基于熔融挤压快速成型技术3D打印实训设备的工艺参数优化

熔融挤压快速成型技术具有价格便宜、工艺简单、性能完备等的特点,目前高职院校常用的3D打印实训设备大多是基于此技术,但是也存在打印制品表面质量比较差等缺点。本文通过利用正交实验法对我院现有3D打印设备进行工艺参数优化,可以获得表面质量比较好的打印制品,从而提升实训的效果。     

7075铝合金杯形反挤压成形计算机仿真

基于DEFORM-3D仿真软件,分析了7075铝合金带筋杯形件反挤压成形过程,研究了挤压速度、扭转速度、坯料初始温度对成形过程的影响,通过正交试验获得最佳成形工艺参数,并将传统的挤压成形与扭转挤压成形进行对比。结果表明,在传统反挤压的基础上,扭转作用使得坯料成形所需载荷降低,金属内部等效应变分布更均匀,通过正交试验最终得到了优化后的成形工艺参数,为铝合金杯形件反挤压成形提供理论基础。     

大宽厚比薄壁异型材挤压多工艺参数优化研究

型材挤压多种工艺参数的优化设计是一个组合优化问题,难以用传统数学优化方法解决。在应用型材挤压CAD/CAE技术建立型材挤压CAD模型,并对其成形过程及其参数变化规律进行CAE仿真的基础上,采用基于正交试验、人工神经网络和遗传算法的型材挤压多工艺参数计算机辅助优化技术建立型材挤压多工艺参数与挤压质量间的关系映射模型,并预测不同参数值搭配对挤压质量的影响,进而确定多工艺参数最优解。试验证明取得了良好的效果。     

基于响应面模型的铝合金壁板挤压成形优化设计

针对铝合金壁板挤压成形中出现的挤压能耗过大、制品缺陷问题,对6063铝合金壁板挤压过程进行多目标优化设计。结合正交试验设计和响应面设计方法,建立挤压成形参数与评价指标的二阶响应面模型。运用遗传算法和模拟退火算法,实施最优个体保留策略,开发出遗传模拟退火程序,对响应面模型进行寻优,获得24组Pareto最优解。通过定义满意度函数,选出了符合要求的满意解。为验证优化结果的真实性,对满意解进行仿真验证,仿真结果与优化结果相吻合。     

汽车引擎盖外板拉延成形工艺参数优化研究

针对汽车引擎盖外板拉延成形容易产生起皱、破裂、拉延不充分等缺陷的问题,对影响引擎盖外板成形质量的工艺参数进行了研究。运用单因素变量法,研究了压边力、凸凹模间隙、摩擦因数、拉延筋高度、冲压速度对引擎盖外板拉延成形的影响;提出了一种基于正交试验法和极差分析法,以最大减薄率为优化目标,获得了最优工艺参数组合,确定了各因素对引擎盖外板最大减薄率影响主次顺序的优化方法,并对试验结果进行了方差分析,得出了影响最大减薄率的显著因素;应用优化后的工艺参数进行了模拟仿真,获得了良好的拉延成形效果。研究结果表明:使用最优工艺参数组合进行试模,获得的引擎盖外板最大减薄率为19.585%,最大增厚率为1.047%,成形质量较好;应用基于正交试验法的数值模拟技术能够提高引擎盖外板成形质量,减少试模次数、缩短生产周期、降低生产成本。     

轻武器高强度合金钢零件温挤压成形研究与应用

针对某型步枪零件30CrMnMoTiA高强度合金钢材料的毛坯进行温挤压成形的工艺应用研究,采用数值模拟分析对工艺参数进行优化,并经过试验验证,成功地获得了良好的温挤压毛坯件,实现了温挤压近净成形技术在轻武器复杂零件毛坯精化中的应用。     

切削-挤压翅片管成形过程的研究

采用无屑切削-挤压成形技术加工翅片管。对翅片的成形过程和加工刀具的特点进行了研究,建立了翅片尺寸的理论模型;通过单因素试验,获得了主偏角、切削深度、进给量、切削速度等工艺参数对翅片尺寸的影响规律;以正交试验为基础,获得了翅片高度和厚度的经验公式。理论和试验研究表明,切削用量参数和刀具几何参数的精确匹配是翅片成形的关键。     

基于人工神经网络的板料弯曲成形工艺优化及回弹分析

结合人工神经网络所表现出来的良好特性,利用正交试验获得的数据作为神经网络的训练样本,建立输入为弯曲工艺参数、输出为回弹量的神经网络模型,并通过样本检验了ANN模型的准确性,从而缩短设定工艺参数的时间,在工艺参数取值范围内,采用ANN模型代替CAE软件模拟试验,结合正交试验法,对工艺参数进一步优化.结果表明:将神经网络与正交试验、数值模拟三者结合用于板料弯曲成形参数优化,可以缩短优化工艺参数的时间.提高工艺设计效率,并能获得比单纯使用正交试验和数值模拟方法更为优化的结果.     

熔融挤压堆积成形质量分析

通过理论分析与工艺试验相结合，探索几种聚合物材料在不同工艺条件下的粘结质量问题，总结出不同材料在熔融挤压堆积成形工艺中的最佳喷嘴温度范围、成形室温度范围及合理层厚。这里结论对于选用成形材料和优化工艺参数提供了重要依据。     

型材挤压过程工艺参数优化模型

提出了一种集数值仿真、人工神经网络和遗传算法为一体的工艺参数优化模型,用于型材挤压成形过程工艺参数优化,合理配置了非对称角铝型材模模孔位置。通过现场试验,验证了提出的工艺参数优化模型是行之有效和正确的。并在模孔位置优化配置结果基础上对其挤压成形过程进行数值仿真,分析了挤压成形过程中各阶段网格畸变情况,给出了挤压变形时应力和应变分布,对指导型材挤压工艺和模具优化设计具有重要意义。     

基于回归遗传算法的高速铣削工艺参数优化

针对高速铣削加工中心效率低、经济性不高的特点,利用正交试验建立了表面粗糙度和铣削力的预测模型,并利用遗传算法优化高速铣削工艺参数,通过验证试验表明,优化后的工艺参数在实际生产中,加工效率、加工质量都有了很大提高.     

铝合金型材等温挤压参数优化及数据管理

先介绍铝合金型材等温挤压工艺参数的确定方法,即先通过模具尺寸优化,实现型材挤出流度相等,再通过工艺参数优化,实现型材挤出温度相等;将得出的数据及曲线录入挤压工艺数据库管理系统以提高挤压生产线的自动化水平,进而提高产品质量。     

基于正交试验的Al2O3陶瓷激光铣削试验

Al2O3陶瓷激光铣削工艺受多种因素影响。基于正交试验方法优化陶瓷激光铣削参数组合,采用极差分析得到了各因素对铣削深度的影响程度大小,并以此为基础优化工艺参数,进行Al2O3陶瓷圆形型腔的激光铣削试验。结果表明:各因素对激光铣削深度的影响大小顺序依次为激光功率、扫描速度、搭接量、离焦量。采用优化后的工艺参数进行加工,单层铣削深度可达到0.4mm,铣削效果令人满意;未吹除的表面熔渣大大增加了加工件表面粗糙度,应增加辅吹气体气压来改善加工件表面质量。     

涂层刀具高速铣削模具钢SKD11的表面粗糙度模型预测

为有效控制和预测高硬度模具钢加工的表面质量和加工效率,通过设计正交切削试验,研究了在不同切削参数组合(主轴转速、进给速度、轴向切削深度和径向切削深度)及冷却润滑方式条件下、Ti Si N涂层刀具对模具钢SKD11(62HRC)的高速铣削。应用BP神经网络原理建立表面粗糙度预测模型,并进行试验验证其准确性。研究表明,在不同加工条件下,基于BP神经网络模型建立的涂层刀具铣削模具钢SKD11表面粗糙度模型有较好的预测精度,其预测误差在3.45%-6.25%之间,对于模具制造企业选择加工工艺参数、控制加工质量和降低加工成本有重要意义。     

Optimization of abrasive flow polishing process parameters for static blade ring based on response surface methodology

Static blade ring process technology is a key part for gas turbine manufacturing, and the surface quality of the static blade ring has great influence on a gas turbine. To improve surface roughness of static blade ring, abrasive flow polishing process technology is studied. First, the range of extrusion pressure is obtained by using ANSYS software to analyze the blade deformation. Then a simplified model of surface roughness is estabalished according to experimental results and ANOVA’s results. At the same time, the optimal polishing parameters are confirmed through the response surface methodology. Finally, the polishing experiment is carried out by using the optimal polishing parameters. The experimental results demonstrate that the surface roughness of static blade ring decreased greatly (nearly 14.7%) compared with result using normal parameters.     

多目标质量的粉末包套等通道挤扭成形致密工艺参数优化

剧烈塑性变形法—挤扭以剪切塑变为主变形方式,成形工艺复杂,影响因素很多,难以精确地建立工艺参数与成形质量之间的关系;选取优化合理的工艺参数匹配是材料尤其是粉末材料成形无破裂、塑性好、致密化程度高等成形质量的关键。以等通道横截面扭转角、螺旋角、摩擦因子、挤压速度、初始相对密度为自变量,正交试验为设计方案,对纯铝粉末烧结材料进行一道次包套等通道扭挤数值模拟,获得以等效塑性应变、静水压力、最大损伤值、相对密度数据,通过层次分析法计算多质量目标的权重,运用追踪点法和灰色系统理论的灰色关联度优化工艺参数,使设计目标值达到等效应变最大、静水压力最大、最大损伤值最小、相对密度最大。模拟和试验结果表明,运用多目标优化组合参数进行等通道挤扭成形能使纯铝粉末体材料迅速地形变累积,静水压力提高,损伤值显著地减小,致密效率高,晶粒明显细化,提高了材料综合质量。     

金属熔覆件侧面激光铣削整形工艺的研究

为提高激光熔覆件的成形质量,采用激光铣削的处理方式来保证熔覆件侧面的光滑平整。为此,对熔覆件侧面铣削进行整形工艺试验研究,选择点间距、激光功率、脉宽和离焦量作为变量,通过正交试验来探究各工艺参数的权重并进行参数优化。用优化后的参数对熔覆件侧面进行铣削加工,得到铣削宽度为0.075mm,面粗糙度为2.319μm。相比原熔覆件,侧面成形质量得到明显改善,证明用激光铣削的方式提高熔覆件成形时侧面的光滑性和平整度是可行的。     

Research on rotary surface topography by orthogonal turn-milling

As a new technology in manufacturing, turn-milling broadens the application ranges of mechanical processing, wherein both cutting tool and workpiece are given a rotary motion simultaneously. The objective of the present work is to study workpiece surface topography during orthogonal turn-milling process. This study begins with two mathematical models, which describe theoretical surface roughness and topography of rotationally symmetrical workpiece. The models are built with the establishment of locus function according to orthogonal turn-milling principle. Then based on these models, the influence law of surface topography affected by various cutting parameters is found by some simulation methods. The law also matches with orthogonal turn-milling surface roughness and topography experiments. By analyzing the experimental results, some parameter selection criteria during orthogonal turn-milling processing are also proposed qualitatively and quantitatively. The comparison between the simulation and experimental results shows that a better surface quality and tiny oil storage structure can be obtained if the cutting parameters are chosen in reason. This conclusion provides a theoretical foundation and reference for the orthogonal turn-milling mechanism research.     

An intelligent system for low-pressure die-cast process parameters optimization

Low-pressure die-cast (LPDC) is widely used in manufacturing thin-walled aluminum alloy products. Since the quality of LPDC parts are mostly influenced by process conditions, how to determine the optimum process conditions becomes the key to improve the part quality. In this paper, a combining artificial neural network and genetic algorithm (ANN/GA) method is proposed to optimize the LPDC process. In this method, considering the more complicated preparation process of thin-walled casting, an ANN model combining learning vector quantization and back-propagation (BP) algorithm is proposed to map the complex relationship between process conditions and quality indexes of LPDC. Meanwhile, the orthogonal array design and numerical simulation is applied to obtain the training samples instead of carrying out a real experiment for the sake of cost saving. The genetic algorithm is employed to optimize the process parameters with the fitness function based on the trained ANN model. Then, by applying the optimized parameters, a thin-walled component of 300 mm in length, 100 mm in width, and 1.5 mm in thickness is successfully prepared. The results indicate that the proposed intelligent system is an effective tool for the process optimization of LPDC.     

An experimental investigation on machining parameters of AISI D2 steel using WEDM

The performance of the wire electrodischarge machining (WEDM) machining process largely depends upon the selection of the appropriate machining variables. Optimization is one of the techniques used in manufacturing sectors to arrive for the best manufacturing conditions, which are essential for industries toward manufacturing of quality products at lowest cost. As there are many process variables involved in the WEDM machining process, it is difficult to choose a proper combination of these process variables in order to maximize material removal rate and to minimize tool wear and surface roughness. The objective of the this work is to investigate the effects of process variables like pulse on time, pulse off time, peak current, servo voltage, and wire feed on material removal rate (MRR), surface roughness (SR), gap voltage, gap current, and cutting rate in the WEDM machining process. The experiment has been done using Taguchi’s orthogonal array L27 (3⁵). Each experiment was conducted under different conditions of input parameters and statistically evaluated the experimental data by analysis of variance (ANOVA) using MINITAB and Design Expert tools. The present work also aims to develop mathematical models for correlating the inter-relationships of various WEDM machining parameters and performance parameters of machining on AISI D2 steel material using response surface methodology (RSM).The significant machining parameters and the optimal combination levels of machining parameters associated with performance parameters were also drawn. The observed optimal process parameter settings based on composite desirability (61.4 %) are pulse on time 112.66 μs, pulse off time 45 μs, spark gap voltage 46.95 V, wire feed 2 mm/min, peak current of 99.99 A for achieving maximum MRR, gap current, gap voltage, cutting rate, and minimum SR; finally, the results were experimentally verified.