基于小波包分析和Elman网络的切削表面粗糙度预测方法

提出了一种基于松散型小波网络的切削表面粗糙度预测方法。结合切削参数和切削振动理论,建立了预测网络结构,为避免频域混叠,采用小波包改进算法来实现振动信号去噪。根据振动加速度及切削参数,利用Elman网络的非线性映射和学习机制,实现切削表面粗糙度的实时在线预测。为减少训练时间,用遗传算法对网络权重进行预先优化。实验表明,该方法的预测误差小于3%。     

基于Web的数控加工切削参数优化系统的研究

对数控加工切削参数优化系统进行研究,引入灰色理论对数控切削参数进行优化,提出了基于Web的数控加工切削参数数据库及网络制造平台的构想,指出资源共享和协作将是推动制造企业发展的动力.     

硬切削表层残余应力特征预测研究

结合精密硬切削加工表层残余应力曲线的勺形分布特征,提出了以表面应力值、最大应力值、最大深度值和有效深度值4个变量作为表达残余应力曲线的特征参数,并作为BP网络的输出量,以刀具几何参数和切削参数等影响硬切削加工最为显著的变量为输入量,建立了硬切削加工表层残余应力特征BP网络预测模型。最后,通过实验验证了模型的准确性,实现了对精密硬切削加工表面残余应力较高精度的预测。     

基于工艺过程的切削数据库系统的研究与开发

针对切削加工中影响切削参数的因素十分复杂难以有效描述的问题,提出了基于工艺过程的切削参数信息描述方法,给出了切削参数信息模型与工艺过程信息模型之间的关联关系。构建了切削参数库与工艺案例库相融合的系统架构,并通过普通和数控2种不同的切削参数获取方法,实现了基于工艺过程的实际切削加工参数的获取和继承。本文最后介绍了已开发的基于工艺过程的切削参数数据库系统。     

基于数控铣削加工切削参数选择的探讨

介绍了数控铣削加工切削参数的内容,铣削加工切削参数的选择原则,提出了合理选择切削参数的方法和注意事项。     

切削参数智能选择系统的研究与开发

介绍了一套材料切削加工参数智能选择系统的研究与开发。该系统基于切削参数工程数据库的经验参数选择；采用模糊综合评判及模糊聚类方法对切削加工性能未知的材料进行切削参数的选择；采用机械最优化方法对特定加工目标的材料切削参数进行选择；采用人工神经网络方法对具有大量加工经验的材料切削参数进行选择。多种方法的结合有效地实现了切削参数的合理选择，并使切削参数的选择具有一定的智能水平。     

基于神经网络的高速铣削表面粗糙度预报

提出利用神经网络进行高速铣削表面粗糙度预报的方法,给出了具体的网络实现过程,应用灵敏度剪枝算法克服了网络隐层难以确定的问题,仿真结果表明该方法的有效性,对高速加工切削参数的选择和表面质量控制具有指导意义。     

高速切削工艺参数优化系统

使用正交试验法对粗糙度指标进行优化,得到了合理的切削参数,达到了提高零件加工表面质量和生产效率的目的,并且对切削工艺参数优化系统的设计思路和实现方法进行了较详细的叙述。通过本切削工艺参数优化系统运用正交试验等方法优化切削参数,从而建立尾翼零件切削参数数据库,为保证型号产品的加工质量提供技术支持。     

基于有限元法的二维正交切削刀具应力分析

运用有限元方法对二维正交切削加工刀具内部应力进行模拟分析。基于刚塑性有限元方法建立了切削加工过程仿真模型,通过模拟获得了切削加工过程中刀具应力的分布和变化情况。通过对不同切削工艺参数条件下的切削过程进行模拟,分析了刀具几何参数以及切削用量对切削加工过程中刀具应力的影响,为正确选择刀具及切削参数提供了参考。     

高速切削工艺参数优化模型研究及发展趋势

工艺参数优化是高速切削应用研究中一项重要的关键技术,由于高速切削的线速度急剧升高,高速切削工艺参数优化具有不同于普通切削的复杂性。文中总结了近年来国内外关于高速切削参数优化的最新研究成果,从优化模型和优化方法两个方面分析了参数优化研究中值得关注的问题,并进一步指出高速切削参数优化的未来发展趋势。     

Neural networks approach to the determination of the machining parameters

A neural networks based approach to determine the appropriate machining parameters such as speed, depth of cut and feed is proposed in this study. In this approach neural networks were used for building automatic process planning systems. Training of neural networks was performed with back propagation method by using data sets sampled in a standard handbook. These networks consist of simple processing, elements or nodes capable of processing information in response to external inputs. This approach saves computing time and storage space. In addition, it provides easy extendability as new data become available. Currently, the system provides three neural networks: for turning, for milling and for drilling operations. The performance of the trained neural network for drilling is evaluated to examine how well it predicts the machining parameters. Test results show that the neural network for the turning operation is able to predict the machining parameter values within an acceptable error rate.     

Application of artificial neural network and optimization algorithms for optimizing surface roughness, tool life and cutting forces in turning operation

Our goal is to propose a useful and effective method to determine optimal machining parameters in order to minimize surface roughness, resultant cutting forces and maximize tool life in the turning process. At first, three separate neural networks were used to estimate outputs of the process by varying input machining parameters. Then, these networks were used as optimization objective functions. Moreover, the proposed algorithm, namely, GA and PSO were utilized to optimize each of the outputs, while the other outputs would also be kept in the suitable range. The obtained results showed that by using trained neural networks with genetic algorithms as optimization objective functions, a powerful model would be obtained with high accuracy to analyze the effect of each parameter on the output(s) and optimally estimate machining conditions to reach minimum machining outputs.     

Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization

In this paper, we introduce a procedure to formulate and solve optimization problems for multiple and conflicting objectives that may exist in turning processes. Advanced turning processes, such as hard turning, demand the use of advanced tools with specially prepared cutting edges. It is also evident from a large number of experimental works that the tool geometry and selected machining parameters have complex relations with the tool life and the roughness and integrity of the finished surfaces. The non-linear relations between the machining parameters including tool geometry and the performance measure of interest can be obtained by neural networks using experimental data. The neural network models can be used in defining objective functions. In this study, dynamic-neighborhood particle swarm optimization (DN-PSO) methodology is used to handle multi-objective optimization problems existing in turning process planning. The objective is to obtain a group of optimal process parameters for each of three different case studies presented in this paper. The case studies considered in this study are: minimizing surface roughness values and maximizing the productivity, maximizing tool life and material removal rate, and minimizing machining induced stresses on the surface and minimizing surface roughness. The optimum cutting conditions for each case study can be selected from calculated Pareto-optimal fronts by the user according to production planning requirements. The results indicate that the proposed methodology which makes use of dynamic-neighborhood particle swarm approach for solving the multi-objective optimization problems with conflicting objectives is both effective and efficient, and can be utilized in solving complex turning optimization problems and adds intelligence in production planning process.     

Optimisation of turning parameters by integrating genetic algorithm with support vector regression and artificial neural networks

This paper focuses on optimisation of process parameters of the turning operation, using artificial intelligence techniques such as support vector regression (SVR) and artificial neural networks (ANN) integrated with genetic algorithm (GA). The model is trained using the turning parameters as the input and corresponding surface roughness, tool wear and power required as the output. Data, obtained from conducting experiments is analysed using support vector machine (SVM) and artificial neural network. SVM, a nonlinear model, is learned by linear learning machine by mapping into high-dimensional kernel-induced feature space. The genetic algorithm is integrated with these to find the optimum from the response surface generated. The results are compared with those obtained by integrating GA with traditional models like response surface methodology (RSM) and regression analysis (RA). This paper illustrates the impact that techniques based on artificial intelligence have on optimising processes.     

Pre-evaluation on surface profile in turning process based on cutting parameters

Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc.) for high accuracy. However, it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper, an approach for surface profile pre-evaluation (prediction) in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and feed rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy.     

实例推理中基于ART算法的动态记忆模型的研究

提出了一种基于神经网络的工艺设计实例推理索引模型，与现存大多数实例推理系统不同，该方法用神经网络实现实例的动态分类和索引。实例层次分类的３层结构，为实现基于符号处理的实例推理求解模式向基于神经计算的模式识别求解模式映射提供了条件。神经网络的自适应，自学习能力将减少系统的日常维护工作，该方法的优点在于实例的高速，有效检索，知识获取的简化等。     

Autonomous cutting parameter regulation using adaptive modeling and genetic algorithms

In this research, a turning process is modeled adaptively by a backpropagation, multilayered neural network with an iterative learning method, and cutting parameters of the process model are optimized through genetic algorithms (GAs). Some constraints were given on the input conditions and the process outputs to provide for the desired surface integrity and to protect the machine tool. Introducing penalty values, which are included in the fitness evaluation of the GAs, we can solve such a constrained problem. Experimental results show that the neural network has the ability to model the turning process on-line, and such cutting conditions as spindle speed and feed rate can be adaptively regulated for maximizing the material removal rate using the GAs.     

基于HMPSO和BP神经网络算法的中频淬火工艺参数预测

中频淬火通过人工控制和PID控制工艺参数难以保证产品质量,因此,基于微粒群优化算法和BP神经网络算法提出了一种基于HMPSO的BP神经网络工艺参数控制方法,首先介绍了微粒群优化算法和BP神经网络算法,接着建立了基于HMPSO的BP神经网络工艺参数控制方法,最后对中频淬火控制系统进行MATLAB仿真研究,结果表明,该方法稳定、有效,可提高中频淬火的质量。     

轴类零件耦合神经网络实例推理 CAPP 索引模型的研究

提出了一种基于神经网络的工艺设计实例推进索引模型。与现存大多数实例推理系统不同,该方法用神经网络实现实例的动态分类和索引。实例层次分类的三层结构和基于特征的聚类模板概念,为实现基于符号处理的实例推理求解模式向基于神经计算的模式识别求解模式映射提供了条件。该方法的优点在于实例的高速、有效检索,知识获取的简化以及基于神经网络的检索算法的鲁棒性。