基于电流信号钻头磨损状态预报系统

介绍了一个钻头磨损状态在线预报系统。通过监测主电机电流信号建立神经网络动态预报模型 ,对钻头后刀面磨损量分类建立在不同磨损类别下的神经网络模型 ,以神经网络模型估算的电流值作为模糊聚类中心 ,根据预报电流值对钻头磨损状态进行模糊分类 ,从而预报磨损状态。实验表明 ,此预报系统具有较高的成功率和可靠性     

基于粗糙模糊神经网络的微孔钻削在线监测

为解决微孔钻削过程中信号特征与钻头磨损状态关系模型难以建立这一问题,提出一种基于粗糙集理论的模糊神经网络关系模型,首先运用粗糙集理论从数据样本中进行规则集约简,使得网络的模糊规则数目减少,克服了当输入维数高时,模糊神经网络模糊规则过多,结构过于庞大的缺点.然后根据这些规则来设计神经网络的结构模型.应用构造好的网络对主轴电机三相电流信号进行实时数据处理,获取隐含微细钻头磨损状态的信息值,对微孔钻削过程进行在线监测实验,结果表明,适当选择监测阈值,可以有效避免微细钻头的折断.     

基于粗糙模糊神经网络的微孔钻削在线监测方法

提出了基于粗糙集理论的模糊神经网络对微孔钻削进行在线实时监测的方法,能有效利用粗糙集理论对控制规则进行约简,再利用钻削力、钻削扭矩和主轴电机电流信号作为神经网络的输入,构建相应的模糊神经网络模型,对系统进行训练,进而对提取的精简规则参数进行优化,从而解决了常规粗糙集控制规则缺乏学习能力,自适应性差的问题.采用该方法可获取微细钻头磨损状态的信息,对微孔钻削过程进行在线实时监测,可有效避免微钻头折断,并降低制造成本,提高生产效率.     

微孔钻削加工的钻削力监控

对以钻削力为监测对象的微孔钻削实时监测系统进行了研究。建立了一个BP神经网络模型，利用该模型对钻削过程进行监测。有效地避免了微钻头的折断，提高了微钻头的利用率。     

基于多信号模糊融合的微孔钻削在线监测

利用轴向力、扭矩和主轴电机电流信号来监测微小钻头在钻削过程中的状态,提出了运用模糊控制作为多信号融合来监测微钻头状态的监测模型。结果表明,利用该模型得到的监测阈值进行在线监测可有效避免钻头折断。     

基于声发射信号的蜗轮磨损程度在线监测

为实现对蜗轮蜗杆减速器工作过程中蜗轮磨损程度的精确监测,利用多通道声发射检测仪器对不同磨损程度的蜗轮声发射信号进行在线采集。采用小波分析方法对信号进行去噪处理,提取声发射特征信号值,根据最小模糊熵优化模型构造出不同磨损程度蜗轮的模糊隶属度函数。采用ANFIS多维模糊神经网络实现多通道声发射信号的决策融合,提高了蜗轮磨损程度识别结果的准确性。通过对随机磨损程度的蜗轮进行实际验证,实验结果验证了系统的有效性和可靠性。     

基于自适应神经-模糊推理系统模糊信息融合的采煤机截齿磨损在线监测

为实现对截齿截割过程中磨损程度的实时精确在线监测,分别测试和提取不同磨损程度的截齿在截割过程中的振动信号、声发射信号和温度信号,建立不同磨损程度截齿截割信号的多特征样本数据库,根据最小模糊度优化模型计算求解各特征信号的最优模糊隶属度函数,采用自适应神经-模糊推理系统多维模糊神经网络方法实现多传感特征信息的决策融合,输出置信度和权重较高的截齿磨损量融合结果。通过随机测试实验对融合系统进行验证,结果表明,基于ANFIS模糊信息融合的截齿磨损监测系统辨识度较高,测试结果最大误差在6.5%以内,系统具有良好的融合效果以及较高的测试精度。     

基于钻削力的微钻头破损特征量的提取

建立了微孔钻削监测系统，对采集的正常钻削与钻头破损两种状态下的钻削力信号进行分析和处理，提出了分别在低频段和高频段能够反映钻头破损状态的特征量。     

基于BP神经网络的超声振动钻削钻头磨损状态监测试验研究

搭建了超声轴向振动钻削钻头磨损状态的钻削力和声发射信号采集系统,采集不同磨损状态下钻中区域的钻削力和声发射信号进行小波分解,得到与钻头磨损状态相关的特征量作为识别钻头磨损状态的特征参数,输入到建立的6-13-3的三层BP神经网络模型中进行融合,识别钻头磨损状态。试验结果表明,通过BP神经网络技术将钻削力和声发射信号融合识别钻头磨损的准确率约88.9%,能够有效监测钻头磨损状态。     

刀具破磨损的自动监测技术

本文主要介绍在机械加工过程中同时监测主轴电机电流和声发射(AE)信号,综合实时判断车刀、钻头等刀具的破损和极限磨损的原理,监测系统框图以及实验方案、实验结果。该监测系统可自动识别(?)0.8mm 以上的钻头破损,大于面积0.2mm~2车刀的破损和极限磨损,成功率达96%以上。     

基于ANFIS的温度传感器非线性校正方法

介绍了用神经网络进行传感器非线性误差校正的原理与方法,分析了自适应神经模糊推理系统(ANFIS)的基本原理。通过模糊聚类和混合学习算法,ANFIS可以逼近高阶输入输出非线性系统,将该算法用于两个典型非线性系统建模,均能获得满意结果。之后,将ANFIS算法用于温度传感器非线性校正中,试验结果表明该方法与基于CMAC网络和BP网络的校正方法相比,校正的精度高于以上两种校正方法。     

用模糊神经网络进行铣刀磨损状态识别

刀具磨损监测过程是一个模式识别过程,模糊推理和人工神经网络都是进行模式识别非常有效的办法,针对模糊系统和神经网络各自表现出来的不足,将模糊推理和神经网络结合起来,充分利用模糊系统在处理结构性知识上的优势和神经网络在自学习和并行处理上的能力,形成模糊神经网络进行刀具磨损在线监测识别.通过研究模糊系统和神经网络的结合形势,...     

Navigation of autonomous mobile robot using adaptive network based fuzzy inference system

Intelligent soft computing techniques such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are proven to be efficient and suitable when applied to a variety of engineering systems. The hallmark of this paper investigates the application of an adaptive neuro-fuzzy inference system (ANFIS) to path generation and obstacle avoidance for an autonomous mobile robot in a real world environment. ANFIS has also taken the advantages of both learning capability of artificial neural network and reasoning ability of fuzzy inference system. In this present design model different sensor based information such as front obstacle distance (FOD), right obstacle distance (ROD), left obstacle distance (LOD) and target angle (TA) are given input to the adaptive fuzzy controller and output from the controller is steering angle (SA) for mobile robot. Using ANFIS tool box, the obtained mean of squared error (MSE) for training data set in the current paper is 0.031. The real time experimental results also verified with simulation results, showing that ANFIS consistently perform better results to navigate the mobile robot safely in a terrain populated by variety obstacles.     

基于模糊神经网络系统的三相感应电机矢量控制

为实现三相感应电机稳定控制,提出了一种基于自适应模糊神经网络推理系统（ANFIS）的感应电机矢量控制方法。ANFIS结合了模糊逻辑的调节能力与神经网络的自适应能力,被广泛的应用到电机的参数估计、转速、转矩和磁链控制中。在分析感应电机工作原理的基础上,推导出其数学模型,在Matlab/Simulink上采用基于ANFIS的矢量控制对三相感应电机进行系统仿真,仿真结果表明,该控制策略转矩波动小,转速响应快,具有良好的动态和静态性能。     

Accurate estimation of surface roughness from texture features of the surface image using an adaptive neuro-fuzzy inference system

Accurate estimation of surface roughness of workpieces in turning operations play an important role in the manufacturing industry. This paper proposes a method using an adaptive neuro-fuzzy inference system (ANFIS) to establish the relationship between actual surface roughness and texture features of the surface image. The accurate modeling of surface roughness can effectively estimate surface roughness. The input parameters of a training model are spatial frequency, arithmetic mean value, and standard deviation of gray levels from the surface image, without involving cutting parameters (cutting speed, feed rate, and depth of cut). Experiments demonstrate the validity and effectiveness of fuzzy neural networks for modeling and estimating surface roughness. Experimental results show that the proposed ANFIS-based method outperforms the existing polynomial-network-based method in terms of training and test accuracy of surface roughness.     

Artificial intelligent modeling to predict tensile strength of inertia friction-welded pipe joints

Adaptive neural network-based fuzzy inference system (ANFIS) is an artificial intelligent neuro-fuzzy technique used for modeling and control of ill-defined and uncertain systems. The present paper proposes this novel technique of ANFIS to predict the tensile strength of inertia friction-welded tubular pipe joints with the aid of artificial neural network approach combined with the principle of fuzzy logic. The proposed model is multiple input–single output type of model which uses rotational speed and forge load as input signals. The set of rules has been generated directly from the experimental data using ANFIS. The performance of the proposed model is validated by comparing the predicted results with the actual practical results obtained by conducting the confirmation experiments. The application of χ ² test confirms that the values of tensile strength predicted by proposed ANFIS model are well in agreement with the experimental values at 0.1 % level of significance. The proposed model can also be used as intelligent online adaptive control model for pipeline welding.     

Research on intelligent launching control of dual clutch transmissions based on adaptive neural fuzzy inference system

Vehicle launching has an important influence on driving performance of the vehicle. For vehicles with dual clutch transmissions (DCT), the clutch torque control is the key to the launching control. Therefore, a data-driven control method for DCT launching process based on adaptive neural fuzzy inference system (ANFIS) is proposed. Firstly, the vehicle test data during launching process is collected and the optimal clutch torque is obtained based on multi-objective particle swarm optimization (MOPSO). Afterward, to learn the launching control rules from optimization results, the combination of neural network and fuzzy logic algorithm, referred to as an ANFIS, is established. The dataset of the optimized launching clutch torque is utilized to train the ANFIS controller. Finally, the simulation and test results show that the data-driven control can accurately learn the launching control rules from the optimality, thereby achieving the optimal control for different launching intentions.

     

非线性系统的模糊建模及仿真

以一个非线性模型为研究对象、通过对自适应神经模糊推理系统（ANFIS）建模机理的研究,建立了非线性实例模糊模型,并且在不同的输入下进行仿真实验,结果表明利用ANFIS进行非线性系统建模和辨识是可行的,其辩识精度很高.     

Using data-fuzzification technology in small data set learning to improve FMS scheduling accuracy

Production decisions in real dynamic flexible manufacturing systems (FMS), especially in the early stages are often made with limited information. Information is limited because scheduling knowledge is hard to establish in such an environment. Though the machine learning technique in the field of Artificial Intelligence is thus used for this task by many researchers, this research is aimed at increasing the accuracy of machine learning for FMS scheduling using small data sets. Approaches used include data-fuzzifying, domain range expansion, and the application of adaptive-network-based fuzzy inference systems (ANFIS). The results indicate that learning accuracy under this strategy is significantly better than that of a traditional crisp data neural networks.     

Application of an Adaptive Neuro-Fuzzy Inference System for the Optimal Analysis of Chemical-Mechanical Polishing Process Parameters

This paper examines the machining parameters during the wafer flattening process by chemical–mechanical polishing (CMP). There are very few data available from CMP experiments for wafer flattening. This study adopted an adaptive neuro-fuzzy inference system (ANFIS) to predict the surface roughness in the absence of CMP experiments. An integrated concept like ANFIS combines the advantages of the two systems of fuzzy control and neuro networks. Next, the feasible directions algorithm and sequential approximation algorithm from the local search method are combined with ANFIS. During the process of combination, the value from the optimisation theory is replaced by that from the ANFIS, so that, the roughness value of the wafer surface can be predicted. Alternatively, the optimal values of various process parameters can also be predicted. To sum up, verification through experiments indicates that the optimal experimental values of process parameters are identical with those predicted by the optimisation theory and ANFIS. Thus, the optimal precise value can be simulated and predicted within the parameters of the experimental design. The predicted optimal result is compared with the optimal experimental result of Kung and Dai to show that the predicted optimal result is acceptable. As a result, the CMP process parameters investigated in this study can be controlled.