Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations

Due to the complexity of the machine tool structure and the cutting process, the dynamics of machining processes are still not completely understood. This is especially true due to the demand of high-speed machining to increase productivity. In order to model and control these complex processes, new approaches, which can represent complex phenomenon combined with learning ability, are needed. The combined neural–fuzzy approach appears to be ideally suited for this purpose. In this paper, the recently developed fuzzy adaptive network (FAN) is used to model surface roughness in turning operations. The FAN network has both the learning ability of neural network and linguistic representation of complex, not well-understood, vague phenomenon. Furthermore, it can continuously improve the initially obtained rough model based on the daily operating data. To illustrate this approach, a model representing the influences of machining parameters on surface roughness is established and then the model is verified by the use of the results of pilot experiments. Finally, a comparison with the results based on statistical regression is provided.     

基于遗传算法的箱型结构焊接顺序优化

基于遗传算法原理,建立了箱型结构热-机耦合非线性三维优化仿真模型,以焊接变形为目标函数,进行了焊接顺序数值仿真优化.通过仿真计算优化结果与试验数据比较,证明所提出的优化方法与热-机模型结合是可行的,对于给定的焊接条件,通过选择合适的目标函数,采用遗传算法可以确定最优焊接顺序.这为针对工程具体问题设计焊接工艺提供了可靠的依据.     

‘Odds Algorithm’-based Opportunistic Maintenance Task Execution for Preserving Product Conditions

Today, a new role for maintenance exists to enhance the eco-efficiency of the product life cycle. The concept of “life cycle maintenance” emerged to stress this role leading to push, at the manufacturing stage, an innovative culture wherein maintenance activities become of equal importance to actual production activities. This equivalence requires mainly considering the integration of the maintenance and production strategy planning for developing opportunistic maintenance task keeping conjointly the product - production - equipment performances. In this paper, a novel approach is proposed for integrating maintenance and production planning. The approach uses the “odds algorithm” and is based upon the theory of optimal stopping. The objective is to select, among all the production stops already planned, those which will be optimal to develop maintenance tasks keeping the expected product conditions. It combines criteria such as functional performance and component reliability. Approach feasibility is shown on a practical example.     

TIG焊熔宽的自整定PID闭环控制系统的研究

对于焊接过程中常出现各种干扰,焊接条件时有变化的TIG自动焊而言,改进传感方法和控制技术是提高焊缝质量闭环控制系统控制效果的两大途径。本文在对采用CCD摄像机正面拍摄熔池图像并用微机进行图象处理,以及对参数在线自整定PID控制原理的研究基础上,设计了一种TIG焊参数在线自整定PID熔宽闭环控制系统。实验结果表明,该系统具有良好的控制特性和对焊接条件变化的适应性,可用于TIG焊焊缝质量的实时控制。     

基于人工神经网络的铜合金形变热处理工艺和性能

利用神经网络对Cu-Cr-Zr合金变形量、时效温度和时间与硬度和电导率样本集进行训练和学习，采用改进的BP网络算法-Levenberg—Marquardt算法，建立了形变热处理工艺BP神经网络模型，得出了具有较高综合性能的最佳工艺参数：在80％变形量，450-480℃，2～5h形变热处理条件下，硬度和电导率分别可达HV150～157和74％～77％(IACS)。     

同伦迭代法在触模算法中的应用

加工过程仿真中的触模处理 ,实际上是射线与曲面求交的问题 ,即非线性方程组的求解问题。广泛应用的Newton迭代法的收敛性取决于迭代初始值的选取 ,是局部收敛的方法。基于同伦映射的同伦算法 ,具有大范围收敛性。文中尝试将同伦迭代应用于触模算法中 ,数值算例验证了这种处理方法的可行性和有效性     

一种基于遗传算法的板形模式识别方法

针对冷轧带钢生产中板形控制的要求 ,提出采用遗传算法进行板形模式识别的方法 ,该方法的特点是可以根据不同轧机的板形控制模式决定板形模式方程 ,板形识别精度高。仿真结果证明了该方法的有效性     

A354合金热处理制度的人工神经网络模型

采用人工神经网络方法,研究了固溶温度、固溶时间、时效温度和时效时间对A354合金热处理性能的影响,建立了A354合金热处理制度的人工神经网络模型.     

多任务小批量流水线遗传算法仿真与实施

文章阐述了在流水线调度时,可利用遗传算法计算出工件的最短通过时间, 但这并非最佳方案,应同时考虑交货期的影响、设备的负荷率和员工加班等问题.在设计过程中,根据多任务,小批量流水线生产的特点,改进常规遗传算法,设计出一种基于交货期的遗传算法,可以很快地搜索到最优个体,并实例证明了其可行性.     

基于遗传算法的Al-5%Cu合金电脉冲孕育处理参数优化

以实验为基础,利用神经网络和遗传算法优化Al-5%Cu合金的电脉冲孕育处理工艺参数。神经网络的输入参数为脉冲电压、脉冲时间和电脉冲孕育处理时熔体温度,输出参数是合金凝固组织的晶粒度。在神经网络训练的基础上,采用遗传算法优化神经网络的输入参数。结果表明,神经网络和遗传算法的组合建模获得了较好的优化结果。