问题,分析了复合层板弯曲过程表面层铝板及中心层聚合物的变形特征,建立了复合层板平面应变纯弯曲回弹理论分析模型.采用建立的模型预测了复合层板纯弯曲过程回弹角变化,并与实验结果进行了对比,分析了聚合物层厚度及铝合金板材力学性能对回弹的影响规律.结果表明:随着中心聚合物层厚度的增加,复合层板回弹角降低;随着表面层铝板强度的降低,复合层板回弹角减小.理论预测结果与实验结果一致,说明了本文推导的理论模型的可靠性.     

Diode laser bending of tongues from slotted steel tubes

A high power diode laser was used to bend slotted tubes. Two stainless steel tubes, different in diameter but with the same thickness, were cut to generate a rectangular tongue on their cylindrical surface. Subsequently these tongues were formed by diode laser in similar process conditions (laser power, rim speed and focalization condition). Two finite element models were built to predict the laser forming processes for the two different tubes and a good agreement between numerical and experimental results was obtained. Even if the tubes behave in a similar way under laser exposure, a larger deformation of the tongue is present for the biggest tube. Moreover, the deformation history during the laser scan differs. However, in both cases, the single tongue deformed in analogy with a flat sheet metal, despite of the cylindrical surface.     

A comprehensive study on variability of relative density in selective laser melting of Ti-6Al-4V

ABSTRACT

In this research, the effects of Selective Laser Melting (SLM) process parameters comprising laser power, scan speed, hatch space and scan pattern angle on the formation of porosity and subsequently density have been analysed. To improve the mechanical properties, post-processes (heat treatment) must be performed. Therefore, heat treatment was added to the design of experiment to analyse the effect of this process coupling with SLM process parameters on the value of density. A comprehensive design set with five levels for each parameter was selected so Taguchi L25 was used as the design of experiment. The significance of each parameter on obtained results was examined using statistical analysis (F-Test) and numerical model (interrogator analysis). The correlation between two process parameters was discussed by using 3D analytical and contour plots and the mechanisms behind these were discussed in depth. The contribution of this paper is a deep investigation of the relation of process parameters and heat treatment on density based on the Artificial Neural Network model. Results showed that better density is obtained with lower scan speed, laser power and scanning pattern angle. Meanwhile, for heat treatment and hatch space, the best density was obtained in their optimum range.