【作者】 吕凤琳；

【导师】 陈善本；

【作者基本信息】 上海交通大学 ， 材料加工工程， 2008， 博士

【摘要】 实现脉冲GTAW(Gas Tungsten Arc Welding)自动化与智能化的困难之一在于实现对焊接熔池动态行为,如熔池的尺寸、熔透及焊缝成形的实时检测与有效控制。电弧焊接是涉及材料、冶金、物理化学变化等多因素交互作用的复杂过程。焊接质量(焊缝成形、接头组织及性能)与焊接工艺的多参数有关,这些参数的作用相互关联,既有动态过程的耦合,又有静态效果的重叠。对于焊接动态过程这样的多变量、非线性、时变且含有诸多不确定因素和约束条件的复杂对象,采用基于精确数学建模方法,难以得到有效的可控制模型,决定了对焊接熔池的动态变化亦即对焊接熔宽、熔透和焊缝成形等质量控制是非常困难的。采用经典及现代控制理论方法来解决上述问题,同时受到理论上和应用上的挑战,难以达到满意的结果。本文以脉冲GTAW焊接过程为对象,对基于被动式熔池直接视觉图像传感的焊接过程的控制进行了深入的研究,研究工作着重于无模型自适应控制(MFC)方法与实际焊接过程的应用结合,将无模型自适应控制方法引入到脉冲GTAW熔透及焊缝成形控制中,该方法只需要GTAW过程的输入输出数据,不仅克服了焊接过程难以建立精确数学模型的困难,且由于具有较强的自适应能力,能克服焊接过程的多种不确定性因素。为了研究熔池特征参数与焊接工艺参数之间的动态关系,本文首先进行了传统阶跃试验,采用一阶惯性环节来描述焊接过程,得到了平稳焊接过程焊接脉冲峰值电流、送丝速度与熔池形状参数之间的数学模型。结果表明对于脉冲GTAW过程,焊接过程存在非线性、强耦合、时变等复杂特点。同时为了预测背面熔宽和正面余高以及满足控制仿真的需要,采用随机试验得到建模数据,分别建立了BP(Back Propagation)神经网络模型和ARX(Auto-Regressive Exogeneous)模型焊接过程很难用基于模型的控制方法实现高质量的焊缝成形控制,为了克服这一困难,本文将无模型自适应控制引入到脉冲GTAW的熔透及焊缝成形控制中。以背面熔宽为被控制量,焊接峰值电流为控制量,设计了GTAW过程单输入单输出无模型自适应控制器,闭环控制系统仿真验证了该方法的可行性。为了进一步验证该方法的有效性,设计了梯形、渐变哑铃形和突变哑铃形三种不同形状的工件,以代表实际焊接过程中不同的散热条件。基于上述三种工件的实验结果表明,在不同的散热条件下单输入单输出无模型自适应控制器均能实现较好的控制效果。为了增强无模型自适应控制器的自适应能力与抗干扰能力,使无模型自适应控制器具有智能化的调节能力,更符合实际的焊接过程控制需要。本文将模糊逻辑引入到无模型自适应控制中,设计了G函数模糊调节的无模型自适应控制器,该控制器能从一定程度上,反映实际焊工的操作经验。通过与无模型自适应控制基本方法的仿真比较可以看出,在相同的调节时间内,具有模糊调节功能的无模型自适应控制的超调量小,且响应曲线更加平滑。而在三种不同形状工件上的实际焊接实验进一步验证了该方法的有效性。与无模型自适应控制的基本方法相比,该方法使控制器的调节更加平滑,从而更有利于焊缝的稳定成形。GTAW过程焊缝成形除外界环境等因素影响外,它还受到多个可控焊接参数的直接影响。因此为了提高焊接质量,实现高效的自动化焊接技术,最终实现多变量GTAW过程控制,首先设计了以背面熔宽为被控量,以峰值电流和送丝速度为控制量的GTAW控制过程多输入单输出无模型自适应控制器。仿真及三种工件焊接过程都较好的保持了平稳性,背面熔宽在期望值附近波动,能较好满足焊缝成形控制的需要。与焊接过程单变量控制器相比,多输入单输出的无模型自适应控制其不仅能取得较满意的控制效果,且由于引入了另一个控制量,焊接过程中可以同时调节两个焊接参数,所以当受到外部干扰时,可以更加快速的将背面熔宽调节到期望值。由于焊接是一个多变量强耦合的复杂过程,为了使控制器能更加真实有效的体现焊接过程这一特征,提高控制器性能,提出了多输入多输出无模型自适应控制理论,给出了其具体的设计步骤及实现过程,该方法仍具有只利用系统输入输出数据即可实现控制目标的特点。针对脉冲GTAW过程,设计多输入单输出及多输入多输出无模型自适应控制器,最终实现了通过同时监测背面熔宽与正面余高,而同时调节焊接峰值电流和送丝速度,实现了正反面焊缝同时稳定成形。更多还原

【Abstract】 The fundamental difficulty of realizing pulsed GTAW automation and intellectualization is to detect and control the weld pool’s dynamic behavior, such as weld pool’s geometry parameters, weld penetration and weld beam forming. Arc welding is a complex process which involves interactions of materials, metallurgy and physical chemistry. Weld quality (weld beam forming, microstructures and properties of joint) is related to the multiple parameters of welding technology, these parameters are independent, coupled in the dynamic process and overlapped in the static process. Because welding process is a multi-variable, non-linear, time-varying process which also contains many uncertain factors and constraint conditions, it is very difficult to get an efficient control model based on the accurate mathematic modeling method, and this makes it very difficult to control the weld pool’s dynamic changes, and to control the weld pool’s width, penetration and weld beam forming. To solve the problem by using of classic and modern control theory will face the challenges both on the theory and application, and this makes it difficult to get an ideal result. This paper takes the pulsed GTAW as the research object, and does research on the control of welding process based on the passive visual sensor of weld pool, this paper focuses on the combination of the model-free adaptive control method and practical application of welding process, and applies the model-free adaptive control method in the penetration and weld beam forming control of pulsed GTAW. This method only needs the input and output data of GTAW process, it not only overcomes the difficulty of obtaining an accurate mathematic model of welding process, but also overcomes many uncertain factors of the welding process because it has stronger adaptive ability.In order to search the dynamic relation between pool characteristic parameters and welding parameters, the conventional step experiment were firstly carried out and the one-order inertia models of welding peak current, wire feeding speed and welding speed to geometry parameters of weld pool are obtained. The results show that arc welding is characterized as multi-variable, strong coupling, nonlinear, time varying. In order to predict the backside width and topside height of the weld pool and meet the requirement of control simulation, the BP (Back Propagation) neural network model and ARX (Auto-Regressive Exogeneous) model were built in a random design. These models can be used in control simulation and actual welding experiments.It’s difficult to model welding process accuratly. To overcome this problem, SISO (Single Input Single Output) model-free adaptive controller of GTAW was designed, and the control variable was weld peak current, the controlled variable was backside width of weld pool. The closed loop control system simulation was carried out based on ARX model and BP neural network prediction model to prove the feasibility of this method. To verify the validity of this controller, three kinds of worpieces were designed to represent different heat emission conditions, which were trapezia-shaped workpiece, graded dumbbell-shaped workpiece and mutant dumbbell-shaped workpiece. The actual welding experiments of three different shaped workpieces were carried out, the results show that SISO model-free adaptive control can get better controlled performance.Enlightened by the intelligent behavior of welders, this paper combines the fuzzy control and model-free adaptive control algorithm base on the general form of model-free adaptive control, merges the welder’s experience knowledge into the design of controller, and designs the fuzzy logic model-free adaptive controller. This controller can reflect the welder’s operating experiences to some extent. Compared with the simulation of the basic model-free adaptive control method, it can be shown that the overshoot of the model-free adaptive control method with fuzzy adjusting function is smaller and the response curve is more smoothing. The welding experiments on the three work-pieces of different shape prove the effectiveness of this method.Besides influencing of uncontrolled variables and external, GTAW process is directly affected by many controlled welding parameters. In order to improve weld quality and reach high efficient automatic welding technology, MISO (Multi Input Multi Output) model-free adaptive control of GTAW were designed by analyzing the problem of variables selection. Simulation and welding experiments show that the welding shaping is uniform and can meet the needs of welding process. Compared with single variable model-free adaptive control of welding process, multi variables model-free adaptive control not only receive better control performance but also make the variation range of control variable smaller.Based on the theory of MISO model-free adaptive control, MIMO (Many Input Many Output) model-free adaptive control theory, design methods and implementation steps are presented. Then MIMO model-free adaptive control of GTAW was designed, in which the control variables were welding peak current and the wire feeding speed, the controlled variables were backside width of weld pool and topside height of weld pool. Simulation and experiments results show that when the heat emission condition of worpieces changes, control variables can make controlled variables to arrive at desire values fastly. And because of choosing topside height of weld pool as another controlled variable, not only fusion penetration can be ensured but also topside appearance of weld quality can be ensured, which is important in practical productions.更多还原