电子束焊熔深神经网络模糊控制系统建模与仿真

研究钛合金电子束焊熔深控制系统建模问题。在分析电子束焊接特点的基础上,设计三因素五水平正交试验,通过试验得到不同焊接参数下熔宽和熔深的值,将熔宽和熔深的值作为训练样本对神经网络进行训练,建立以熔宽为输入,以熔深为输出的误差反向传播(Error back propagation,BP)神经网络模型,该模型由一个S型函数隐含层加上一个线性输出层组成。针对熔深数学模型难以获得的情况,设计以熔深的偏差和偏差变化率为输入变量,焊接电流的变化量为输出变量的模糊控制器,该控制器有9条模糊控制规则。将BP神经网络模型和模糊控制器结合起来建立钛合金电子束焊熔深控制系统模型,并且采用单位阶跃信号对该模型进行仿真试验,试验结果表明所设计的控制系统动态性能和稳态性能良好。     

脉冲熔化极气体保护焊焊接电源—电弧系统建模与仿真

因脉冲熔化极气体保护焊(Pulse gas metal arc welding,GMAW-P)焊接电弧负载非线性与时变性的特点,GMAW-P焊接电源-电弧系统的研究、设计及其控制参数的选定有其固有的难点。采用基于Matlab/Simulink扩展工具S函数模块设计技术,建立GMAW-P焊接电源-电弧系统动态仿真模型。基于所建立的仿真模型,在完成对控制器参数优化设计的同时,实现对GMAW-P电源-电弧系统的抗弧长干扰仿真研究。试验证明,仿真计算结果和试验结果具有较好的一致性,验证了所建仿真模型的正确性、控制系统调节器优化参数的适应性及电源控制系统的动态性能,为GMAW-P焊接电源-电弧系统仿真建模研究及控制器参数的优化设计提供了新的途径。     

脉冲熔化极气体保护焊的模糊控制

为实现GMAW-P焊过渡过程的稳定,设计了GMAW-P焊新的焊接电流波形,并在此基础上建立了以电弧电压为被控制量、基值时间和送丝速度为控制量的自调整因子的模糊控制系统。试验结果表明该控制系统响应速度快,控制精度高,可实现对电弧电压的稳定控制。     

基于二分法指数衰减的液位控制

受限于独特的工况,现有的液位控制模式并不总是最佳的选择。针对单输入单输出的连续单调非线性液位控制,采用二分法原理对输入进行寻值,推算出寻值的误差曲线随检索次数的增加呈指数衰减。依据此原理设计了一套液位控制系统,0~5V模拟信号的输入控制V/F变频模式调节泵的转速,实现液位的升降;3个简单的开关信号对输出的到位情况进行判定。结果表明:二分法的液位控制策略可迅速的在输入范围内检索出近似的理想输入值;该控制系统结构简单、可靠,应用范围广。     

脉冲MIG焊建模仿真分析及弧长控制

针对脉冲MIG焊过程中的电弧稳定性问题,建立基于尖端不稳定熔滴过渡理论的焊丝熔化动态电弧模型,并对焊接过程中的电弧电压、电弧长度及熔滴过渡尺寸进行仿真,得到与实际焊接相似的电弧电压波形,分析脉冲电流下熔滴过渡频率及影响电弧稳定性的因素,且进行弧长稳定控制的仿真研究,在此基础上运用快速原型技术建立铝合金脉冲MIG焊控制系统,采用电弧电压反馈对铝合金脉冲MIG焊过程进行送丝调节的弧长控制试验。研究表明:所建立的铝合金脉冲MIG焊焊丝熔化动态电弧模型很好反映了实际焊接过程,揭示出脉冲MIG焊焊接过程中熔滴过渡时间间隔具有一定的不确定性和电弧长度的不稳定性,通过电弧电压反馈控制可显著改善铝合金脉冲MIG焊过程电弧系统的稳定性。     

线性摩擦焊机振动系统幅相控制策略研究

为保证线性摩擦焊接过程能量输入的稳定性，提高线性摩擦焊机振动系统幅值与相位的控制精度，建立了振动系统目标信号、输入信号与输出信号之间的关系，提出幅相控制策略，设计了幅相控制算法控制器，并采用Simulink建立了系统控制模型，通过模拟仿真，得到了不同焊接参数下的算法输入参数。通过典型参数下的焊接试验，验证了幅相控制的可行性。根据焊接过程参数分析了不同控制方法的控制效果，结果表明采用所设计的幅相控制策略，焊接过程中振动系统的振幅误差为1.2%，相位误差为0.16°。     

焊接过程的模糊控制

从焊接过程的控制实测出发,简明扼要地引入模糊控制的一些最基本的概念,如模糊集合图与从属函数、模糊语言和逻辑推理过程、模糊控制器的结构原理及实用的算法。详细介绍了作者应用模糊控制于焊接过程的一些研究工作的结果。MIG焊熔透的模糊控制部分叙述了一个典型的单输入单输出模糊控制系统、焊缝轨迹自动跟踪的模糊控制部分讨论了如何确定系统参数与改善系统控制性能的方法、CO 2焊接规范参数的模糊控制部分则是一个多输入多输出模糊控制系统应用举例。     

对角CARIMA模型输入输出约束自适应广义预测控制

为了简化输入输出约束存在时多变量广义预测控制算法的设计与实现问题,提出了对角结构形式的多变量CARIMA模型的约束广义预测控制算法.根据对角CARIMA模型的结构特点将多输人多输出对象的模型输出预报和参数辨识转化为多输入单输出子对象的模型输出预报和参数辨识,并详细分析了系统的输入输出约束形式,将模型输出预报、目标函数和约束条件中依赖于模型参数的项与依赖于过程历史数据的项完成分离,简化了模型输出预报的计算及优化问题的求解,从而简化了约束多变量广义预测控制算法的实现.在一个DCS控制的非线性液位装置上的对比实验结果表明了该方法的有效性.     

基于模糊预测控制的燃料电池温度控制系统的研究

PEMFC的温度控制系统表现为多输入多输出、耦合、时滞、时变性、非线性、不确定性、信息量少等特点,采用传统的控制方法通常不能得到比较好的控制效果。因此,文章从控制和建模的角度出发,选择了一种基于模糊模型的预测控制算法对PEMFC的温度进行控制,并且通过仿真试验得出了比较满意的控制效果。     

压电定位平台Hammerstein建模与反馈线性化控制

压电定位平台以压电陶瓷、柔性铰链作为驱动及放大机构,具有高定位精度和快响应速度,被广泛应用于各种精密/超精密定位领域。压电定位平台面临的主要挑战是压电陶瓷的固有迟滞非线性特性,这严重影响平台的定位和跟踪精度。针对此问题,提出一种基于Hammerstein结构的迟滞建模方法及基于此模型的输入-输出反馈线性化控制策略。首先,建立Hammerstein结构的迟滞模型,并进行模型参数估计。接着,以基于Hammerstein模型的输入-输出反馈线性化控制策略设计跟踪控制器。最后,在压电定位平台上对建立的模型和设计的跟踪控制器进行实验验证。模型辨识实验结果表明：提出的Hammerstein模型能有效地拟合压电定位平台输入量与输出量之间的迟滞非线性特性,其均方根误差小于0.5μm。轨迹跟踪实验结果表明：设计的跟踪控制器对期望信号（幅值60μm,频率100 Hz）的跟踪均方根误差为0.926 6μm,相较于基于改进的速率相关PI(Modified Rate-dependent Prandtl-Ishlinskii,MRPI)模型的前馈补偿跟踪控制、基于MRPI模型的前馈补偿与PID反馈复合跟踪控制,精...     

Nano-scale multi-layered coatings for improved efficiency of ceramic cutting tools

Electron beam welding, though considered a sophisticated welding process, still requires the operator to first carry out several trial welds to find the right combination of welding parameters based on intuition and experience. This archaic method is often unreliable, leading to unproductive manufacturing lead time, man hours, quality control tests, and material wastage. The current study eliminates this “trial and error” method by providing a reliable model which can predict the right combination of weld parameters to achieve a high-quality weld. Beads on plate welds were carried out on AISI 304 stainless steel plates using a low-kilovolt electron beam welding (EBW) machine. A model that can predict weld bead geometry and provide optimized output for minimum weld area condition without compromising on weld quality was developed. Experimental data were collected as per full factorial design of experiments, and the levels for each input parameter were established through pilot experiments. A multivariate regression analysis has been conducted to establish a relationship between four weld input parameters (three levels each) and four weld bead responses. Response surface methodology (RSM) has been used to study the interrelationship between input parameters and their effect on each response variable. Further, minimization of weld cross-sectional area was done using genetic algorithm for maximum penetration and minimum weld area condition. The optimized mathematical model convincingly establishes that the focusing current is a significant input parameter with very high influence over the weld bead geometry. Extensive material characterization and mechanical tests have been carried out to validate the regressed input-output relationship and the optimized mathematical model.     

波控短路过渡CO_2焊接恒频自适应控制系统

针对短路过渡ＣＯ２焊接工艺的特殊性,基于熔滴与熔池短路阶　段电源输出回路电阻的变化特征,确定了有利于消除瞬时短路及提高熔滴过渡规则性的焊接　电源输出电流波形。在对短路过渡频率及焊丝伸出长度进行实时检测的基础上,采用ＭＣ６８ＨＣ　１１Ａ１单片机为主控ＣＰＵ,　建立了焊丝伸出长度变化前馈补偿—短路过渡频率负反馈控制的波控短路过渡ＣＯ２焊接恒频自适应控制系统,以提高焊接工艺过程的稳定性、抑制焊丝伸出长度变化对焊接工艺过程的扰动。     

脉冲GMAW焊接极性对高压环境下焊缝成形的影响

高压环境会造成焊接电弧收缩,焊接过程较不稳定。鉴于脉冲熔化极气体保护焊(Pulsed gas metal arc welding,GMAW-P)具有热输入量可控、电弧挺度较好等特点,将其应用于高压环境下是一个有益尝试。开展压力在0.1~1.0 MPa范围内不同极性下的干法GMAW-P试验,分析随着环境压力升高电流电压波形图的变化趋势,由此确定焊接极性不同时焊接过程稳定性随环境压力升高的变化规律;在此基础上,对比分析不同极性下GMAW-P焊接飞溅及焊缝成形随压力升高的变化规律。随着环境压力增加,在直流反接情况下,电流电压波形周期性逐渐减弱,焊接过程趋于不稳定,飞溅逐渐增多,焊缝成形变差;而采用直流正接方式时,高压下电流电压波形的周期性虽然不及常压下稳定,但相较于同一压力下的直流反接方式,周期规律性有所增强,焊接过程相对稳定,且焊接时产生的飞溅比采用直流反接方式时的飞溅少且颗粒小。另外,随环境压力增加,两种极性GMAW-P焊缝宽度均有变窄的趋势,余高也随之增加。对高压环境下不同焊接极性GMAW-P的电弧形态及相应电弧力进行深入探讨,明确了上述现象出现的原因,为高压环境下焊接工艺的改进提供有效的理论依据。     

Integrated design and process analysis of a blow molding turbo-charged pipe

These days, the trend in every manufacturing industry including welding is to automate the processes in order to increase productivity. To achieve this objective, it is therefore necessary to make use of models to relate the input parameters with the responses. This paper reports on the applicability of fuzzy logic to predict the weld bead penetration in submerged arc welding process as affected by input welding parameters. Fuzzy logic is a computer technique which allows expressing, evaluating, and simplifying complexities in regard to the relationships in a process by describing the dependencies between output and input parameters in a linguistic form. To develop the fuzzy logic model, the arc voltage, welding current, welding speed, electrode stick-out, and thickness of TiO₂ nanoparticles were taken as the input parameters and the weld bead penetration as the response. In order to generate experimental data, a five-level five-factor rotatable central composite design of experiments was employed. Experiments were performed, and the weld bead penetrations were measured. The predicted results using fuzzy logic were compared with the experimental ones. The correlation coefficient value obtained was 99.99 % between the measured and predicted values of weld bead penetration. The results show that the fuzzy logic is an accurate and reliable technique used in predicting the weld bead penetration due to its low error rate.     

基于人工神经网络的激光点焊焊点形态预测

建立适用于激光点焊焊点形态预测的人工神经网络模型,以点焊过程中的三个主要工艺参数(激光功率、点焊时间和离焦量)作为模型输入,输出为焊点表面、熔合面、背面直径以及熔深和横截面面积五个焊点形态参数。在此基础上,建立焊点形态模型,模型输入为神经网络的预测结果,输出为焊点形态。所建立的神经网络预测模型和焊点形态模型结合之后,可以实现激光点焊焊点的形态预测。网络测试结果显示实际值与网络预测值之间的RMS误差为0.1左右,模型输出的预测焊点形态与实际焊点形态之间较为吻合。根据模型的仿真结果,进一步研究点焊参数对焊点尺寸和形态的影响规律。结果表明未熔透焊点形态为Y形,而熔透焊点则存在多种形态,形态之间的转变主要受激光功率的影响。     

Nonlinear Model-Based Predictive Control Using a Generalised Hammerstein Model and its Application to a Semi-Batch Reactor

In recent years, much attention has been focused upon predictive control of nonlinear systems. The implementation of such a control strategy for real processes has greatly improved their performance. This paper deals with a model-based predictivecontrol (MBPC) strategy using a generalised Hammerstein model and its application to the temperature control of a semibatch reactor. Both unconstrained and constrained adaptive control problems are considered. A simple identification method based on the weighted recursive least squares method (WRLS) is used to estimate the model parameters on-line. An indirect adaptive nonlinear controller is designed by combining the predictive controller with an indirect parameter estimation algorithm. This adaptive scheme has been applied for the control of a semi-batch chemical reactor. Experimental results show that the performance of the generalised Hammerstein MBPC (NLMBPC) was significantly better than that of a linear model predictive controller (LMBPC). ID="A1"Correspondance and offprint requests to: Dr F. M'sahli, Department of Electrical Engineering, ISETKH, Avenue Hadj Ali Soua, 5070, Ksar Hellal, Monastir, Tunisia. E-mail: msahli-fn@iyahoo.fr     

ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL

0 INTRODUCTION(The satisfied control of the overall weld process is not easily accomplished, largely due to the inadequacies of the available process models. Without exceptions, most welding control methods are based upon the analytical welding models. Although these models are derived directly from the physical laws that govern the main features of the weld pool, a number of assumptions are made to obtain the mathematical solutions and some variables are ignored due to the complexity of t…     

Modeling of multiple characteristics of an arc weld joint

Process parameters modeling have always been one of the key aspects in development of an adaptive control of arc welding process. The welding process parameters are inherently nonlinear, time-delayed, and interdependent, and their on-time adjustment highly influences a sound weld bead formation and process monitoring. During the welding process, parameters control is the primary goal to leads a quality welding. Moreover, the final weld joint behavior, i.e., residual stress, welding strength, and micro-crack formation are generally observed after cooling of the weld product. Thus, it has always been a difficult task to control mechanical properties of a final weld joint. To obtain the best mechanical properties, the final weld joint characteristics needed to be controlled and predicted during the process itself by precise adjustment of the process parameters. The paper presents a neuro-fuzzy modeling approach to provide adaptive control for the automatic process parameter adjustment. Three input parameters wire feed speed, welding gap, and torch speed are modeled with welding current output, providing control over weld bead formation during the welding. The same input process parameters are also modeled to predict final weld joint characteristics, i.e., dilution ratio, hardness of weld bead, hardness of fused zone, and bead width. In order to ascertain the effectiveness of the neuro-fuzzy modeling approach, multiple regression models were also developed to compare the performances.