粒子群优化人工神经网络在高速铣削力建模中的应用

将粒子群优化人工神经网络理论应用于高速铣削力的建模研究中.采用粒子群算法与反向传播算法相结合的方法,对反向传播神经网络模型进行优化.用粒子群算法训练网络参数,直到误差趋于一稳定值,然后用优化的权值进行反向传播算法运算,以实现高速铣削力的预测.充分发挥了粒子群算法的全局寻优能力和反向传播算法的局部搜索优势.仿真结果表明,与其他几种反向传播算法相比较,粒子群算法与反向传播算法的学习算法训练的神经网络,不仅训练时间明显缩短,而且其预报精度也得到了较大的提高,能够有效地建立铣削力模型,并对铣削力进行准确的预测.     

An artificial neural network approach for the control of the laser milling process

Laser milling (LM) can be classified as a layer manufacturing process in which the material is removed by a laser beam by means of the ablation mechanism. It is a laser machining process which uses a laser beam to produce 3D shapes into a wide variety of materials. It is also known as laser ablation. It shows clear advantages versus the traditional milling such as the unlimited choice of materials, the direct use of computer-aided design structure data, the high geometric flexibility, and the touchless tool. LM requires the selection of optimal machining parameters for the job. Unlike the mechanical milling and the mechanical incision, the depth of the single removed layer is chosen at the beginning as input parameter of the process. In LM, the ablated depth depends from the process parameters such as laser power, scan speed, pulse duration, and pulse frequency. This work aims to develop an algorithm that can predict the parameters necessary to execute the material removal with a preset ablation depth. Using the results of an experimental campaign, the laser milling process was modeled by means of a back-propagation artificial neural network. Then, an iterative algorithm, based on the previous trained neural network, permitted to calculate the scanning velocity and pulse frequency that approached for the best the preset ablation depth. The developed approach represents a mean for the rational selection of laser ablation process parameters. It can be performed in an intuitive manner since it uses simple artificial intelligence like the artificial neural network.     

模糊神经网络在UV-LIGA工艺优化中的应用

将模糊神经网络理论和算法应用于负性光刻胶(SU-8)加工高分辨率和高深宽比微结构的工艺研究,在正交试验的基础上对网络进行训练,建立了光刻图形质量与前烘时间、前烘温度、曝光量、后烘时间之间的预测模型。该模型采用五层前向模糊神经网络,学习算法为梯度下降法。进行了实验,实验结果表明,前烘温度与前烘时间对光刻质量影响最大。对120~340 μm厚的光刻胶,前烘温度取95℃,前烘时间100 min时,图形的相对线宽差最小;超声搅拌能缩短显影时间,显著改善图形质量,试验结果与计算结果十分吻合。将模糊神经网络应用于UV－LIGA工艺中,能实现光刻加工微结构的工艺参数优化。     

Adaptive modelling of the milling process and application of a neural network for tool wear monitoring

An adaptive signal processing scheme that uses a low-order autoregressive time series model is introduced to model the cutting force data for tool wear monitoring during face milling. The modelling scheme is implemented using an RLS (recursive least square) method to update the model parameters adaptively at each sampling instant. Experiments indicate that AR model parameters are good features for monitoring tool wear, thus tool wear can be detected by monitoring the evolution of the AR parameters during the milling process. The capability of tool wear monitoring is demonstrated with the application of a neural network. As a result, the neural network classifier combined with the suggested adaptive signal processing scheme is shown to be quite suitable for in-process tool wear monitoring     

应用BP神经网络预测高速铣削表面粗糙度

表面粗糙度的预测是切削加工质量分析的重要研究方向,为了在保证铣削的同时预测加工表面的粗糙度、提高生产率,将人工神经网络技术应用于铣削加工领域。应用BP神经网络建立高速铣削加工表面粗糙度预测模型,将预报结果与试验真值进行对比验证,结果表明该方法能够得到较好的预测精度,对高速铣削参数的选择和表面质量的控制具有指导意义。     

Analysis of the effect of a new process control agent technique on the mechanical milling process using a neural network model: Measurement and modeling

In this study, a new process control agent (PCA) technique called as gradual process control agent technique was developed and the new technique was compared with conventional process control agent technique. In addition, a neural network (ANN) approach was presented for the prediction of effect of gradual process control agent technique on the mechanical milling process. The structural evolution and morphology of powders were investigated using SEM and particle size analyzer techniques. The experimental results were used to train feed forward and back propagation learning algorithm with two hidden layers. The four input parameters in the proposed ANN were the milling time, the gradual PCA content, previous PCA content and gradual PCA content. The particle size was the output obtained from the proposed ANN. By comparing the predicted values with the experimental data it is demonstrated that the ANN is a useful, efficient and reliable method to determine the effect of gradual process control agent technique on the mechanical milling process.     

基于人工神经网络的铣削参数优化

探讨了金属切削加工的优化问题.并以铣削为例,建立最高生产率为目标的数学模型,通过人工神经网络的方法进行优化.通过实例表明,用人工神经网络优化方法可降低加工成本和提高劳动生产率.     

两种神经网络在注塑产品工艺参数确定中的应用

汽车外饰件的塑料化趋势对注塑模成型质量提出了更高要求.为解决传统CAE方法需多次试验才能得到较优工艺的缺点,以一汽车观后镜为研究对象,建立了基于人工神经网络的从注塑工艺参数到注塑翘曲量的非线性映射关系,并对比了两种经典的前馈神经网络(BP网络和RBF网络)的学习能力,从而实现用神经网络模型代替CAE软件获得注塑翘曲量.研究结果表明,该方法能有效地缩短优化工艺参数的时间,提高了工艺设计效率.     

A neural network solution for LOM process performance

A neural network (NN) modeling approach is presented for the prediction of laminated object manufacturing (LOM) process performance. A NN was developed using experimental data which were conducted on a LOM 1015 machine according to the principles of Taguchi design of experiments (DoE) method. The process parameters considered in the experiment to investigate LOM process performance were nominal layer thickness (NLT), heater temperature (HT), platform retract (PR), heater speed (HS), laser speed (LS), feeder speed (FS), and platform speed (PS). LOM process performance is divided in dimensional errors in X and Y directions (Ex and Ey), actual layer thickness (ALT), average surface roughness of vertical supporting frame (VSF-R_a), and tensile strength in X direction (TSx). It was found that NN approach can be applied in an easy way on designed experiments and predictions can be achieved, fast and quite accurate. The developed NN is constrained by the experimental region in which the designed experiment is conducted. Thus, it is very important to select parameters’ levels as well as the limits of the experimental region and the structure of the orthogonal experiment. The above analysis is useful for LOM users when prediction of process performance is needed. This methodology could be easily applied to different materials and initial conditions for optimization of other Rapid Prototyping (RP) processes.     

高速铣削淬硬钢的研究进展

对国内外高速铣削淬硬钢的研究成果进行评述.讨论高速切削的概念和特点、切削力、金属软化效应、涂层刀具加工淬硬钢的切削性能、切屑形成机理、冷却方式以及对加工表面的影响,并提出高速铣削淬硬钢研究中的热点问题.     

Specific cutting force coefficients modeling of end milling by neural network

In a high precision vertical machining center, the estimation of cutting forces is important for many reasons such as prediction of chatter vibration, surface roughness and so on. The cutting forces are difficult to predict because they are very complex and time variant. In order to predict the cutting forces of end-milling processes for various cutting conditions, their mathematical model is important and the model is based on chip load, cutting geometry, and the relationship between cutting forces and chip loads. Specific cutting force coefficients of the model have been obtained as interpolation function types by averaging forces of cutting tests. In this paper the coefficients are obtained by neural network and the results of the conventional method and those of the proposed method are compared. The results show that the neural network method gives more correct values than the function type and that in the learning stage as the omitted number of experimental data increase the average errors increase as well.     

人工神经网络在机械加工中的应用

介绍神经网络技术在机械加工领域的应用现状,包括人工神经网络在工艺规程编制中的应用、在加工参数优化中的应用及在工况监测及预报中的应用。并对这项技术的应用作了进一步展望。     

基于人工神经网络的电火花加工工艺建模方法研究

电火花加工是一个受多参数影响的复杂随机过程,很难建立一个适当的机理模型。用神经网络技术以铜加工SKD-Ⅱ为例建立了电火花加工工艺模型,并依据工艺样本中各参数数据的不同特点采用了不同的预处理方法,测试表明效果较好,所建模型能精确地预测出给定条件下的加工工艺参数,反映了该机床的加工工艺规律。另外,采用石墨加工SKD-Ⅱ工艺数据验证了该方法的通用性及有效性。     

BP神经网络技术在雨刮电动机故障诊断中的应用

在电动机故障诊断技术中,最能全面反映电动机运行状态的唯独有振动信号。因此,提出一种基于小波分析和BP神经网络的电动机故障诊断方法。首先该方法采用小波包分析对振动信号消噪滤波并计算频带能量,随后根据振动信号大小提取其能量特征值,并以此建立电动机故障诊断的BP神经网络模型,再以Matlab软件的仿真模块为平台,最终开发了雨刮电动机故障诊断的智能检测系统。试验表明该系统的建立能够提高雨刮电动机故障诊断的效率和准确性。     

Application of a Taguchi-based neural network prediction design of the film coating process for polymer blends

In this paper, an approach for developing the prediction model for polymer blends using a back-propagation neural network (BPNN) combined with the Taguchi quality method is presented in an attempt to improve the deficiencies in current neural networks associated with the design of network architecture, including the selection of one optimal set of learning parameters to accomplish faster convergence during training and the desired accuracy during the recall step. The objective of the prediction model is to explore the relationships between the control factor levels and surface roughness in the film coating process. In addition, the feasibility of adopting this approach is demonstrated in the study optimizing the learning parameters of the BPNN structure to forecast the target characteristics of the product or process with various control conditions in the manufacturing system.     

Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations

Surface roughness plays a key role in the performance of machined components??specially dies and moulds??manufactured for the aerospace and automotive industries, among others. However, roughness can only be measured off-line after the part has been machined, when cutting conditions may no longer be adjusted to surface roughness requirements. A reliable surface roughness prediction application is presented in this paper. It is based on ensemble learning for vertical high-speed milling operations with ball-end mills for finishing operations on quenched steel 1.2344 (AISI H13) that are widely used in the manufacture of moulds and dies. The new approach was validated with an experimental dataset that includes geometrical tool factors, cutting conditions, dynamic factors and lubricant type. An intensive comparison with an artificial neural network approach for the same dataset is included, to reveal the improvements of the new technique over other well-established ones for this industrial problem. This comparison shows that ensemble learning can by-pass the time-consuming task of tuning neural network parameters and can also improve prediction model accuracy, both of which are features that could lead to greater use of these kinds of prediction models in real workshops. Finally, a methodology, based on this new approach, is presented, in order to illustrate how the prediction model can be used in workshops to optimize cutting conditions in terms of their surface quality and productivity.     

免疫神经网络在故障诊断中的应用

利用生物免疫学的原理,将神经网络和免疫算法结合起来,形成免疫神经网络,并应用于电液伺服阀的故障诊断中.结果表明,免疫神经网络能够以较小的网络规模实现对多种故障模式的准确识别,具有高效率、容错性能好和强大的自适应能力.     

Optimization of milling parameters using artificial neural network and artificial immune system

The present paper is an attempt to predict the effective milling parameters on the final surface roughness of the work-piece made of Ti-6Al-4V using a multi-perceptron artificial neural network. The required data were collected during the experiments conducted on the mentioned material. These parameters include cutting speed, feed per tooth and depth of cut. A relatively newly discovered optimization algorithm entitled, artificial immune system is used to find the best cutting conditions resulting in minimum surface roughness. Finally, the process of validation of the optimum condition is presented.     

The selection of milling parameters by the PSO-based neural network modeling method

During the past decade, polymer nanocomposites have emerged relatively as a new and rapidly developing class of composite materials and attracted considerable investment in research and development worldwide. An increase in the desire for personalized products has led to the requirement of the direct machining of polymers for personalized products. In this work, the effect of cutting parameters (spindle speed and feed rate) and nanoclay (NC) content on machinability properties of polyamide-6/nanoclay (PA-6/NC) nanocomposites was studied by using high speed steel end mill. This paper also presents a novel approach for modeling cutting forces and surface roughness in milling PA-6/NC nanocomposite materials, by using particle swarm optimization-based neural network (PSONN) and the training capacity of PSONN is compared to that of the conventional neural network. In this regard, advantages of the statistical experimental algorithm technique, experimental measurements artificial neural network and particle swarm optimization algorithm, are exploited in an integrated manner. The results indicate that the nanoclay content on PA-6 significantly decreases the cutting forces, but does not have a considerable effect on surface roughness. Also the obtained results for modeling cutting forces and surface roughness have shown very good training capacity of the proposed PSONN algorithm in comparison to that of a conventional neural network.     

Detection process approach of tool wear in high speed milling

The cutting tool wear degrades the quality of the product in the manufacturing process, for this reason an on-line monitoring of the cutting tool wear level is very necessary to prevent any deterioration. Unfortunately there is no direct manner to measure the cutting tool wear on-line. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission, etc. The main objective of this work is to establish a relationship between the acquired signals variation and the tool wear in high speed milling process; so an experimental setup was carried out using a horizontal high speed milling machine. Thus, the cutting forces were measured by means of a dynamometer whereas; the tool wear was measured in an off-line manner using a binocular microscope. Furthermore, we analysed cutting force signatures during milling operation throughout the tool life. This analysis was based on both temporal and frequential signal processing techniques in order to extract the relevant indicators of cutting tool state. Our results have shown that the variation of the variance and the first harmonic amplitudes were linked to the flank wear evolution. These parameters show the best behavior of the tool wear state while providing relevant information of this later.