The optimal cutting-parameter selection of production cost in HSM for SKD61 tool steels

The main purpose of this study was to construct an investigation of optimal cutting parameters for minimizing production cost on the rough machining of high speed milling operation. A machining model is constructed based on a polynomial network. The polynomial network can learn the relationships between cutting parameters (cutting speed, feed per tooth, and axial depth of cut) and tool life through a self-organizing technique. Once the material removal volume for machined parts and various time and cost components of the high speed milling operations are given, an optimization algorithm using a simulated annealing method is then applied to the polynomial network for determining optimal cutting parameters. The optimal cutting parameters are subjected to an objective function of minimum production cost with the feasible range of cutting parameters.     

Determination of optimal cutting parameters in wire electrical discharge machining

Owing to the complexity of wire electrical discharge machining (wire-EDM), it is very difficult to determine optimal cutting parameters for improving cutting performance. The paper utilizes a feedforward neural network to associate the cutting parameters with the cutting performance. A simulated annealing (SA) algorithm is then applied to the neural network for solving the optimal cutting parameters based on a performance index within the allowable working conditions. Experimental results have shown that the cutting performance of wire-EDM can be greatly enhanced using this new approach.     

基于响应面法的环网柜接线套管干切削工艺参数优化研究

采用干切削加工是修复环网柜接线套管表面烧蚀、裂痕的一种有效方法。利用单因素试验研究切削用量在未涂层、TiAlCrN涂层和TiAlSiN涂层刀具下对切削力的影响规律。在单因素试验的基础上运用Box-Behnken中心组合试验方法,采用性能最优的TiAlSiN涂层刀具对环网柜接线套管切削工作参数进行试验研究,以切削速度、进给量和背吃刀量为试验因素,以刀具切向力、轴向力、径向力为试验指标进行三因素三水平二次旋转回归正交试验。通过建立响应面数学模型,分析各切削工艺参数对切削性能的影响,并对试验因素进行综合优化。试验结果表明：影响切削力显著顺序为背吃刀量＞进给量＞切削速度;最优参数组合为切削速度94.589 m/min、进给量0.097 mm/r、背吃刀量0.501 mm,此时刀具切向力为11.75 N、轴向力为34.80 N、径向力为19.53 N;验证试验结果与理论优化值基本吻合。     

Application of an optimized SA-ANN hybrid model for parametric modeling and optimization of LASOX cutting of mild steel

Laser assisted oxygen cutting (LASOX) process is an efficient method for cutting thick mild steel plates compared to conventional laser cutting process. However, scanty information is available as to modeling of the process. The paper presents an optimized SA-ANN model of artificial neural network (ANN) and simulated annealing (SA) to predict and optimize cutting quality of LASOX cutting process of mild steel plates. Optimization of SA-ANN parameters is carried out first where the ANN architecture and initial temperature for SA are optimized. The optimized ANN architecture is further trained using single hidden layer back propagation neural network (BPNN) with Bayesian regularization (BR). The trained ANN is then used to evaluate the objective function during optimization with SA. Experimental dataset employed for the purpose consists of input cutting parameters comprising laser power, cutting speed, gas pressure and stand-off distance while the resulting cutting quality is represented by heat affected zone (HAZ) width, kerf width and surface roughness. Results indicate that the SA-ANN model can predict the optimized output with reasonably good accuracy (around 3%). The proposed approach can be extended for prediction and optimization of operational parameters with reasonable accuracy for any experimental dataset.     

基于遗传算法BP神经网络的DEFORM-3D车削加工模拟优化

采用DEFORM-3D软件对高速车削进行仿真,得出车削过程中的工艺数据;构建BP神经网络,利用遗传算法优化BP网络,对结果做出了精确预报,找到了模拟条件的最优值,节省了大量的时间以及人力物力,有利于了解车削机理和提高车削质量。     

基于人工神经网络和遗传算法的平面铣削加工参数自适应优化

机械加工最优自适应控制的关键在于自适应加工模型的建立和实时优化策略的制定。本文提出用人工神经网络方法建立加工过程模型 ,用遗传算法实现在线优化。基于以上算法 ,构造了平面铣削加工参数自适应优化系统 ,可使加工系统在不违反加工约束的前提下 ,总是获得最大材料去除率。     

等离子切割多参数智能决策与控制

等离子体切割是一种经济、高效的金属切割工艺.等离子切割工艺参数中,一些参数相互耦合,共同影响切割质量与切割效率,因此,切割参数的选择是一个关键而困难的问题.论文介绍了智能数控等离子体切割机的体系结构和主要功能模块,采用神经网络实现了等离子体切割工艺参数的优化选择,采用电压测量方式实时检测割炬头与被切割板材的距离并自动调节割炬头高度,以提高切割质量和效率.系统具有自动编程、切割仿真与切割轨迹实时跟踪显示等功能.     

Gradual wear monitoring of turning inserts using wavelet analysis of ultrasound waves

Discrete wavelet transforms of ultrasound waves is used to measure the gradual wear of carbide inserts during turning operations. Ultrasound waves, propagating at a nominal frequency of 10 MHz, were pulsed into the cutting tools towards the cutting edge at a burst frequency of 10 KHz. The reflected waves off the mark, nose and flank surfaces were digitized at a sampling rate of 100 MHz. Daubechies Quadrature Mirror Filter pair was used to decompose ultrasound signals into frequency packets using a tree structure.Normalized signals in each level of decomposition were used to search for a neural network architecture that correlates the ultrasound measurements to the wear level on the tool. A three-layer Multi-Layer Perceptron architecture yielded the best correlation (95.9%) using the wave packets from the fourth level of decomposition with frequencies 3.75–4.375 and 5.625–6.875 MHz.     

A chaotic cutting process and determining optimal cutting parameter values using neural networks

A model of an orthogonal cutting system is described as an elastic structure deformable in two directions. In the system, a cutting force is generated by material flow against the tool. Nonlinear dependency of the cutting force on the cutting velocity can cause chaotic vibrations of the cutting tool which influence the quality of a manufactured surface. The intensity and the characteristics of vibrations are determined by the values of the cutting parameters. The influence of cutting depth on system dynamics is described by bifurcation diagrams. The properties of oscillations are illustrated by the time dependence of tool displacement, the corresponding frequency spectra and phase portraits. The corresponding strange attractors are characterized by correlation dimension. The vibrations are characterized by the maximum Lyapunov exponent. The manufactured surface at the first cut is taken as the incoming surface in the second cut, thus incorporating the influence of the rough surface in the model. Again, bifurcation diagrams, the correlation dimension and the maximum Lyapunov exponent are employed to describe the effects of parametrical excitation on the cutting dynamics. A cost function is defined which describes the dependence of the cutting performance on cutting depth. The cost function is empirically modeled using a self-organizing neural network. A conditional average estimator is applied to determine the optimal value of the cutting depth applicable as a control variable of the cutting process.     

高速切削CNC系统的研究

比较了高速切削加工与传统数控加工的区别 ,指出了高速切削对CNC系统的特殊要求 ,基于这些要求分析了目前高速切削CNC系统存在的四个问题 :①CNC体系结构封闭 ;②与CAM系统集成不够 ;③插补器和进给伺服控制器存在局限 ;④CNC缺乏对已知信息的综合考虑。针对这些问题的解决 ,提出基于PC的开放式CNC系统是解决问题的可行方案 ,应用面向对象的方法建立了这种方案的层次化参考模型 ,最后 ,给出了其实现的硬件拓扑结构     

Sensing tool breakage in face milling with a neural network

A new approach using a neural network to process the features of the cutting force signal for the recognition of tool breakage in face milling is proposed. The cutting force signal is first compressed by averaging the cutting force signal per tooth. Then, the average cutting force signal is passed through a median filter to extract the features of the cutting force signal due to tool breakage. With the back propagation training process, the neural network memorizes the feature difference of the cutting force signal between with and without tool breakage. As a result, the neural network can be used to classify the cutting force signal with or without tool breakage. Experiments show this new approach can sense tool breakage in a wide range of face milling operations.     

On-line monitoring of flank wear in turning with multilayered feed-forward neural network

A multilayer feed-forward neural network (MLFF N-Network) algorithm is presented for on-line monitoring of tool wear in turning operations. The algorithm is based on the cutting conditions (cutting speed and feed rate) and measured cutting forces, which are used as inputs to a three-layer MLFF N-Network. The network is first trained using a set of workpiece material (P20 mold steel) and a tungsten carbide (H13A) cutting tool at various cutting conditions. The algorithm is later successfully verified on-line during turning of the same mold steel at conditions that differ from the data used in training. The algorithm is packaged in a software module, and integrated to an open Intelligent Machining Module used on industrial CNC systems.     

Grey relational analysis coupled with principal component analysis for optimization design of the cutting parameters in high-speed end milling

This paper investigates optimization design of the cutting parameters for rough cutting processes in high-speed end milling on SKD61 tool steel. The major characteristics indexes for performance selected to evaluate the processes are tool life and metal removal rate, and the corresponding cutting parameters are milling type, spindle speed, feed per tooth, radial depth of cut, and axial depth of cut. In this study, the process is intrinsically with multiple performance indexes so that grey relational analysis that uses grey relational grade as performance index is specially adopted to determine the optimal combination of cutting parameters. Moreover, the principal component analysis is applied to evaluate the weighting values corresponding to various performance characteristics so that their relative importance can be properly and objectively described. The results of confirmation experiments reveal that grey relational analysis coupled with principal component analysis can effectively acquire the optimal combination of cutting parameters. Hence, this confirms that the proposed approach in this study can be an useful tool to improve the cutting performance of rough cutting processes in high-speed end milling process.     

Optimization of cutting parameters for maximizing tool life

Optimum use of the cutting tool is a growing need in modern industries since the cost of production is directly affected by this. This paper presents a new approach for improving the cutting tool life by using optimal values of velocity and feed throughout the cutting process. A tool life equation has been established from experimental data and the adhesion wear model. Optimization techniques have been used to maximize the tool life subject to practical constraints while maintaining a constant metal removal rate. The experimental results showed an improvement in tool life by 30%.     

型腔加工中的行切方向的优化方法

采用数学优化的方法来优化行切加工轨迹的方向，用此方法生成的刀具轨迹是满足既定目标的最优行切刀轨。概括了型腔加工轨迹生成的方法，分析了采用数学优化的方法来优化行切加工轨迹方向的原理、过程，并给出了相应的程序框图。     

Intelligent adaptive control and monitoring of band sawing using a neural-fuzzy system

In bandsaw machines, it is desired to feed the bandsaw blade into the workpiece with an appropriate feeding force in order to perform an efficient cutting operation. This can be accomplished by controlling the feed rate and thrust force by accurately detecting the cutting resistance against the bandsaw blade during cutting operation. In this study, a neural-fuzzy-based force model for controlling band sawing process was established. Cutting parameters were continuously updated by a secondary neural network, to compensate the effect of environmental disturbances. Required feed rate and cutting speed were adjusted by developed fuzzy logic controller. Results of cutting experiments using several steel specimens show that the developed neural-fuzzy system performs well in real time in controlling cutting speed and feed rate during band sawing. A material identification system was developed by using the measured cutting forces. Materials were identified at the beginning of the cutting operation and cutting force model was updated by using the detected material type. Consequently, cutting speed and feed rate were adjusted by using the updated model. The new methodology is found to be easily integrable to existing production systems.     

Optimal drilling of particulate metal matrix composites based on experimental and numerical procedures

An experimental and numerical study of the evolution of cutting forces, tool wear and surface finish, measured when drilling the particulate metal matrix composite A356/20/SiCp-T6 is presented. The experimental work was developed through the continuous measurement of the cutting forces with an appropriate piezoelectric dynamometer. The wear type was identified and its evolution with cutting time was measured. Drills with polycrystalline diamond were tested. The surface finish of the holes was evaluated with a profilometer.Using the experimental results, a numerical search of optimal drilling conditions was performed. Since there are contradictory objectives, such as maximisation of tool life and minimisation of tool wear, the concept of the Pareto optimum solution is considered in the optimisation procedure. An evolution strategy is adopted to obtain the optimal solution for cutting speed, feed rate and tool life prediction with industrial interest.     

神经网络在切削加工过程中的应用

文章提出了一种基于神经网络的自适应控制方法，并以非线性的切削加工过程为对象，进行了仿真。仿真结果表明，该系统在切削工况发生时变的情况下仍能实现恒切削力控制，具有很强的鲁棒性，达到了提高加工效率的目的。     

Cutting parameter optimization for one-step shaft excavation technique based on parallel cutting method

The outcome of the cutting blasting in a one-step shaft excavation is heavily related to the cutting parameters used for parallel cutting method. In this study, the relationships between the cutting parameters (such as the hole spacing L and the empty hole diameter D) and damage zones were investigated by numerical simulation. A damage state index γ was introduced and used to characterize the crushing and crack damage zones through a user-defined subroutine. Two indices, i.e., η₁ and η₂ that can reflect the cutting performance, were also introduced. The simulation results indicate that an optimal value of L can be obtained so that the η₁ and η₂ can reach their optimal states for the best cutting performance. A larger D results in better cutting performance when the L value maintains its best. In addition, the influences of the loading rate and the in-situ stress on the cutting performance were investigated. It is found that an explosive with a high loading rate is suit for cutting blasting. The propagation direction and the length of the tensile cracks are affected by the direction and the magnitude of the maximum principal stress.     

A Topological Semantics for Rule Extraction with Neural Networks

Rule extraction with neural networks is a subject of increasing interest. Research in this area could benefit from the availability of a formal model of the semantics of the rules. A model of this kind would express the relationship between the application data, the neural network learning model and the extracted rules with mathematical rigor, allowing systematic analysis and modification of rule extraction approaches and the neural network architectures used. However, formal models of this kind are not in common use. This paper proposes a formal semantic model and includes an analysis of an example rule extraction architecture and some issues raised by it and other architectures. In the formal model, the semantics of a neural network is expressed through a form of model theory based upon concepts from topology, including limit points and continuous functions. A state of adaptation of the neural network in which it has learned a set of rules from training data corresponds to a continuous function between topological systems. Topological systems, the domains of inputs to the network, are a generalization of the concept of a topological space. The results of an example analysis with this model suggest a direction for improvements to the example architecture and the desirability of applying the model to other rule extraction approaches.