数控机床切削用量的模糊控制

阐述了在数控机床上使用模糊控制理论,对切削用量做出合理选择并使其达到最优值的一种方法。首先,利用模糊控制原理,结合数控机床实际,将切削力、毛坯材料、主轴转速、切削温度作为输入,进给速度、背吃刀量作为输出,设计出相应的模糊控制推理框图,展示推理过程;其次,依据推理框图和数控机床输入、输出条件,制订模糊推理规则,提出输入量检测的方法,推导出输入条件和输出条件的隶属度函数并以图形展示,定义输入、输出条件的模糊量子集;最后,根据输入、输出条件和模糊推理规则,构造出模糊控制规则表,实现了采用人类语言、模拟人类思维的控制方式。     

Application of particle swarm optimisation in artificial neural network for the prediction of tool life

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operations. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of a back-propagation algorithm and it is proven that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method .     

Application of particle swarm optimisation in artificial neural network for the prediction of tool life

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operation. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of the back-propagation algorithm and it is proved that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method.     

模具钢铣削中刀具磨损的试验研究

以面铣刀刀片磨损为研究对象,结合类神经网络系统建构高速数控铣削加工的预测模型。以加工参数为模型输入条件,刀腹磨耗为输出条件。采用多因素试验方法,选择切削速度、进给速度、切削深度三个试验参数,利用直交表式的试验计划法设计试验点。依照试验点铣削工件后再测量刀具加工后的刀腹磨耗量,进而求得倒传递网络所需的36组训练范例与11组验证数据。刀腹磨耗预测模式是利用类神经网络中的倒传递网络原理,以田口法求得倒传递网络参数的最优值。试验结果显示,刀腹磨耗随着切削速度、进给速度、切削深度增加而上升。铣削模具钢后,刀具磨耗预测值的平均误差为4.72%,最大误差为11.43%,最小误差为0.31%。整体而言,类神经网络对于铣削加工可进行有效预测。     

Multi-output fuzzy inference system for modeling cutting temperature and tool life in face milling

This paper proposes a method for cutting parameters identification using the multi-inputs-multi-outputs fuzzy inference system (MIMO-FIS). The fuzzy inference system (FIS) was used to identify the initial values for cutting parameters (cutting speed, feed rate and depth of cut) and flank wear using cutting temperature and tool life as outputs. The objective was to determine the influence of cutting parameters on cutting temperature and tool life. The model for determining the cutting temperature and tool life of steel AISI 1060 was trained (design rules) and tested by using the experimental data. The average deviation of the testing data for tool life was 11.6 %, while that of the cutting temperature was 3.28 %. The parameters used in these testing data were different from the data collected for the design rules. The test results showed that the proposed MIMO-FIS model can be used successfully for machinability data selection. The effect of parameters and their interactions in machining is analyzed in detail and presented in this study.     

A step towards the in-process monitoring for electrochemical microdrilling

The bandsawing as a multi-point cutting operation is the preferred method for cutting off raw materials in industry. Although cutting off with bandsaw is very old process, research efforts are very limited compared to the other cutting process. Appropriate online tool condition monitoring system is essential for sophisticated and automated machine tools to achieve better tool management. Tool wear monitoring models using artificial neural network are developed to predict the tool wear during cutting off the raw materials (American Iron and Steel Institute 1020, 1040 and 4140) by bandsaw. Based on a continuous data acquisition of cutting force signals, it is possible to estimate or to classify certain wear parameters by means of neural networks thanks to reasonably quick data-processing capability. The multi-layered feed forward artificial neural network (ANN) system of a 6?×?9?×?1 structure based on cutting forces was trained using error back-propagation training algorithm to estimate tool wear in bandsawing. The data used for the training and checking of the network were derived from the experiments according to the principles of Taguchi design of experiments planned as L ₂₇. The factors considered as input in the experiment were the feed rate, the cutting speed, the engagement length and material hardness. 3D surface plots are generated using ANN model to study the interaction effects of cutting conditions on sawblade. The analysis shows that cutting length, hardness and cutting speed have significant effect on tooth wear, respectively, while feed rate has less effect. In this study, the details of experimentation and ANN application to predict tooth wear have been presented. The system shows that there is close match between the flank wear estimated and measured directly.     

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.     

基于进化神经网络的刀具寿命预测

为预测道具寿命,引入人工神经网络技术,建立了刀具寿命预测神经网络模型,同时对切削参数进行优化选择.在刀具寿命预测中,针对反向传播算法存在收敛速度慢、容易陷入局部极小值及全局搜索能力弱等缺陷,采用遗传算法训练反向传播神经网络,设计了进化神经网络的学习算法.实验和仿真结果表明:基于进化计算的反向传播神经网络可以克服单纯使用反向传播网络易陷入局部极小值等难题,刀具寿命的预测精度较高,从而为刀具需求计划制定、刀具成本核算,以及切削参数制定提供理论依据,节约了制造执行系统中的生产成本.     

Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.     

Monitoring in precision metal drilling process using multi-sensors and neural network

This paper presents a new method to estimate hole diameters and surface roughness in precision drilling processes, using coupons taken from a sandwich plate composed of a titanium alloy plate (Ti6Al4V) glued onto an aluminum alloy plate (AA 2024T3). The proposed method uses signals acquired during the cutting process by a multisensor system installed on the machine tool. These signals are mathematically treated and then used as input for an artificial neural network. After training, the neural network system is qualified to estimate the surface roughness and hole diameter based on the signals and cutting process parameters. To evaluate the system, the estimated data were compared with experimental measurements and the errors were calculated. The results proved the efficiency of the proposed method, which yielded very low or even negligible errors of the tolerances used in most industrial drilling processes. This pioneering method opens up a new field of research, showing a promising potential for development and application as an alternative monitoring method for drilling processes.     

Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.     

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.     

A new model of the antifriction effect on wiredrawing with ultrasound

In this paper, two different evolutionary algorithm-based neural network models were developed to optimise the unit production cost. The hybrid neural network models are, namely, genetic algorithm-based neural network (GA-NN) model and particle swarm optimization-based neural network (PSO-NN) model. These hybrid neural network models were used to find the optimal cutting conditions of Ti[C,N] mixed alumina-based ceramic cutting tool (CC650) and SiC whisker-reinforced alumina-based ceramic cutting tool (CC670) on machining glass fibre-reinforced plastic (GFRP) composite. The objective considered was the minimization of unit production cost subjected to various machine constraints. An orthogonal design and analysis of variance was employed to determine the effective cutting parameters on the tool life. Neural network helps obtain a fairly accurate prediction, even when enough and adequate information is not available. The GA-NN and PSO-NN models were compared for their performance. Optimal cutting conditions obtained with the PSO-NN model are the best possible compromise compared with the GA-NN model during machining GFRP composite using alumina cutting tool. This model also proved that neural networks are capable of reducing uncertainties related to the optimization and estimation of unit production cost.     

SiC_p/Al复合材料高速铣削工艺过程参量预测模糊专家系统研究

使用聚晶金刚石(PCD)刀具在600-1200m/min切削速度范围内对SiCp/2009Al复合材料进行高速铣削试验。对刀具耐用度、表面粗糙度、切削力、切削温度等工艺参量进行了测量。运用VC++及WXCLIPS软件开发了一套具有自学习功能的模糊专家系统,对SiCP/2009Al复合材料高速铣削加工中的上述工艺参量进行预测。经验证,预测结果与试验结果有很好的一致性。     

刀具寿命诊断的神经网络技术

提出了一种刀具寿命的检测方法。该方法把模糊逻辑和神经网络结合起来，并用神经网络分解技术，建立了一上刀具状态识别网络。该网络适于进行多传感器刀具复杂状态的识别和分类，具有训练时间短，扫行速度快，可靠性高，抗噪能力强的特点。     

冲天炉网形图重构的自适应模糊推理方法

网形图是指导冲天炉熔炼操作的重要工具。综合自适应模糊推理的建模功能和神经网络的学习能力，直接从实验数据中提取推理规则，建立了基于网形图的冲天炉熔炼过程模型。模型具有较高的预测精度和泛化能力，可以帮助操作者认识熔炼规律，据此得出的新型网形图使用起来更为方便快捷。     

基于模糊小波神经网络的加工过程自适应控制

为了克服模糊控制动态响应慢和鲁棒性差的缺点,将模糊控制的定性知识表达能力与小波分析优异的局部控制性能和神经网络的定量学习能力相结合,提出了一种模糊小波神经网络自适应控制器,并将其应用于加工过程控制。对变切削深度的铣削加工过程控制的仿真结果表明,基于模糊小波神经网络的加工过程自适应控制,其控制效果优于一般的模糊控制和神经网络控制,具有很好的动、静态性能。该自适应控制器能有效防止刀具损坏和提高加工效率,是一种有效的加工过程控制方法。     

Development of a rough set-based fuzzy neural network for online monitoring of microdrilling

A rough set-based fuzzy neural network has been developed in this study for online monitoring of microdrilling. By applying the rough set theory, we obtained reduced rule sets from data samples as the fuzzy neural network rules. Then the neural network model was designed based on the reduced rule sets. The number of the network fuzzy rules was reduced. Shortcomings in high-dimensional fuzzy neural network, such as huge structure, were overcome. Data that were sampled in real-time from the spindle motor three-phase currents were processed by the trained network to monitor the microdrill wear online. The experiment results show that if the threshold is properly selected, the microdrill breakage will be effectively prevented by the monitoring.     

基于B样条模糊神经网络的刀具磨损监测

刀具状态监测是实现自动化加工和无人化加工的关键技术。本文使用切削力和声发射传感器监测金属切削过程,提出了基于B样条模糊神经网络作为刀具磨损量监测模型。该模型能够准确描述刀具磨损和信号特征之间的非线性关系,和常用的BP前馈神经网络相比,具有收敛速度快和局部学习能力等优点。试验结果表明:采用B样条模糊神经网络对提高刀具磨损在线监测的准确度和可靠度非常有效。     

线性回归模型诊断和在线预测刀具磨损量的方法研究

目的是研究诊断端面铣刀磨损量和在线预测铣刀的剩余寿命的方法.采用线性回归模型估计测刀面的磨损量.线性回归模型的输入是从铣刀受力信号提取出的特征和切削条件,比如进给量、转速等.在诊断了刀具的磨损量后,采用双指数平滑方法跟随诊断结果预测铣刀的使用寿命.最后,通过卖验验证了基于线性回归模型得到的刀具的磨损量和基于双指数平滑方法在线预测铣刀的剩余寿命的可行性.