基于冲压成形困难区域的拉延筋阻力稳健设计

汽车覆盖件冲压过程中,工艺参数和材料参数存在不确定性,这种不确定性导致工艺优化设计的难度增加。以冲压件成形困难的局部区域为质量评价区域,通过冲压因素敏感分析筛选出噪声因素和设计变量,将实验设计与响应面法相结合,构建评价指标与设计变量和噪声因素的响应面模型。在此基础上,将响应面模型、蒙特卡洛模拟技术与遗传算法相结合,建立了基于产品质量工程的稳健优化设计方法。实例分析结果表明,经稳健优化后,冲压件成形质量和工艺稳健性获得了显著提高。     

基于熵权与响应面模型的DPVC工艺多目标优化

为解决真空差压注型成形工艺参数优化问题,提出基于熵值权重法、响应面模型及多种群的改进遗传算法的真空差压注型工艺参数优化方法。采用正交试验设计和数值模拟方法获取试验样本数据,引入熵值权重法,从数理角度确定各项质量指标对产品综合质量的影响权重;结合中心复合实验设计和响应面模型,建立真空差压注型产品的综合质量评价模型;针对传统遗传算法的不足,提出一种基于多种群的改进遗传算法,从而完成对产品综合质量评价模型的优化求解;在自主开发的差压式数字控制V500-ND原型机上,通过对某摩托车前车灯灯罩的工艺参数优化实验,验证了所提方法的有效性,为真空差压注型成形工艺过程的制定提供了一种可行的工艺优化方法。     

基于响应面法的中型挖掘机模型参数标定

针对目前机电液一体化模型建立后,模型中参数难以快速准确标定的问题,把响应面思想应用于中型挖掘机这一类机电液一体化模型参数标定。采用键合图理论建立挖掘机动态数学模型,通过响应面法与遗传算法相结合,实现系统仿真模型中未知参数的自动优化与标定。研究结果表明,把响应面法与遗传算法结合起来,对挖掘机仿真模型中未知参数进行自动优化与标定,具有一定可行性,使用优化标定后的参数,模型仿真曲线与实验曲线吻合度更好。研究结果对挖掘机优化设计有指导意义。     

动车组电机吊架多目标可靠性优化设计

为提高高速动车组电机吊架的承载能力,考虑几何参数的随机性,将响应面法和多目标遗传算法相结合对其进行可靠性优化设计.首先,对电机吊架进行静强度和位移灵敏度分析,找出设计变量;其次,对设计变量进行中心组合试验设计,根据试验设计点来拟合静强度和位移的多项式响应面模型;然后,将几何参数的随机性转化为可靠度,运用最小二乘法拟合可靠度函数方程作为约束条件;最后,运用多目标遗传算法对电机吊架进行可靠性优化设计.研究结果表明:响应面法与结构可靠性优化方法相结合,可以简化求解过程,提高优化结果的可靠度.     

基于NSGA-Ⅱ算法的汽车后背门内板拉延成形质量控制与预测

为避免汽车覆盖件冲压时出现起皱、开裂等成形缺陷,提出基于带精英策略的非排序遗传算法（NSGA-Ⅱ）对冲压成形工艺参数进行优化,实现对冲压成形质量的控制与预测。以某型汽车后背门内板作为研究对象,以压边力、摩擦系数、冲压速度和模具间隙作为试验因素,拉延变形时的最大增厚率和最大减薄率作为质量控制目标。应用拉丁超立方抽样方法并结合CAE分析技术建立试验样本,构建冲压工艺参数同成形质量控制目标之间的响应面模型,基于NSGA-Ⅱ算法在构建的响应面模型内计算获得了满足成形质量的一组最优工艺参数组合,同时给出了成形质量的预测值。实际冲压试模结果证明了文章所提方法的有效性,为汽车覆盖件的冲压成形质量控制与预测提供了一套可借鉴的方法。     

基于随机聚焦搜索算法的冲压成形工艺优化

针对板料冲压成形工艺优化问题,研究了一种群集智能算法。该方法通过正交实验与数字化仿真技术相结合获取神经网络的学习样本,利用反向传播神经网络构建随机聚焦搜索算法的目标函数模型。在此模型基础上,应用随机聚焦搜索算法对板料冲压成形的工艺参数进行优化。以深盒形件为例,将优化后的工艺参数输入eta／DYNAFORM仿真模型进行验证,结果表明该算法可获得较好的成形质量。为了进一步验证随机聚焦搜索算法在执行效率及寻优的全局搜索方面的优越性,与遗传算法的优化结果进行对比分析,说明随机聚焦搜索在板料冲压成形工艺参数优化方面是一种较好的优化算法。     

基于BBD设计和响应面法的隔热罩冲压成形工艺参数优化

通过与实验结果对比,建立发动机隔热罩冲压成形较准确的仿真模型。由此采用BBD设计安排45次实验,获得其最大减薄率和起皱率。以工艺参数为自变量,最大减薄率和起皱率为因变量,分别建立了两者的三次多项式响应面模型,并对其优化。以最大减薄率响应面模型为约束,起皱率响应面模型为目标函数,分别对工艺参数进行优化。优化后的工艺参数,使得最大减薄率和起皱率控制在较好范围,且基于优化响应面模型优化的工艺参数,对应的最大减薄率和起皱率更优。     

基于BP-NSGA的注塑参数多目标智能优化设计

为获得成型性能最优的注塑参数设计方案,提出了基于BP神经网络和非支配排序遗传算法的注塑参数多目标优化方法。将注塑模结构尺寸参数和注塑工艺参数作为待优化的设计变量,建立了以高质量、低成本、高效率为优化目标的注塑参数优化设计模型。基于非支配排序遗传算法获取给定参数范围内的所有Pareto最优解,并通过建立多输入和多输出的BP神经网络来快速获得非支配排序遗传算法优化进程中所有个体的适应度值。开发了基于BP神经网络与非支配排序遗传算法集成的注塑参数智能优化设计系统,并通过鼠标注塑参数设计实例,验证了其适用性和有效性。     

基于遗传算法的高速加工切削参数优化

结合响应面法和遗传算法的原理及应用,以回归统计方法建立了淬硬模具钢高速铣削表面粗糙度的响应面模型,通过遗传算法得到最优的切削工艺参数组合,为高速加工切削参数的选择和表面质量的控制提供了一种有效的方法。     

基于遗传算法的锻模阻力墙结构多目标优化设计

针对传统模锻工艺中飞边槽结构的不足,在综合常规飞边槽与楔形飞边槽优点的基础上,提出锻模新型飞边结构形式--阻力墙.以减少模具磨损和降低成形载荷为目标,通过优化分析得到最佳阻力墙结构参数,为阻力墙的应用提供设计依据.应用拉丁超立方方法对阻力墙结构参数进行抽样,对所得样本进行有限元模拟.将模拟结果作为响应,以阻力墙结构参数为变量,分别建立模具磨损和成形载荷的Kriging模型.基于上述近似模型,采用线性加权法将磨损Kriging模型和载荷Kriging模型转化为单目标函数,利用遗传算法进行全局寻优,得到优化的阻力墙结构参数.采用该方法,充分利用Kriging模型适合计算机仿真的优点,并利用遗传算法适合求解隐式函数优化问题的特点.以曲轴为例,验证了阻力墙的优化设计应用.     

A study of optimization of injection molding process parameters for SGF and PTFE reinforced PC composites using neural network and response surface methodology

This study analyzed variations of mechanical characteristics that depend on the injection molding techniques during the blending of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites. A hybrid method including back-propagation neural network (BPNN), genetic algorithm (GA), and response surface methodology (RSM) are proposed to determine an optimal parameter setting of the injection molding process. The specimens are prepared under different injection molding processing conditions based on a Taguchi orthogonal array table. The results of 18 experimental runs were utilized to train the BPNN predicting ultimate strength, flexural strength, and impact resistance. Simultaneously, the RSM and GA approaches were individually applied to search for an optimal setting. In addition, the analysis of variance was implemented to identify significant factors for the injection molding process parameters and the result of BPNN integrating GA was also compared with RSM approach. The results show that the RSM and BPNN/GA methods are both effective tools for the optimization of injection molding process parameters.     

Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm

During the production of thin shell plastic parts by injection molding, warpage depending on the process conditions is often encountered. In this study, efficient minimization of warpage on thin shell plastic parts by integrating finite element (FE) analysis, statistical design of experiment method, response surface methodology (RSM), and genetic algorithm (GA) is investigated. A bus ceiling lamp base is considered as a thin shell plastic part example. To achieve the minimum warpage, optimum process condition parameters are determined. Mold temperature, melt temperature, packing pressure, packing time, and cooling time are considered as process condition parameters. FE analyses are conducted for a combination of process parameters organized using statistical three-level full factorial experimental design. The most important process parameters influencing warpage are determined using FE analysis results based on analysis of variance (ANOVA) method. A predictive response surface model for warpage data is created using RSM. The response surface (RS) model is interfaced with an effective GA to find the optimum process parameter values.     

System identification of a composite plate using hybrid response surface methodology and particle swarm optimization in time domain

Material properties of composites are identified using a novel hybrid RSM–PSO method in this paper. Different response surface methodology (RSM) methods and particle swarm optimization (PSO) methods are studied initially on a 4 degrees-of-freedom (4DOF) dynamic system on their performance in terms of speed and accuracy. The best combination is used as a hybrid RSM–PSO method to evaluate the performance on system identification of an orthotropic plate along with a 4DOF dynamic system and an isotropic plate. The novelty of the present paper is to identify the composite plate material properties using RSM methods based on time domain signals, which is not hitherto reported in the literature. Also, whereas previous papers have used full factorial design for system identification, here CCDI is used. The input factors (design variables) are the system parameters which are to be identified and the response (objective function) is error sum-of-square of acceleration response with respect to new test system. The performance of the proposed method is also evaluated with the addition of 5% Gaussian noise to simulate the experimental errors. The system parameters of the orthotropic plate were identified with 0% and 0.25% average prediction error with zero and 5% addition of noise respectively by the proposed hybrid RSM–PSO method. It is also showed a much better performance and robustness to noise addition when compared to the other RSM methods in the literature.     

Parametric analysis on electrochemical discharge machining of silicon nitride ceramics

The electrochemical discharge machining (ECDM) process has a potential in the machining of silicon nitride ceramics. This paper describes the development of a second order, non-linear mathematical model for establishing the relationship among machining parameters, such as applied voltage, electrolyte concentration and inter-electrode gap, with the dominant machining process criteria, namely material removal rate (MRR), radial overcut (ROC) and thickness of heat affected zone (HAZ), during an ECDM operation on silicon nitride. The model is developed based on response surface methodology (RSM) using the relevant experimental data, which are obtained during an ECDM micro-drilling operation on silicon nitride ceramics. We also offer an analysis of variance (ANOVA) and a confirmation test to verify the fit and adequacy of the developed mathematical models. From the parametric analyses based on mathematical modelling, it can be recommended that applied voltage has more significant effects on MRR, ROC and HAZ thickness during ECDM micro-drilling operation as compared to other machining parameters such as electrolyte concentration and inter-electrode gap.     

大型复杂钢管焊接结构损伤识别方法研究

将支持向量机引入响应面重构计算中,利用支持向量机对小样本数据优秀的拟合和泛化能力,提出了一种最小二乘支持向量机响应面新方法,并将其应用于大型钢管焊接结构的模型修正及损伤识别中。对最小二乘支持向量机响应面的核函数进行了加权,提出一种综合了一次多项式核函数的线性模拟能力和高斯核函数非线性拟合能力的线性-高斯组合核函数。同时对训练样本进行了尺度变换,并对训练样本的选择方法进行了改进。通过损伤识别数值仿真及实验验证,与传统灵敏度方法进行了对比,结果表明改进响应面方法的识别效果更好,且收敛性及精度也大大提高了,为解决大型复杂结构的损伤识别问题提供了新的思路。     

Measurement and analysis of thrust force in drilling of particle board (PB) composite panels

Particleboard is a wood based composite extensively used in wood working. Drilling is the most commonly used machining process in furniture industries. The surface characteristics and the damage free drilling are significantly influenced by the machining parameters. The thrust force developed during drilling play a major role in gaining the surface quality and minimizing the delamination tendency. The objective of this study is to measure and analyze the cutting conditions which influences the thrust force in drilling of particle board panels. The parameters considered are spindle speed, feed rate and point angle. The drilling experiments are performed based on Taguchi’s design of experiments and a response surface methodology (RSM) based mathematical model is developed to predict the influence of cutting parameters on thrust force. The results showed that high spindle speed with low feed rate combination minimizes the thrust force in drilling of pre-laminated particle board (PB) panels.     

OPTIMAL MQL AND CUTTING CONDITIONS DETERMINATION FOR DESIRED SURFACE ROUGHNESS IN TURNING OF BRASS USING GENETIC ALGORITHMS

The evolving concept of minimum quantity of lubrication (MQL) in machining is considered as one of the solutions to reduce the amount of lubricant to address the environmental, economical and ecological issues. This paper investigates the influence of cutting speed, feed rate and different amount of MQL on machining performance during turning of brass using K10 cemented carbide tool. The experiments have been planned as per Taguchi's orthogonal array and the second order surface roughness model in terms of machining parameters was developed using response surface methodology (RSM). The parametric analysis has been carried out to analyze the interaction effects of process parameters on surface roughness. The optimization is then carried out with genetic algorithms (GA) using surface roughness model for the selection of optimal MQL and cutting conditions. The GA program gives the minimum values of surface roughness and the corresponding optimal machining parameters.     

Analysis of shrinkage and warpage in an injection-molded part with a thin shell feature using the response surface methodology

This paper presents a systematic methodology to analyze the shrinkage and warpage in an injection-molded part with a thin shell feature during the injection molding process. The systematic experimental design based on the response surface methodology (RSM) is applied to identify the effects of machining parameters on the performance of shrinkage and warpage. The experiment plan adopts the centered central composite design (CCD). The quadratic model of RSM associated sequential approximation optimization (SAO) method is used to find the optimum value of machining parameters. One real case study in the injection molding process of polycarbonate/acrylonitrile butadiene styrene (PC/ABS) cell phone shell has been performed to verify the proposed optimum procedure. The mold temperature (M _T), packing time (P _t), packing pressure (P _P) and cooling time (C _t) in the packing stage are considered as machining parameters. The results of analysis of variance (ANOVA) and conducting confirmation experiments demonstrate that the quadratic models of the shrinkage and warpage are fairly well fitted with the experimental values. The individual influences of all machining parameters on the shrinkage and warpage have been analyzed and predicted by the obtained mathematical models. For the manufacture of PC/ABS cell phone shell, the values of shrinkage and warpage present the reduction of 37.8 and 53.9%, respectively, using this optimal procedure.     

A study on fiber laser micro-spot welding of thin stainless steel using response surface methodology and simulated annealing approach

This study analyzed variations of shear strength that depend on the fiber laser process during micro-spot welding of AISI 304 stainless thin sheets. A preliminary study used ANSYS results to obtain initial process conditions. The experimental plan was based on a Taguchi orthogonal array table. A hybrid method that includes the response surface methodology (RSM)- and back propagation neural network (BPNN)- integrated simulated annealing algorithm (SAA) is proposed to search for an optimal parameter setting of the micro-spot welding process. In addition, an analysis of variance was implemented to identify significant factors influencing the micro-spot welding process parameters, which was also used to compare the results of BPNN-integrated SAA with the RSM approach. The results show that the RSM and BPNN/SAA methods are both effective tools for the optimization of micro-spot welding process parameters. A confirmation experiment was also conducted in order to validate the optimal welding process parameter values.     

Investigation into electrochemical micromachining (EMM) through response surface methodology based approach

Electrochemical micromachining (EMM) could be used as one the best micromachining technique for machining electrically conducting, tough and difficult to machine material with appropriate machining parameters combination. This paper attempts to establish a comprehensive mathematical model for correlating the interactive and higher-order influences of various machining parameters, i.e. machining voltage pulse on/off ratio, machining voltage, electrolyte concentration, voltage frequency and tool vibration frequency on the predominant micromachining criteria, i.e. the material removal rate and the radial overcut through response surface methodology (RSM), utilizing relevant experimental data as obtained through experimentation. Validity and correctiveness of the developed mathematical models have also been tested through analysis of variance. Optimal combination of these predominant micromachining process parameters is obtained from these mathematical models for higher machining rate with acuuracy. Considering MRR and ROC simultaneously optimum values of predominant process parameters have been obtained as; pulse on/off ratio, 1.0, machining voltage, 3 V, electrolyte concentration, 15 g/l, voltage frequency of 42.118 Hz and tool vibration frequency as 300 Hz. The effects of various process parameters on the machining rate and radial overcut are also highlighted through different response surface graphs. Condition of machined micro-holes are also exhibited through the SEM micrographs in this paper. Pulse voltage pattern during electrochemical micromachining process has been analyzed with the help of voltage graphs. Irregularities in the nature of pulse voltage pattern during electrochemical micromachining have been observed and the causes of these irregularities are further investigated.