人工神经网络预测刀具磨损和切削力

刀具磨损和切削力预测与控制是切削加工过程中需要考虑的重要问题.本文介绍了利用人工神经网络模型预测刀具磨损和切削力的步骤并且针对产生误差的因素进行分析.首先将切削速度、切削深度、切削时间、主轴转速和不同频带的能量值通过归一化法处理,作为输入特征值,对改进的神经网络模型进行训练.然后利用训练完成的神经网络模型预测刀具磨损和切削力.结果表明:神经网络模型能够综合考虑加工过程中更多的影响因素,与经验公式结果对比,具有更高的预测精度.研究结果表明神经网络模型预测刀具磨损和切削力具有可行性和准确性,为刀具结构的优化及加工参数的选择提供了依据.     

高速高精度运动控制算法的研究与应用

为解决裁床运动控制系统在加工不规则轨迹曲线中存在的插补精度低,效率不高的问题,提出了基于改进BP神经网络B样条曲线插补算法的研究与设计。该算法通过加入动量因子改进BP神经网络离线训练B样条曲线,利用负反馈校正输出预测插补点,避免了BP神经网络插补器自身带来的偏差。同时根据加工曲线曲率半径的变化完成对速度的前瞻规划,实现了加工在拐角处的高速过渡。最后在Matlab上进行了算法仿真并在实验平台上进行了测试,实验结果表明本文提出的裁床运动控制算法能够高效高精度的完成材料切割。     

A neural network based methodology for machining operations selection in Computer-Aided Process Planning for rotationally symmetrical parts

The relevant literature on machining operations selection in Computer-Aided Process Planning (CAPP) by decision trees, expert systems and neural networks has been reviewed, highlighting their contributions and shortcomings. This paper aims at contributing to the applicability of back-propagation neural network method for the selection of all possible operations for machining rotationally symmetrical components, by prestructuring the neural network with prior domain knowledge in the form of heuristic or thumb rules. It has been achieved by developing two forms of representation for the input data to the neural network. The external representation is used to enter the crisp values of the input decision variables (namely the feature type and its attributes such as diameter or width, tolerance and surface finish). The purpose of internal representation is to categorize the above crisp values into sets, which correspond to all the possible different ranges of the above input variables encountered in the antecedent ‘IF’ part of the thumb rules mentioned above. The input layer of the neural network has been designed in such a way that one neuronal node is allocated for each of the feature types and the sets of various feature attributes. In the output layer of the neural network, one neuronal node is allocated to each of the various feasible machining operation sequences found in the consequent ‘THEN’ part of the thumb rules. A systematic method for training of the neural network has been presented with the above thumb rules used to serve as guidelines for choosing the input patterns of the training examples. This method simplifies the process of training, reduces the time for preparation of training examples and hence the time to develop the overall process planning system. It can further help ensure that the entire problem domain is represented in a better manner and improve the quality of response of the neural network. The example of an industrially-relevant rotationally symmetrical workpiece has been analyzed using the proposed approach to demonstrate its potential for use in the real manufacturing environment. By this novel methodology, workpieces of complex shapes can be handled by investing a very limited amount of time, hence making it attractive and cost effective for industrial applications. Received: June 2005 / Accepted: January 2006     

A neural network approach to multiple-objective cutting parameter optimization based on fuzzy preference information

This paper presents a neural network approach to multiple-objective cutting parameter optimization for planning turning operations. Productivity, operation cost, and cutting quality are considered as criteria for optimizing machining operations. A feedforward neural network and a dynamic training procedure are proposed for modeling manufacturers' preferences using sampled fuzzy preferential data. Optimum cutting parameters are determined based on neural network representations of manufacturers' fuzzy preference structures.     

进化神经网络在机床工具损耗预测中的应用

设计了一个基于实数编码的改进进化算法优化神经网络的连接权和网络结构.该算法 可以根据种群停止进化代数自适应调节变异率、根据个体适应度调节变异量.加工实验表明采用 进化神经网络可以较准确预测出电火花铣削加工工具损耗,所提出的进化算法是有效的,预测结 果较标准BP神经网络高.该预测模型为电火花铣削加工工具在线自动补偿打下基础.     

On-line robust identification of tool-wear via multi-sensor neural-network fusion

Real-time identification and monitoring of tool-wear in shop-floor environments is essential for the optimization of machining processes and the implementation of automated manufacturing systems. This paper analyzes the signals from an acoustic emission sensor and a power sensor during machining processes, and extracts a set of feature parameters that characterize the tool-wear conditions. In order to realize real-time and robust tool-wear monitoring for different cutting conditions, a sensor-integration strategy that combines the information obtained from multiple sensors (acoustic emission sensor and power sensor) with machining parameters is proposed. A neural network based on an improved backpropagation algorithm is developed, and a prototype scheme for the real-time identification of tool-wear is implemented. Experiments under different conditions have proved that a higher rate of tool-wear identification can be achieved by using the sensor integration model with a neural network. The results also indicate that neural networks provide a very effective method of implementing sensor integration for the on-line monitoring of tool abnormalities.     

Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.     

Improving error compensation via a fuzzy-neural hybrid model

Factors that influence the accuracy of machining and in-cycle measuring processes are varied. It is very difficult or impossible to identify and fix each error by in-cycle measuring systems with touch trigger probes. Moreover, even where errors have been determined, the effects and relationships among them are very complicated, and there are no existing mathematical models to be applied to control or compensate the machining processes. This paper introduces a new in-cycle measuring and error compensation system based on a fuzzy controller combined with a supervised neural network. The fuzzy neural hybrid compensation model consists of a multilayer feed-forward neural network trained with the back propagation gradient descent algorithm. The fuzzy rules are implemented by the hidden layer of the network, and the fuzzy max-min operations are replaced by the feed-forward summation. The proposed system reveals that it is feasible to achieve an improved machining performance by adapting the fuzzy membership functions and generating linguistic control rules. A series of experiments is performed, and the characteristics of the system are evaluated and discussed.     

基于小波神经网络的加工过程自适应控制

把信息熵、小波分析和神经网络相结合,提出了基于小波神经网络的加工过程自适应控制系统及其自适应控制算法。提出并定义了广义熵方误差函数,在理论上证明了广义熵方误差函数的有效性。用广义熵方误差函数准则取代BP算法的均方误差准则,用自适应地搜索小波基函数和自适应地调整小波的尺度参数、平移参数和神经网络权值的方法对参数变化的切削力进行在线控制。仿真结果表明,该系统响应快,无超调,比传统的加工过程神经网络自适应控制具有更好的控制效果。     

加工面点云数据深度学习的加工特征自动识别

加工特征识别是实现CAD/CAPP/CAM系统集成的关键技术.针对传统基于符号推理加工特征识别模式存在鲁棒性问题,提出一种基于加工面点云数据深度学习的加工特征自动识别方法;基于PointNet点云识别框架,构建了一个面向加工面点云数据的加工特征自动识别卷积神经网络;通过收集CAD模型中的加工特征面集和采样点云,构建了适...     

BP神经网络合理隐结点数确定的改进方法

合理选择隐含层结点个数是BP神经网络构造中的关键问题,对网络的适应能力、学习速率都有重要的影响.在此提出一种确定隐结点个数的改进方法.该方法基于隐含层神经元输出之间的线性相关关系与线性无关关系,对神经网络隐结点个数进行削减,缩减网络规模.以零件工艺过程中的加工参数作为BP神经网络的输入,加工完成的零件尺寸作为BP神经网络的输出建立模型,把该方法应用于此神经网络模型中,其训练结果证明了该方法的有效性.     

Neural networks for estimating the tool path length in concurrent engineering applications

This paper deals with the development of a neural computing system that can predict the cutting tool path length for milling an arbitrary pocket defined within the domain of a product design, in a computer numerically controlled (CNC) setting. Existing computer aided design and manufacturing systems (CAD/CAM) consume significant amounts of time in terms of data entry pertaining to the geometries and subsequent modifications to them. In the concurrent engineering environment, where even the designer needs information from the CAD/CAM systems, such time-consuming processes can be expensive. To alleviate this problem, a neural network system can be used to estimate machining time by predicting cost-dependent variables such as tool path length for the pocket milling operation. Pockets are characterized and classified into various groups. A randomized design is described so that the training samples that have been chosen represent the domain evenly. An appropriate network was built and trained with the sample pocket geometries. The analysis of the performance of the system in terms of tool path length prediction for new pocket geometries is presented.     

Surface roughness prediction in machining of cast polyamide using neural network

This paper is about predicting the surface roughness by means of neural network approach method on machining of an engineering plastic material. The work material was an extruded PA6G cast polyamide for the machining tests. The network has 2 inputs called spindle speed and feed rate for this study. Output of the network is surface roughness (Ra). Gradient Descent Method was applied to optimize the weight parameters of neuron connections. The minimum Ra is obtained for 400 rpm and 251 cm/min as 0.8371 μm.     

LabVIEW中人工神经网络计算的实现与应用

介绍了利用MATLABScript节点和调用动态链接库(dll)在LabVIEW中进行人工神经网络计算的方法,通过动量BP算法的实例说明了各自的实现流程,并比较了两者的优缺点和应用范围。利用dll调用在基于LabVIEW的加工状态网络监测系统中实现了金刚笔钝化状态的自动识别,取得了良好的效果。     

Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools

Decision-making process in manufacturing environment is increasingly difficult due to the rapid changes in design and demand of quality products. To make decision making process (selection of machining parameters) online, effective and efficient artificial intelligent tools like neural networks are being attempted. This paper proposes the development of neural network models for prediction of machining parameters in CNC turning process. Experiments are designed based on Taguchi's Design of Experiments (DoE) and conducted with cutting speed, feed rate, depth of cut and nose radius as the process parameters and surface roughness and power consumption as objectives. Results from experiments are used to train the developed neuro based hybrid models. Among the developed models, performance of neural network model trained with particle swarm optimization model is superior in terms of computational speed and accuracy. Developed models are validated and reported. Signal-to-noise (S/N) ratios of responses are calculated to identify the influences of process parameters using analysis of variance (ANOVA) analysis. The developed model can be used in automotive industries for deciding the machining parameters to attain quality with minimum power consumption and hence maximum productivity.     

An interactive multi-objective artificial neural network approach for machine setup optimization

In this paper, we develop an artificial neural network method for machine setup problems. We show that our new approach solves a very challenging problem in the area of machining i.e. machine setup. A review of machine setup concepts and methods, along with feedforward artificial neural network is presented. We define the problem of machine setup to assessing the values of machine speed, feed and depth of cut (process inputs) for a particular objective such as minimize cost, maximize productivity or maximize surface finish. We use cutting temperature, cutting force, tool life, and surface roughness (process outputs) rather than objective functions to communicate with the decision maker. We show the relationship between process inputs to process outputs. This relationship is used in determining machine setup parameters (speed, feed, and depth of cut). Back propagation neural network is used as a decision support tool. The network maps, the forward relationship, and backward relationship between process inputs and process outputs. This mapping facilitates an interactive session with the decision maker. The process input is appropriately selected. Our method has the advantage of forecasting machine setup parameters with very little resource requirement in terms of time, machine tool, and people. Forecast time is almost instantaneous. Accuracy of the forecast depends on training and a well determined training sample provides very high accuracy. Trained network replaces the knowledge of an experienced worker, hence labor cost can be potentially reduced.     

Simulation of a complex optical polishing process using a neural network

 Most modern manufacturing processes change their set of parameters during machining in order to work at the optimum state. But in some cases, like computer-controlled polishing, it is not possible to change these parameters during the machining. Then usually a standard set of parameters is chosen which is not adjusted to the specific conditions. To gather the optimum set of parameters anyway simulation of the process prior to manufacturing is a possibility. This research illustrates the successful implementation of a neural network to accomplish such a simulation. The characteristic of this neural network is described along with the decision of the used inputs and outputs. Results are shown and the further usage of the neural network within an automation framework is discussed. The ability to simulate these advanced manufacturing processes is an important contribution to extend automation further and thus increase cost effectiveness.     

基于BP神经网络的预测建模系统的研究与实现

神经网络具有良好的记忆、归纳和学习能力，对难以用数学方法建立精确模型的信息、工艺等能够进行有效地预测建模。该文通过对BP神经网络的分析和研究，针对传统BP算法的不足，采用Levenberg—Marquardt(LM)优化算法的建立一个基于BP神经网络预测建模系统。在介绍了系统的主要功能之后，给出了用MATLAB软件实现该系统主要模块的具体程序。最后采用该系统对一个制造过程中刀具磨损量的进行了预测建模，实验仿真结果表明：系统具有良好的预测效果，刀具实际磨损量与预测磨损量的误差基本上在10％以下。     

Recognizing control chart patterns with neural network and numerical fitting

Control chart has been widely used to determine whether the state of machining process is stable or not, and pattern recognition technology is often used to automatically judge the changing modes of control chart. It is because that the abnormal patterns of a control chart can reveal the potential problem of machining quality. In order to improve the recognition rate and efficiency of control chart patterns, a neural network-numerical fitting (NN-NF) model is proposed to recognize different control chart patterns. A back propagation (BP) network is first used to recognize control chart patterns preliminarily. And then, numerical fitting method is adopted to estimate the parameters and specific types of the patterns, which is different from the traditional neural network-based control chart pattern recognition methods. Based on this, Monte Carlo simulation is used to generate training and testing data samples. The results of simulated experiment show that training time of this NN-NF model can be reduced. At the same time, the recognition rate can also be improved. At last, a real example is also provided to illustrate the NN-NF model.     

An approach to in-process surface texture condition monitoring

Surface textures formed in the machining process have a great influence on parts’ mechanical behaviours. Normally, the surface textures are inspected by using the images of the machined and cleaned parts. In this paper, an in-process surface texture condition monitoring approach is proposed. Based on the grey-level co-occurrence matrices, some surface texture image features are extracted to describe the texture characteristics. On the basis of the empirical model decomposition, some sensitive features are also extracted from the vibration signal. The mapping relationship from texture characteristics to texture image features and vibration signal features is found. A back propagation neural network model is built when the signal features and the texture conditions are respectively inputs and outputs. The particle swarm optimization is used to optimise the weights and thresholds of the neural network. Experimental study verifies the approach's effectiveness in monitoring the surface texture conditions during the machining process. The approach's accuracy and robustness are also verified. Then, the surface texture condition can be monitored efficiently during the machining process.