基于神经网络的激光切割加工工艺优化建模及工艺参数自动选取…

本文讨论了激光切割加工工艺特征及其相关工艺参数对加工质量的影响，介绍了以ＢＰ神经网络为基础的专家系统的特点，在此基础上，建立了激光切割加工工艺参数模型，它具有自学习，易扩展，易使用等优点，用户可以通过用优化后的工艺参数样本对此模型进行训练可以自动生成激光切割加工工艺数据选择专家系统。     

基于神经网络的激光切割加工工艺优化建模及工艺参数自动选取系统的研究

本文讨论了激光切割加工工艺特征及其相关工艺参数对加工质量的影响，介绍了以ＢＰ神经网络为基础的专家系统的特点，在此基础上建立了激光切割加工工艺参数模型，它具有自学习、易扩展、易使用等优点，用户可以通过用优化后的工艺参数样本对此模型进行训练可以自动生成激光切割加工工艺数据选择专家系统。     

激光切割不锈钢板工艺参数研究

基于激光切割工作原理,分析了影响不锈钢板激光切割质量的三个重要工艺参数,即:离焦量、辅助气体压力和切割速度,理论研究了其影响机理,并通过对不同厚度不锈钢板离焦量的确定进行大量工艺实验,总结出离焦量的确定方法,然后对不同厚度不锈钢板辅助气体压力进行实验,实验研究出其确定方法,最后对切割速度进行分析,提出切割速度理论确定方法。分析研究为激光切割应用提供了参考,对实际加工有一定的指导意义。     

遗传算法和人工神经网络在ITS中的应用

针对遗传算法(Genetic Algorithm,GA)和人工神经网络(Artificial Neural Network,ANN)的优缺点,提出了将遗传算法与人工神经网络有机结合起来的遗传-神经网络(Genetic Neural Network,GNN)优化计算模型,既利用了遗传算法能并行计算且能快速、全局搜索的优点,又克服了神经网络固有的搜索速度慢且易陷入局部早熟的缺点.结果表明遗传-神经网络算法能加快非线性模型的收敛速度,具有较强的鲁棒性,在ITS中有着广泛的应用前景.     

大功率激光切割机切割工艺参数分析

初步探讨了影响激光切割碳素结构钢板切割质量的因素,在其他参数不变的情况下,最小打孔功率和最小无"挂渣"切割功率随着板材厚度的增加而增大;切缝断面粗糙度随着切割气体压力和切割速度先减小后增大。     

激光精密切割技术在特种雕刻行业中的应用

激光加工是激光应用的主要领域,而激光切割又是激光加工中发展最为成熟、应用最为广泛的一种新技术。本文在简介激光加工材料的物理过程的基础上,分析了我国的激光精密切割技术在雕刻行业中的应用现状及应用前景。     

氮气在激光切割中的应用

通过和氧气切割的对比，介绍了氮气在激光切割中的优势和应用。     

激光切割参数数据库的研究

在分析激光切割工艺参数获取难点的基础上，介绍了激光切割工艺参数数据库的建立，并提出了基于模糊关系以及基于网络的激光切割工艺参数数据库，为实现激光加工的自动化提供了基础。     

激光在汽车工业板材切割中的应用

介绍了激光切割在车桥桥壳设计和加工中的应用，较好地解决了传统板材切割工艺中存在的加工和质量问题，同时还展示了其与传统工艺相比所具有的优势。     

钣金激光切割工艺优化方法及应用研究

激光切割因精度高且价格逐渐降低受到了企业的青睐,因此本文着重研究钣金激光切割工艺优化技术。     

基于遗传算法的柔性制造系统机床选择问题研究

由于激烈的市场竞争环境,灵活的制造商应该以最快的速度向市场推出产品,以最少的成本进行生产,从而拥有使消费者满意的巨大能力.而具有快速时间响应和高度柔性的制造系统是必要的.在假设的条件下,构建基于零件加工时间和成本加权和为目标的柔性制造系统机床选择数学模型,在模型中考虑机床的维修成本.用C语言实现遗传算法在柔性制造系统机床选择中的应用,并与以前的例子进行比较.最后通过实验对遗传算法的参数进行分析.     

Parameter optimization of non-vertical laser cutting

In some cases, in order to avoid interference during 3D laser cutting of thin metal a laser head could not be kept vertical to the surface of a work piece. In such situations, the cutting quality depends not only on “typical” cutting parameters but also on the slant angle of the laser head. Traditionally, many tests had to be done in order to obtain the best cutting results. In this paper, an experimental design is employed to reduce the number of tests and an artificial neural network (ANN) is set up to describe quantitatively the relationship between cutting quality and cutting parameters in the non-vertical laser cutting situation. A quality point system is used to evaluate the cutting result of the thin sheet quantitatively. Testing of this novel method shows that the calculated “quality point” using ANN is quite closely in accord with the actual cutting result. The ANN is very successful for optimizing parameters, predicting cutting results and deducing new cutting information.     

基于遗传算法的激光打孔路径优化

针对目前激光打孔过程中存在的问题,采用遗传算法对激光打孔路径进行优化.建立了路径优化目标函数模型,对激光打孔路径优化总体设计,遗传算法的实现所需要的适应度函数、选择算子、交叉算子、变异算子等遗传操作进行了说明,并通过实例说明,采用遗传算法对激光打孔路径进行优化,可以显著地提高激光打孔加工效率.     

采用遗传PID整定的电液伺服系统仿真研究

建立了电液伺服系统数学模型,提出了基于遗传算法的电液伺服系统PID参数整定方法,并利用Matlab编程软件进行仿真,仿真结果表明,基于遗传PID整定的电液伺服系统具有良好的稳定性和较强的鲁棒性,达到了预期的控制效果,表现出了优越的控制性能.     

基于人工神经网络进行带钢轧制过程轧辊偏心的识别

人工神经网络是一门新兴的学科。伴随着计算机技术的飞速发展 ,人工神经网络的应用前景越来越广阔 ,本文试图运用人工神经网络的基本理论与观点 ,结合轧制过程的控制数模 ,对带钢轧制过程中轧辊偏心识别进行研究 ,并建立一种新的数学模型 [1 ] 。     

磨料水射流切割质量的神经网络预测

磨料水射流切割质量影响因素较多,难以建立有效的理论模型,结合实验结果,建立磨料水射流切割质量的神经网络预测模型。结果表明,对于所给定切割参数,该模型能快速、准确、可靠地预测出切割质量。     

应用遗传算法优化支持向量机的疲劳裂纹扩展预测

准确讯速地预测疲劳裂纹的扩展进程具有十分重要的现实意义和显著的经济效益.为了实现疲劳裂纹长度的准确预测,提出基于遗传算法优化支持向量机(GA-SVM)的疲劳裂纹扩展预测方法,其中遗传算法用于确定SVM中的训练参数,得到优化的SVM预测模型.试验结果表明:用GA-SVM对疲劳裂纹长度进行预测具有很好的预测精度.     

An improved artificial neural network for laser welding parameter selection and prediction

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.     

Implementation of ANN-based Selective Harmonic Elimination PWM using Hybrid Genetic Algorithm-based optimization

A PWM technique with Selective Harmonic Elimination (SHE) is used to control fundamental harmonic and eliminate harmonics of chosen lower-order in voltage source inverters (VSI). Therefore, this PWM technique requires the determination of the optimum switching angles by solving the nonlinear equation set. The determined angles are recorded on a look-up table to generate PWM signals in real-time systems. The paper proposes two Artificial Neural Networks (ANN) based solution for determining angles and generating PWM signals. ANN generates optimum switching angels for all modulation index between 0 and 1.20 because of it has learning capability differently from the look-up table. Primarily, the optimum 11-switching angles for three-phase two-level inverter are determined by using offline Hybrid Genetic Algorithm (HGA). The first ANN was trained by the data obtained from HGA to calculate the switching angles without using a look-up table. Second ANN was trained by using these switching angles to generate PWM signals. The ANN-based SHEPWM was designed to obtain inverter output voltage which has a bipolar waveform with quarter-wave symmetry. The algorithm of ANN-based SHEPWM is performed by using TMS320F28335 Digital Signal Processor (DSP). The experimental results related to dc-link voltage, inverter output voltage and load current are measured for different M_a by using scope and power quality analyzer. The waveform of inverter output voltage are also analyzed with FFT for an induction motor load. The low-order harmonics are successfully eliminated by proposed ANN based SHEPWM.     

A multiobjective genetic algorithm for scheduling a flexible manufacturing system

Though the designers of Flexible Manufacturing Systems (FMS) strive to ensure the maximum flexibility in the system, in practice, after the implementation of such systems the operational executives often find it hard to accommodate frequent variations in the part designs of incoming jobs. This difficulty can very well be overcome by scheduling the variety of incoming parts into the system efficiently. In this work an appropriate scheduling mechanism is designed to generate a nearer-to-optimum schedule using Genetic Algorithm (GA) with two different GA Coding Schemes. Two contradictory objectives of the system were achieved simultaneously by the scheduling mechanism. The results are compared with those obtained by different scheduling rules and conclusions are presented.