共查询到20条相似文献,搜索用时 156 毫秒
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基于改进粒子群算法和小波神经网络的高强钢扭曲回弹工艺参数优化* 总被引:2,自引:0,他引:2
针对高强钢复杂件冲压后出现的扭曲回弹现象,运用有限元仿真软件DYNAFORM对复杂件的冲压、回弹过程进行数值模拟,提出了评价复杂件扭曲回弹程度的指标,并运用试验设计和小波神经网络代理模型方法对扭曲回弹进行了优化研究。以某弯曲梁为研究对象,以扭曲回弹为成形目标,通过正交试验设计筛选出对扭曲回弹影响较大的工艺参数作为影响因素。利用拉丁超立方对影响因素进行抽样,通过数值模拟获得样本数据,建立影响因素与成形目标之间的小波神经网络代理模型,利用改进的粒子群算法对该模型迭代寻优获得最优参数。结果表明:采用优化后的工艺参数能有效地减小该弯曲梁的扭曲回弹,该方法为减小复杂件的扭曲回弹提供一种有益的指导。 相似文献
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鼠标外壳注塑件翘曲变形模拟分析 总被引:1,自引:0,他引:1
基于CAE数值分析技术和田口实验方法,研究了保压压力、熔体温度、冷却时间、保压时间和模具温度等因素对翘曲变形的影响.以翘曲变形量最小为目标,通过正交试验方法获得了最佳参数组合以及最佳参数组合条件下的翘曲变形量.结果显示,通过模流分析来预报产品缺陷,优化工艺参数,可以缩短模具开发周期,降低成本,提高企业竞争力. 相似文献
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针对汽轮机转子轮槽铣削加工过程中工艺参数难以确定的问题,利用有限元软件DEFORM-3D建立铣削仿真模型,模拟分析转子轮槽铣削过程中的切削力,并结合正交试验的方法,设计不同切削用量不同水平的正交试验表,研究铣削速度、进给量、背吃刀量对切削力的影响规律。以铣削过程中的切削力的大小为优化目标,通过极差及方差分析获得最优参数组合,并将最优参数组合切削力仿真值与理论值进行对比分析,以验证有限元数值模拟应用于轮槽铣刀铣削过程的可行性,对汽轮机转子轮槽铣削加工过程参数的优化具有一定的参考价值。 相似文献
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以Moldflow软件模拟得到的不同工艺参数下飞机机头雷达罩模型的翘曲变形量为训练样本,在雷达罩模型成型工艺参数与其翘曲变形量间建立反向传播(Back Propagation,BP)神经网络模型,然后采用遗传算法对工艺参数进行优化,得到使雷达罩模型翘曲变形量最小的工艺参数并进行试验验证.结果表明:在相同工艺参数下由BP神经网络得到的雷达罩模型翘曲变形量与采用Moldflow软件模拟得到的翘曲变形量相近,相对误差小于4%,证明了BP神经网络的可靠性;模拟得到雷达罩模型的最优成型工艺参数为注塑温度295℃、模具温度80℃、注塑时间0.75 s、保压时间8 s、保压压力125 MPa,此时翘曲变形量最小,为0.1213 mm;在最优成型工艺参数下进行注塑成型后得到的雷达罩模型最大翘曲变形量为0.1260 mm,试验结果与预测结果间的相对误差小于3.7%,验证了BP神经网络与遗传算法相结合方法的准确性. 相似文献
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为减少高强钢冲压成形扭曲回弹,提出一种基于渐变凹模圆角半径的模具补偿方法。以高强钢TRIP780双C件为研究对象,采用板料冲压成形仿真软件DYNAFORM对该双C件的冲压、扭曲回弹过程进行数值模拟。提出一种评价双C件扭曲回弹程度的指标,并进行双C件扭曲回弹试验,通过三坐标测量仪测量扭曲回弹角,对有限元模型进行了验证。以冲压成形后的扭曲回弹为优化目标,结合相关的工艺参数,利用BP神经网络,基于正交试验,建立凹模圆角半径渐变量、工艺参数与扭曲回弹角之间的网络模型。最后采用遗传算法对该模型迭代寻优获得最优凹模圆角半径渐变量和工艺参数。通过对比优化前后的扭曲回弹角,证明了优化流程有效地减少了双C件扭曲回弹。该方法为扭曲回弹的控制提供了一种新的思路。 相似文献
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Zhong Yuguang Xue Kai Shi Dongyan 《The International Journal of Advanced Manufacturing Technology》2013,68(1-4):755-762
In the laser welding production, the selection and prediction of welding parameters is essentially important to guarantee weld quality. Artificial neural networks (ANN), which perform a nonlinear mapping between inputs and outputs, are an alternative approach for developing welding parameter forecasting model. In this paper, in order to speed up the convergence and avoid local minimum of the conditional ANN, genetic algorithm simulated annealing (GASA) based on the random global optimization is inducted into the network training. By means of GASA method, weights and threshold of neural networks can be globally optimized with short training time. Meanwhile, the gray correlation model (GCM) is used as a pre-processing tool to simplify the original networks based on obtaining the main influence elements of network inputs. The GCM–GASA–ANN method combines the complementary features of three computational intelligence techniques and owns very good applicability. Through the simulation and analysis of an orthogonal experiment, the proposed method can be proved to have higher accuracy and to perform better than the traditional ANN to forecast the laser welding parameters. 相似文献
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点焊熔核尺寸与工艺参数关系的模型化处理 总被引:7,自引:0,他引:7
针对生产中常用的 1Cr18Ni9Ti板材 ,采用正交试验设计法研究了其交流点焊接头的熔核尺寸 (熔核直径、焊透率 )受主要焊接工艺参数影响的规律性。对实验数据进行了多元线性回归 ,建立了熔核尺寸与焊接电流、焊接时间、电极压力、电极端面尺寸、工件厚度之间关系的数学模型 ,并采用该模型对熔核尺寸进行了预测 相似文献
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铣削加工粗糙度的智能预测方法 总被引:1,自引:0,他引:1
吴德会 《计算机集成制造系统》2007,13(6):1137-1141
提出了一种基于最小二乘支持向量机的铣削加工表面粗糙度智能预测方法.首先进行了铣削工艺参数对工件表面粗糙度影响的正交实验,再通过对主轴转速、进给速率和切削深度三因素,以及各因素之间交互三水平实验的数据分析,找出了铣削工艺参数对工件表面粗糙度影响的一些规律.利用最小二乘支持向量机算法建立了铣削预测模型,通过该模型能在有限实验基础上利用工艺参数方便地得到粗糙度预测值.实际预测表明,在相同情况下,该模型构造速度比反向传播神经网络建模预测方法高2个~3个数量级,预测精度高10倍左右. 相似文献
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给出了正交车铣工件理论粗糙度的计算模型 ,分析了主要工艺参数对工件理论粗糙度的影响 ,并通过实验对工件的表面粗糙度进行了分析 ,证明了用正交车铣完全可实现零件的精加工。 相似文献
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Liqiang Zhang Rongji Wang 《The International Journal of Advanced Manufacturing Technology》2013,65(1-4):517-524
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. 相似文献
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X. Hyacinth Suganthi U. Natarajan S. Sathiyamurthy K. Chidambaram 《The International Journal of Advanced Manufacturing Technology》2013,68(1-4):339-347
In the present trend of technological development, micro-machining is gaining popularity in the miniaturization of industrial products. In this work, a hybrid process of micro-wire electrical discharge grinding and micro-electrical discharge machining (EDM) is used in order to minimize inaccuracies due to clamping and damage during transfer of electrodes. The adaptive neuro-fuzzy inference system (ANFIS) and back propagation (BP)-based artificial neural network (ANN) models have been developed for the prediction of multiple quality responses in micro-EDM operations. Feed rate, capacitance, gap voltage, and threshold values were taken as the input parameters and metal removal rate, surface roughness and tool wear ratio as the output parameters. The results obtained from the ANFIS and the BP-based ANN models were compared with observed values. It is found that the predicted values of the responses are in good agreement with the experimental values and it is also observed that the ANFIS model outperforms BP-based ANN. 相似文献