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.     

Milling force prediction using regression and neural networks

This study focuses on developing a good empirical relationship between the cutting force in an end milling operation and the cutting parameters such as speed, feed and depth-of-cut, by using both multiple regression and neural network modeling processes. A regression model was first fitted to experimentally collected data and any abnormal data points indicated by this analysis were filtered out. By repeating this process several times, a final set of filtered data was obtained and analyzed using neural networks to yield a good, final model. This study shows that analyzing milling force data using conventional regression can lead to a more accurate neural networks model for force prediction.     

Modeling and adaptive force control of milling by using artificial techniques

The contribution discusses the use of combining the methods of neural networks, fuzzy logic and PSO evolutionary strategy in modeling and adaptively controlling the process of ball-end milling. On the basis of the hybrid process modeling, off-line optimization and feed-forward neural control scheme (UNKS) the combined system for off-line optimization and adaptive adjustment of cutting parameters is built. This is an adaptive control system controlling the cutting force and maintaining constant roughness of the surface being milled by digital adaptation of cutting parameters. In this way it compensates all disturbances during the cutting process: tool wear, non-homogeneity of the workpiece material, vibrations, chatter, etc. The basic control principle is based on the control scheme (UNKS) consisting of two neural identifiers of the process dynamics and primary regulator. An overall procedure of hybrid modeling of cutting process used for creating the CNC milling simulator has been prepared. The experimental results show that not only does the milling system with the design controller have high robustness, and global stability, but also the machining efficiency of the milling system with the adaptive controller is 27% higher than for traditional CNC milling system.     

基于计算智能算法的铣刀状态监测

本文提出了基于智能融合技术进行铣刀磨损量监测和预测方法。利用多传感器对切削力和振动信号进行监测,通过频率变换提取切削力特征量,采用小波包分解技术提取振动信号特征量。通过信号特征值的组合,分别探讨了几种计算智能数据融合技术-小波神经网络、遗传神经网络、遗传小波神经网络对刀具磨损量的预测效果。实验分析表明,本文提出的几种计算智能数据融合技术均能够有效地完成刀具磨损量预测。     

Modeling and characterization of surface generation in fast tool servo machining of microlens arrays

A microlens array is composed of a series of microlens distributed in a regular pattern and has been used in a wide range of photonic products. Fast Tool Servo (FTS) machining is an enabling and efficient technology for fabricating high quality microlens arrays with submicrometer form accuracy and nanometric surface finish. Although there have been a number of studies on modeling and characterization of surface generation in Single Point Diamond Turning (SPTD), there is relatively little research on the modeling and characterization of surface generation in FTS machining of microlens arrays, which is radically different from SPTD and has additional process parameters. This paper therefore establishes a theoretical model for the prediction of surface generation in FTS machining of microlens arrays based on the cutting mechanism of FTS, cutting tool geometry, machining parameters, and the workpiece surface contour. A surface matching based method has been developed to characterize the surface quality of the microlens array as a whole instead of a single lens evaluation. A series of cutting experiments have been conducted, the actual results of which were found to largely agree with the predicted results. The successful development of the deterministic models and methods not only make the surface generation in FTS machining of microlens array more predictable, but also allow a better evaluation of the surface quality of the machined microlens array. It also helps to minimize or eliminate the need for conducting trial-and-error cutting experiments to optimize the machining process.     

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。     

Use of artificial neural networks for estimation of agricultural wheel traction force in soil bin

This paper presents an application of artificial neural networks (ANNs) for the prediction of traction force using readily available datasets experimentally obtained from a soil bin utilizing single-wheel tester. Aiming this, firstly the tests were carried out using two soil textures and two tire types as affected by velocity, slippage, tire inflation pressure, and wheel load. On this basis, the potential of neural modeling was assessed with multilayered perceptron networks using various training algorithms among which, backpropagation algorithm was compared to backpropagation with declining learning rate factor algorithm due to their primarily yielded superior performance. The results divulged that the latter one could better achieve the aim of study in terms of performance criteria. Furthermore, it was inferred that ANNs could reliably provide a promising tool for prediction of traction force and its modeling.     

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.     

工件表面粗糙度计算机预报平台设计

构建了基于工控机的工件表面粗糙度预报硬件平台,完成了自动检测和预报软件的分析与设计,核心部分采用神经网络进行建模,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。     

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.     

Estimation of cutting forces and surface roughness for hard turning using neural networks

Metal cutting mechanics is quite complicated and it is very difficult to develop a comprehensive model which involves all cutting parameters affecting machining variables. In this study, machining variables such as cutting forces and surface roughness are measured during turning at different cutting parameters such as approaching angle, speed, feed and depth of cut. The data obtained by experimentation is analyzed and used to construct model using neural networks. The model obtained is then tested with the experimental data and results are indicated.     

In-process regressions and adaptive multicriteria neural networks for monitoring and supervising machining operations

The authors develop a monitoring and supervising system for machining operations using in-process regressions (for monitoring) and adaptive feedforward artificial neural networks (for supervising). The system is designed for: (1) in-process tool life measurement and prediction; (2) supervision of machining operations in terms of the best machining setup; and (3) catastrophic tool failure monitoring. The monitoring system predicts tool life by using different sensors for gathering information based on a regression model that allows for the variations between tools and different machine setups. The regression model makes its prediction by using the history of other tools and combining it with the information obtained about the tool under consideration. The supervision system identifies the best parameters for the machine setup problem within the framework of multiple criteria decision making. The decision maker (operator) considers several criteria, such as cutting quality, production rate and tool life. To make the optimal decision with several criteria, an adaptive feedforward artificial neural network is used to assess the decision maker's preferences. The authors' neural network approach learns from the decision maker's complex behavior and hence, in automatic mode, can make decisions for the decision maker. The approach is not computationally demanding, and experiments demonstrate that its predictions are accurate.     

多传感器数据融合技术在刀具状态监测中的应用

提出了一种基于混合智能融合技术进行铣刀磨损量监测和预测方法。利用多传感器对切削力和振动信号进行监测,通过频率变换提取切削力特征量,采用小波包分解技术提取振动信号特征量。通过信号特征值的组合,分别探讨了几种混合智能数据融合技术——小波神经网络、遗传神经网络、遗传小波神经网络对刀具磨损量的预测效果。试验分析表明:提出的几种基于多传感器的混合智能数据融合技术均能够有效地完成刀具磨损量监测和预测,同时,对这几种数据融合技术各自的特点进行了比较分析。     

A new approach for predicting and collaborative evaluating the cutting force in face milling based on gene expression programming

Cutting force is one of the fundamental elements that can provide valuable insight in the investigation of cutter breakage, tool wear, machine tool chatter, and surface finish in face milling. Analyzing the relationship between process factors and cutting force is helpful to set the process parameters of the future cutting operation and further improve production quality and efficiency. Since cutting force is impacted by the inherent uncertainties in the machining process, how to predict the cutting force presents a significant challenge. In the meantime, face milling is a complex process involving multiple experts with different domain knowledge, collaborative evaluation of the cutting force model should be conducted to effectively evaluate the constructed predictive model. Gene Expression Programming (GEP) combines the advantages of the Genetic Algorithm (GA) and Genetic Programming (GP), and has been successfully applied in function mining and formula finding. In this paper, a new approach to predict the face milling cutting force based on GEP is proposed. At the basis of defining a GEP environment for the cutting force prediction, an explicit predictive model has been constructed. To verify the effectiveness of the proposed approach, a case study has been conducted. The comparisons between the proposed approach and some previous works show that the constructed model fits very well with the experimental data and can predict the cutting force with a high accuracy. Moreover, in order to better apply the constructed predictive models in actual face milling process, a collaborative model evaluation method is proposed to provide a distributed environment for geographical distributed experts to evaluate the constructed predictive model collaboratively, and four kinds of collaboration mode are discussed.     

The prediction of profile deviations from multi process machining of complex geometrical features using combined evolutionary and neural network algorithms with embedded simulation

The capability to generate complex geometrical features at tight tolerances and fine surface roughness is a key element in the implementation of Creep Feed grinding process in specialist applications such as the aerospace manufacturing environment. Based on the analysis of 3D cutting forces this paper proposes a novel method of predicting the profile deviations of tight geometrical features generated using Creep Feed grinding. In this application, there are several grinding passes made at varying depths providing an incremental geometrical change with the last cut generating the final complex feature. With repeatable results from co-ordinate measurements both the radial and tangential forces can be gauged versus the accuracy of the ground features. The tangential force was found more sensitive to the deviation of actual cut depth from the theoretical one. However, to make a more robust prediction on the profile deviation its values were considered as a function of both force components (proportional to force: power was also included). For multi process, one machining platforms hole making was also investigated in terms of monitoring the force to ensure the mean cylinder was kept within required tolerances and with minimal subsequent machining (due to these imposed accuracies this is also considered a complex feature). Genetic programming (GP), an evolutionary programming technique, has been used to compute the prediction rules of part profile deviations based on the extracted radial and tangential force correlated with the said chosen “gauging” methodology (for grinding process). GP was also used to correlate the force and flank wear (VB) for hole deviations. It was found that using this technique, complex rules can be achieved and used online to dynamically control the geometrical accuracy of ground and drilled hole features. The GP complex rules are based on the correlation between the measured forces and recorded deviation of the theoretical profile (both grinding and hole making). The mathematical rules are generated from Darwinian evolutionary strategy which provides the mapping between different output classes. GP works from crossover recombination of different rules and the best individual is evaluated in terms of the given ‘best fitness value so far’ which closes on an optimal solution. The best obtained GP terminal sets were realised in rule-based embedded coded systems which were finally implemented into a real-time Simulink simulation. This realisation gives a view of how such a control regime can be utilised within an industrial capacity. Neural networks were used for GP decision verification ensuring less sensitivity to possible outliers giving more robustness to the integrated system.     

Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process

In this paper, evolutionary algorithms (EAs) are deployed for multi-objective Pareto optimal design of group method of data handling (GMDH)-type neural networks which have been used for modelling an explosive cutting process using some input–output experimental data. In this way, multi-objective EAs (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity-preserving mechanism are used for Pareto optimization of such GMDH-type neural networks. The important conflicting objectives of GMDH-type neural networks that are considered in this work are, namely, training error (TE), prediction error (PE), and number of neurons (N) of such neural networks. Different pairs of theses objective functions are selected for 2-objective optimization processes. Therefore, optimal Pareto fronts of such models are obtained in each case which exhibit the trade-off between the corresponding pair of conflicting objectives and, thus, provide different non-dominated optimal choices of GMDH-type neural networks models for explosive cutting process. Moreover, all the three objectives are considered in a 3-objective optimization process, which consequently leads to some more non-dominated choices of GMDH-type models representing the trade-offs among the training error, prediction error, and number of neurons (complexity of network), simultaneously. The overlay graphs of these Pareto fronts also reveal that the 3-objective results include those of the 2-objective results and, thus, provide more optimal choices for the multi-objective design of GMDH-type neural networks in terms of minimum training error, minimum prediction error, and minimum complexity.     

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.     

基于混合智能计算的铣刀状态监测

提出了一种基于混合智能融合技术进行铣刀磨损量监测和预测方法。利用多传感器对切削力和振动信号进行监测,通过频率变换提取切削力特征量,采用小波包分解技术提取振动信号特征量。通过信号特征值的组合,分别探讨了几种混合智能数据融合技术-小波神经网络,遗传神经网络,遗传小波神经网络对刀具磨损量的预测效果。实验分析表明,提出的几种基于多传感器的混合智能数据融合技术均能够有效地完成刀具磨损量监测和预测,同时对它们各自的特点进行了比较分析。     

三维工序模型切削载荷动态变化的工艺优化策略

综合零件的加工特征及约束条件并引入布尔差运算,可以分解出零件典型面组并 生成对应的工艺路线谱系,达到零件加工工艺快速优化设计的目的。通过分析三维工序模型典 型面组加工时的动态特性,采用B 样条曲线插值参数化的方法构建刀具-工件接触区域动态边界 的统一数学模型,预测切削载荷动态变化状况,并进行切削参数优化达到有效控制切削载荷的 目的。最后以某零件的车削加工为例进行分析,验证该工艺优化策略的可行性。     

Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks

In a modern machining system, tool condition monitoring systems are needed to get higher quality production and to prevent the downtime of machine tools due to catastrophic tool failures. Also, in precision machining processes surface quality of the manufactured part can be related to the conditions of the cutting tools. This increases industrial interest for in-process tool condition monitoring (TCM) systems. TCM supported modern unmanned manufacturing process is an integrated system composed of sensors, signal processing interface and intelligent decision making strategies. This study includes key considerations for development of an online TCM system for milling of Inconel 718 superalloy. An effective and efficient strategy based on artificial neural networks (ANN) is presented to estimate tool flank wear. ANN based decision making model was trained by using real time acquired three axis (F_x, F_y, F_z) cutting force and torque (M_z) signals and also with cutting conditions and time. The presented ANN model demonstrated a very good statistical performance with a high correlation and extremely low error ratio between the actual and predicted values of flank wear.