Nonlinear interval model control of quasi-keyhole arc welding process

This paper addresses the development of a nonlinear model based interval model control system for the quasi-keyhole arc welding process, a novel arc welding process which has advantages over the laser welding process and conventional arc welding processes. The structure of the nonlinear model chosen was proposed based on an analysis of the quasi-keyhole process to be controlled. Because of the variations in the manufacturing conditions, the parameters of the nonlinear model are uncertain but bounded by fixed intervals if the range of the manufacturing conditions is specified. To determine the intervals, extreme operating conditions/parameters (manufacturing conditions) were used to conduct experiments. Each experiment gives a set of model parameters and the interval for each parameter is given by the minimum and maximum among the values obtained from different experiments. Closed-loop control experiments have verified the effectiveness of the developed system as a robust control which requires no re-adjustment and can function properly when fluctuations/variations in manufacturing conditions, and thus the process dynamics, change, vary, or fluctuate.     

基于粗糙集理论的焊接过程建模方法

针对目前焊接建模的状况,针对一种比较典型的焊接材料和焊接方法———铝合金脉冲钨极电弧(TIG)焊接,提出一种基于粗糙集理论的建模方法,并对建模过程的主要步骤和关键问题作了阐述.实验结果表明,该模型精度和复杂度可以接受.该方法拓展了粗糙集理论的应用领域,并为焊接建模提供了一种新方法,具有较大的应用潜力.     

脉冲MIG焊电弧双模模糊控制

针对脉冲M IG焊焊钢时的电弧稳定性控制,采用峰值弧压反馈的方式,设计了基于修正因子的电弧双模模糊控制器,当峰值弧压偏差较大时采用基于修正因子的粗调模糊控制规则,以送丝速度Vf为控制量,而当峰值弧压偏差较小时采用基于修正因子的细调模糊控制规则,以基值时间Tb为控制量,以查表方式实现模糊控制器.最后通过试验验证了在焊接过程中弧长变化时所设计的模糊控制器可保持电弧的稳定从而保证焊接过程的稳定.     

YYPO-SA：一种新的基于YYPO和SA的混合单目标随机优化算法

针对原阴阳对优化算法(YYPO)早熟易收敛的问题,在YYPO算法中的阴阳两点交换阶段加入模拟退火算法(SA)策略,提出了两种使用不同交换策略的新算法,即YYPO-SA1和YYPO-SA2,统称为YYPO-SA.YYPO-SA算法既保持了YYPO轻量级的特点,又综合了YYPO优秀的全局搜索能力和SA良好的局部搜索性能.算法采用2013年进化计算大会的单目标实参算法竞赛中使用的28个测试函数进行性能评估,将YYPO-SA和YYPO、自适应阴阳对算法(AYYPO)、改进的阴阳对算法(IYYPO),以及另三个性能优越的单目标优化算法,即灰狼优化算法、鲸鱼优化算法,正弦余弦算法进行性能比较.实验结果表明YYPO-SA能取得更为稳定的求优能力和更高的计算精度.最后通过一个工程优化任务来展示新算法的性能.     

弧焊机器人直线轨迹精度测试研究

在传统平面最小二乘拟合方法的基础上,提出空间直线拟合的方法。利用该方法对弧焊机器人的直线轨迹进行了拟合,拟合结果显示,机器人实到轨迹与指令轨迹之间存在较大误差,通过分析误差分布情况可以为机器人的位姿误差补偿及精度优化设计提供依据。     

基于旋转电弧传感器的焊接电流波形研究

为了实现焊接的自动化,必须研制出新型的传感器,并对采集到的信号进行滤波与分析。介绍了旋转电弧传感器的工作原理。由于采样得到的焊接电流中含有大量的噪声,必须通过多种滤波方法的组合,最终得到较理想的焊接电流波形。为了研究焊接电流的波形,进行了小弯曲角焊缝的跟踪实验,直角转弯角焊缝的跟踪实验。结果表明:滤波后的焊接电流波形比较理想,通过焊接电流波形,可以识别出焊枪是前倾、后倾,还是既不前倾也不后倾,焊枪是左偏、右偏,还是对中,为偏差识别和焊接机器人的控制铺平了道路。     

Autonomous weld seam identification and localisation using eye-in-hand stereo vision for robotic arc welding

One of the main difficulties in using robotic welding in low to medium volume manufacturing or repair work is the time taken to programme the robot to weld a new part. It is often cheaper and more efficient to weld the parts manually. This paper presents a method for the automatic identification and location of welding seams for robotic welding using computer vision. The use of computer vision in welding faces some difficult challenges such as poor contrast, textureless images, reflections and imperfections on the surface of the steel such as scratches. The methods developed in the paper enables the robust identification of narrow weld seams for ferrous materials combined with reliable image matching and triangulation through the use of 2D homography. The proposed algorithms are validated through experiments using an industrial welding robot in a workshop environment. The results show that this method can provide a 3D Cartesian accuracy of within ±1 mm which is acceptable in most robotic arc welding applications.     

基于模拟退火的自适应粒子群优化算法的改进策略

针对PSO算法在求解问题的优化问题中易陷入局部收敛且收敛速度较慢等缺陷,引入一种初始化改进策略,并将模拟退火算法与PSO算法相结合,提出了一种全新的算法。该算法将寻优过程分为两个阶段:为了提高算法的执行速度,前期使用标准PSO算法进行寻优,后期运用模拟退火思想对PSO中的参数进行优化搜索最优解。最后将该算法应用于八个经典的单峰/多峰函数中。模拟结果表明,该算法有效地避免了早熟收敛现象,并提高了收敛速度,从而提高了PSO算法解决全局优化的性能。     

A smart modelling for the casting temperature prediction in an electric arc furnace

The efficient and reliable control of an electric arc furnace (EAF) is a challenging problem, due to the strong intercorrelation among process variables, the large dimension of the input and output space, the strong interaction among process variables, a large time delay, and a highly nonlinear behaviour. This paper presents a model that allows us to optimize the control and, therefore, the electric power consumption in an EAF. The data used for this study were collected from Bizkaia Steel Mill (Arcelor Company). Neural network and fuzzy logic techniques have been applied on these data in order to get an improved model of the casting temperature inside the furnace's hearth. First, we developed some neural network models with different topologies and input variables. Then we used the best model obtained in the previous step to combine it with a fuzzy logic technique to get the final model. Comparison with experimental data and other models is carried out to validate the proposed model. Finally, the conclusions and future studies are exposed.     

解决多维全局最优问题的一种新方法

对一种新的全局优化方法(称为APSAM方法)进行了研究,将模拟退火方法的随机搜索策略与局部寻优算法POWELL相结合,使得求解过程可以跳出局部最优值的区域,最终获得全局最优解。最后通过对一些典型的多极值方程进行优化,比较了APSAM方法与模拟退火法、POWELL法和PSAM方法的优化结果,仿真结果说明提出的算法优化能力较强,效果稳定可靠。     

Detection of fillet weld joints using an adaptive line growing algorithm for robotic arc welding

One of the main challenges for robotic welding in low to medium volume manufacturing or repair work is the time taken to programme the robot path for a new job. It is often cheaper and more efficient to weld the parts manually. There are many papers published on the detection of butt welds, however there is no mature method for the identification of fillet welds which are more common. This paper presents a novel method that can autonomously identify fillet weld joints regardless of the base material, surface finish and surface imperfections such as scratches, mill scale and rust. The new method introduces an adaptive line growing algorithm for robust identification of weld joints regardless of the shape of the seam. The proposed method is validated through experiments using an industrial welding robot in a workshop environment. The results show that this method can detect realistic fillet weld joints for industrial arc welding applications.     

基于连续覆盖的城市消防站布局优化

摘要：随着城市化和工业化进程的加快,城市灾害风险日益加剧,城市已有消防站布局已不能满足消防安全需求,为了有效克服传统离散覆盖算法在选址问题中的误差大的问题,算法主要把图的离散的节点-边二元图结构替换为连续的网状空间结构,将离散的集覆盖加以扩展,实现对道路网的连续覆盖。在优化选址过程中,引入模拟退火算法,并对退火流程加以改进。通过算例验证该选址方法,并对两种选址方法的指标进行对比分析。实例表明,基于连续覆盖的选址方法有较好表现,具有较强的实用性和合理性。     

利用遗传模拟退火算法优化神经网络结构

常用的神经网络是通过固定的网络结构得到最优权值,使网络的实用性受到影响。引入了一种基于方向的交叉算子和变异算子,同时把模拟退火算法引入了遗传算法,结合遗传算法和模拟退火算法的优点,提出了一种优化神经网络结构的遗传——模拟退火混合算法,实现了网络结构和权值的同时优化。仿真实验表明,与遗传算法和模拟退火算法相比,该算法优化的神经网络收敛速度较快、预测精度较高,提高了网络的处理能力。     

Application of genetic algorithm to external noise cancellation and compensation in automatic arc welding system

This paper develops an automatic welding control system which uses a genetic algorithm to carry out external noise cancellation and compensation. In the proposed approach, the genetic algorithm is used to identify the polynomial form or parameters of the external force or disturbance, including the amplitude, frequency and phase to compensate for nonlinear phenomenon such as disturbance in the mechanical system. In compensating for the nonlinear phenomenon, a piecewise linearization of the approximation polynomial is performed and the genetic algorithm optimization process is then used to identify the parameters of the polynomial function. The simulation and experimental results confirm that the proposed automatic welding control system provides an effective means of compensating for the effects of the external force or disturbance and therefore results in an enhanced welding performance.     

Impact of gas formation on the transfer of Ti and O from TiO2-bearing basic-fluoride fluxes to submerged arc welded metals: A thermodynamic approach

Gas-slag-metal equilibrium calculations are performed to investigate the transfer of Ti and O to submerged arc welded metals with increasing TiO₂ addition in basic-fluoride fluxes. The results indicate that activities of TiO₂ and Ti₂O₃ considering slag-metal reactions alone do not account for Ti transfer behaviors since TiF₃ gas tends to form and reduce Ti-oxide activities. Thermodynamic simulation indicates that consideration of gases is essential to improve the prediction accuracy of Ti and O concentrations.     

VC++环境下的BP神经网络建模和模拟退火优化研究

文章简要介绍了BP神经网络和模拟退火算法的原理,提出了一种基于VC 环境的BP神经网络建模与模拟退火优化方法,通过模拟退火对BP网络的输入参数进行优化,实现算法接口并进行了软件设计.采用一个焊接算例进行验证,证明了该BP神经网络逼近能力强、收敛速度快,模拟退火能寻找到最优焊接参数,该软件平台具有一定的实用性.     

A study on the quality improvement of robotic GMA welding process

With the advance of the robotic welding process, procedure optimisation that selects the welding procedure and predicts bead geometry that will be deposited has increased. A major concern involving procedure optimisation should define a welding procedure that can be shown to be the best with respect to some standard, and chosen combination of process parameters, which give an acceptable balance between production rate and the scope of defects for a given situation.

This paper represents a new algorithm to establish a mathematical model for predicting top-bead width through a neural network and multiple regression methods, to understand relationships between process parameters and top-bead width, and to predict process parameters on top-bead width in robotic gas metal arc (GMA) welding process. Using a series of robotic GMA welding, additional multi-pass butt welds were carried out in order to verify the performance of the multiple regression and neural network models as well as to select the most suitable model. The results show that not only the proposed models can predict the top-bead width with reasonable accuracy and guarantee the uniform weld quality, but also a neural network model could be better than the empirical models.     

Simulating and modelling the DAX index and the USO Etf financial time series by using a simple agent-based learning architecture

This work presents an extensive case study on modelling the DAX (Deutscher Aktienindex) index and United States Oil Fund (USO) exchange-traded fund (Etf) time series with the financial agent-based system learning financial agent-based simulator (L-FABS) that exploits simulated annealing as a learning method. The USO Etf time series is highly correlated with oil price behaviour, and the DAX index is based on the weighted and accumulated behaviour of the share prices of some of the largest companies traded on the Frankfurt Stock Exchange. These two time series are driven by completely different economic factors and thus provide two diverse empirical settings to evaluate the effectiveness of our methodology. Our experimentation shows that a relatively simple computational representation of real financial markets is effective in capturing the overall behaviour of the time series with varying approximation levels while the prediction target is moved into the future. The reported experimental investigation of L-FABS shows that it is robust notwithstanding the learning method used and the data sets exploited. L-FABS indeed produced a relatively low approximation error in several settings even when evaluated with respect to other modelling approaches, for example, 0.88% and 1.61% errors on average for 1 day ahead experiments in, respectively, DAX index and USO Etf.     

一种用于片上网络布图规划的改进模拟退火与粒子群混合算法

智能优化算法作为解决大规模集成电路芯片设计中布图规划问题的经典方法已被研究多年。结合异构三维片上网络布图问题的具体特点,采用B*-tree间接描述布图问题中的解结构,针对模拟退火收敛速度慢、优化效率低的缺点,对搜索策略和概率性的劣向转移作出了改进,并将改进后的模拟退火思想引入粒子群优化算法中,使结合后的算法结合了粒子群并行计算的特点和模拟退火能够实现全局优化的特点。通过仿真实验验证,所提出的该混合改进算法在解决布图问题中要优于传统模拟退火算法。     

智能优化算法的应用研究

优化问题广泛存在于各个领域,对该问题的求解问题从没停止过.自从优化问题提出以来,人们提出了各种各样的智能优化算法.文中简要介绍了遗传算法、蚁群算法、模拟退火算法3种智能优化算法,并简述其优缺点及应用研究的使用情况.