车削过程切削力的计算机数值仿真

切削力是表征切削过程最重要特征的物理量,其动态变化将直接影响加工过程中刀具与工件的相对位移、刀具磨损和表面加工质量等,所以对切削力建模是进行加工过程物理仿真研究的基础。因此在基于实时工况的切削实验研究基础上,考虑切削参数的因素,利用BP（back pmpagation）神经网络建立车削过程中的切削力的仿真模型。通过大量的样本训练,使神经网络能够对切削力进行较准确地数值仿真。     

Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling

Hard turning with ceramic tools provides an alternative to grinding operation in machining high precision and hardened components. But, the main concerns are the cost of expensive tool materials and the effect of the process on machinability. The poor selection of cutting conditions may lead to excessive tool wear and increased surface roughness of workpiece. Hence, there is a need to investigate the effects of process parameters on machinability characteristics in hard turning. In this work, the influence of cutting speed, feed rate, and machining time on machinability aspects such as specific cutting force, surface roughness, and tool wear in AISI D2 cold work tool steel hard turning with three different ceramic inserts, namely, CC650, CC650WG, and GC6050WH has been studied. A multilayer feed-forward artificial neural network (ANN), trained using error back-propagation training algorithm has been employed for predicting the machinability. The input?Coutput patterns required for ANN training and testing are obtained from the turning experiments planned through full factorial design. The simulation results demonstrate the effectiveness of ANN models to analyze the effects of cutting conditions as well as to study the performance of conventional and wiper ceramic inserts on machinability.     

ADAPTIVE LEARNING CONTROL OF CUTTING PARAMETERS FOR SCULPTURED SURFACE CUTTING BASED ON GENETIC ALGORITHMS AND NEURAL NETWORK

An adaptive learning control scheme intended to the on-line optimization of sculptured surface cutting process is presented. The scheme uses a back-propagation neural network to learn the relationships between process inputs and process states. The cutting parameters of the process model are optimized through a genetic algorithms(GA). The capacity of the proposed scheme for determining optimum process inputs under a variety of process conditions and optimization strategies is evaluated on the basis of milling of a sculptured surface using a ball-end mill. The experimental results show that the neural network could model the cutting process efficiently, and the cutting conditions such as spindle speed could be regulated for achieving high efficiency and high quality. Therefore the proposed approach can be well applied to the manufacturing of dies and molds.     

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.     

利用神经网络控制技术消除车削过程 自激振荡的仿真研究

车削过程自激振荡是一种以残留振痕作为机械延时反馈造成的动态失稳现象，消除这一现象是机械加工过程中的技术关键之一。将人工神经网络理论引入非线性或不稳定系统行为的控制，即可形成一种基于神经网络控制技术的消除车削过程自激振荡的新方法。仿真结果表明，该方法在消除残留振痕引起的车削再生颤振，提高车削稳定性方面具有特殊的作用。     

PCD刀具车削超硬铝合金的切削性能研究

以Al7075-T6为加工对象,通过车削试验对PCD刀具车削超硬铝合金的三向动态切削力和表面粗糙度展开研究,建立基于BP神经网络的切削力和表面粗糙度预测模型。结果表明:随着切削用量三要素的变化,切削力变化显著;对于表面粗糙度而言,背吃刀量、进给量和切削速度之间无交互作用;基于L-M优化算法的BP神经网络对样本的拟合度高,且对切削力和表面粗糙度的预测精度高。     

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     

Real-Time Prediction of Workpiece Errors for a CNC Turning Centre, Part 2. Modelling and Estimation of Thermally Induced Errors

Real-time modelling and estimation of thermally induced error have very important contributions for precision machining. In this paper, a novel radial basis function (RBF) neural network, which combines a regression tree and a radial basis function, has been considered and selected to model the thermally induced errors of CNC turning centres. Then, a simple and low-cost compact measurement system is applied to measure the time variant thermal errors of CNC turning centres. The thermally induced errors are estimated in real-time using the trained RBF neural network. The application of the measurement system and the novel RBF neural network are described in detail in this paper. The proposed approach is verified through some tests under different cutting conditions.     

Real-Time Prediction of Workpiece Errors for a CNC Turning Centre, Part 4. Cutting-Force-Induced Errors

A neural network method is presented for predicting cutting-force-induced errors in real-time during turning operations based on the estimated cutting forces. Workpiece errors can be considerably affected by the deflections of the machine–workpiece–tool system. A model of the elastic deflections of the machine–workpiece–tool system due to the cutting force in turning developed. A novel radial basis function (RBF) neural network is used to map the relationship between the cutting-force components (radial, axial and tangential) and the consequent dimensional deviation of the finished parts caused by the combined deflections of the machine–workpiece–tool system. Cutting tests were performed on a two-axis CNC turning centre and the experimental results showed that the prediction accuracy of the maximum diameter error of the workpiece was within 15%. The trained RBF neural network was able to predict the cutting force induced error in real-time during turning.     

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。     

硬车削在滚动轴承套圈加工中的应用

分析硬车削的特点及加工条件,详细介绍了采用硬车削加工滚动轴承套圈时的机床设备、装卡夹具、刀具选择等参数的确定,采用正交试验法对试验中的切削速度、进给量和切削深度3参数的影响水平进行了分析,得出了以硬车削加工代替磨削加工的可行性。     

Mathematical model of micro turning process

In recent years, significant advances in turning process have been achieved greatly due to the emergent technologies for precision machining. Turning operations are common in the automotive and aerospace industries where large metal workpieces are reduced to a fraction of their original weight when creating complex thin structures. The analysis of forces plays an important role in characterizing the cutting process, as the tool wear and surface texture, depending on the forces. In this paper, the objective is to show how our understanding of the micro turning process can be utilized to predict turning behavior such as the real feed rate and the real cutting depth, as well as the cutting and feed forces. The machine cutting processes are studied with a different model compared to that recently introduced for grinding process by Malkin and Guo (2006). The developed two-degrees-of-freedom model includes the effects of the process kinematics and tool edge serration. In this model, the input feed is changing because of current forces during the turning process, and the feed rate will be reduced by elastic deflection of the work tool in the opposite direction to the feed. Besides this, using the forces and material removal during turning, we calculate the effective cross-sectional area of cut to model material removal. With this model, it is possible for a machine operator, using the aforementioned turning process parameters, to obtain a cutting model at very small depths of cut. Finally, the simulated and experimental results prove that the developed mathematical model predicts the real position of the tool tip and the cutting and feed forces of the micro turning process accurately enough for design and implementation of a cutting strategy for a real task.     

基于神经网络的细长轴车削加工尺寸误差预测研究

为优化细长轴车削加工,应用人工神经网络方法建立使用跟刀架车削细长轴时的加工尺寸误差预测模型,并基于获得的预测模型研究切削用量对尺寸误差的影响。试验结果表明,该模型具有良好的预测精度,为细长轴车削加工切削用量的选择提供了依据。     

FUZZY CLUSTERING MODELLING FOR SURFACE FINISH PREDICTION IN FINE TURNING PROCESS

ABSTRACT

In this paper, fuzzy subtractive clustering based system identification and Sugeno type fuzzy inference system are used to model the surface finish of the machined surfaces in fine turning process to develop a better understanding of the effect of process parameters on surface quality. Such an understanding can provide insight into the problems of controlling the quality of the machined surface when the process parameters are adjusted to obtain certain characteristics. Surface finish data were generated for aluminum alloy 390 (73 BHN), ductile cast iron (186 BHN), and inconel 718 (BHN 335) for a wide range of machining conditions defined by cutting speed, cutting feed rate and cutting tool nose radius. These data were used to develop a surface finish prediction fuzzy clustering model as a function of hardness of the machined material, cutting speed, cutting feed rate, and cutting tool nose radius. Surface finish of the machined part is the output of the process. The model building process is carried out by using fuzzy subtracting clustering based system identification in both input and output space. Minimum error is obtained through numerous searches of clustering parameters. The fuzzy logic model is capable of predicting the surface finish for a given set of inputs (workpiece hardness, cutting speed, cutting feed rate and nose radius of the cutting tool). As such, the machinist may predict the quality of the surface for a given set of working parameters and may also set the process parameters to achieve a certain surface finish. The model is verified experimentally by further experimentation using different sets of inputs. This study deals with the experimental results obtained during fine turning operation. The findings indicate that while the effects of cutting feed and tool nose radius on surface finish were generally consistent for all materials, the effect of cutting speed was not. The surface finish improved for aluminum alloy and ductile cast iron but it deteriorated with speed for inconel.     

FUZZY CLUSTERING MODELLING FOR SURFACE FINISH PREDICTION IN FINE TURNING PROCESS

In this paper, fuzzy subtractive clustering based system identification and Sugeno type fuzzy inference system are used to model the surface finish of the machined surfaces in fine turning process to develop a better understanding of the effect of process parameters on surface quality. Such an understanding can provide insight into the problems of controlling the quality of the machined surface when the process parameters are adjusted to obtain certain characteristics. Surface finish data were generated for aluminum alloy 390 (73 BHN), ductile cast iron (186 BHN), and inconel 718 (BHN 335) for a wide range of machining conditions defined by cutting speed, cutting feed rate and cutting tool nose radius. These data were used to develop a surface finish prediction fuzzy clustering model as a function of hardness of the machined material, cutting speed, cutting feed rate, and cutting tool nose radius. Surface finish of the machined part is the output of the process. The model building process is carried out by using fuzzy subtracting clustering based system identification in both input and output space. Minimum error is obtained through numerous searches of clustering parameters. The fuzzy logic model is capable of predicting the surface finish for a given set of inputs (workpiece hardness, cutting speed, cutting feed rate and nose radius of the cutting tool). As such, the machinist may predict the quality of the surface for a given set of working parameters and may also set the process parameters to achieve a certain surface finish. The model is verified experimentally by further experimentation using different sets of inputs. This study deals with the experimental results obtained during fine turning operation. The findings indicate that while the effects of cutting feed and tool nose radius on surface finish were generally consistent for all materials, the effect of cutting speed was not. The surface finish improved for aluminum alloy and ductile cast iron but it deteriorated with speed for inconel.     

Prediction and analysis of surface roughness characteristics of a non-ferrous material using ANN in CNC turning

Surface roughness, an indicator of surface quality is one of the most-specified customer requirements in a machining process. For efficient use of machine tools, optimum cutting parameters (speed, feed, and depth of cut) are required. So it is necessary to find a suitable optimization method which can find optimum values of cutting parameters for minimizing surface roughness. The turning process parameter optimization is highly constrained and non-linear. In this work, machining process has been carried out on brass C26000 material in dry cutting condition in a CNC turning machine and surface roughness has been measured using surface roughness tester. To predict the surface roughness, an artificial neural network (ANN) model has been designed through feed-forward back-propagation network using Matlab (2009a) software for the data obtained. Comparison of the experimental data and ANN results show that there is no significant difference and ANN has been used confidently. The results obtained conclude that ANN is reliable and accurate for predicting the values. The actual R _a value has been obtained as 1.1999???m and the corresponding predicted surface roughness value is 1.1859???m, which implies greater accuracy.     

基于进化神经网络的电火花铣削加工电极损耗预测

针对电火花铣削加工的时变非线性特性,提出基于神经网络的电火花铣削加工电极损耗预测模型,利用该网络预测加工速度和工具的相对损耗,从而可在加工中实时计算出工具实际损耗量,为实现电极损耗的在线动态补偿打下基础。针对神经网络传统训练算法-BP算法的不足,提出了一种自适应调节变异率和变异量的进化算法来优化网络权值和网络结构,提高了网络的逼近精度和进化速度。     

Prediction of optimal stability states in inward-turning operation using neurogenetic algorithms

This paper proposes a neurogenetic-based optimization scheme for predicting localized stable cutting parameters in inward turning operation. A set of cutting experiments are performed in inward orthogonal turning operation. The cutting forces, surface roughness, and critical chatter locations are predicted as a function of operating variables including tool overhang length. Radial basis function neural network is employed to develop the generalization models. Optimum cutting parameters are predicted from the model using binary-coded genetic algorithms. Results are illustrated with the data corresponding to four work materials, i.e., EN8 steel, EN24 steel, mild steel, and aluminum operated over a high speed steel tool.     

Construction of a surface roughness prediction model for high speed machining

In manufacturing environment prediction of surface roughness is very important for product quality and production time. For this purpose, the finite element method and neural network is coupled to construct a surface roughness prediction model for high-speed machining. A finite element method based code is utilized to simulate the high-speed machining in which the cutting tool is incrementally advanced forward step by step during the cutting processes under various conditions of tool geometries (rake angle, edge radius) and cutting parameters (yielding strength, cutting speed, feed rate). The influences of the above cutting conditions on surface roughness variations are thus investigated. Moreover, the abductive neural networks are applied to synthesize the data sets obtained from the numerical calculations. Consequently, a quantitative prediction model is established for the relationship between the cutting variables and surface roughness in the process of high-speed machining. The surface roughness obtained from the calculations is compared with the experimental results conducted in the laboratory and with other research studies. Their agreements are quite well and the accuracy of the developed methodology may be verified accordingly. The simulation results also show that feed rate is the most important cutting variable dominating the surface roughness state.     

Prediction model and simulation of cutting force in turning hard-brittle materials

The removal mechanism of hard-brittle material was studied in this paper. The shear strain and specific shear work of brittle material cutting were analyzed. The cutting force model of hard-brittle material was developed based on the fracture mechanics. Johnson-Cook model was modified and applied to finite element simulation of hard-brittle material cutting. The cutting force of machinable ceramics was predicted by BP neural network. The turning experiments of machinable ceramics were carried out. The influence of processing parameters on cutting force was investigated. The results show that the modified constitutive model well reflects the fracture removal process of brittle material. The simulation results are in well agreement with experimental data and theoretical data. The effects of cutting depth and feed speed on cutting force are larger than those of cutting speed and tool cutting edge angle.