On-line prevention of wire breakage in wire electro-discharge machining

Wire electro-discharge machining (WEDM) is a fully extended and competitive machining process widely used to produce dies and moulds. However, the risk of wire breakage affects adversely the full potential of WEDM since the overall process efficiency is considerably reduced. The present paper discusses the results of the analyses of an exhaustive experimental database that reproduces unexpected disturbances that may appear during normal operation. The results of the analyses reveal new symptoms that allow one to predict wire breakage. These symptoms are especially related to the occurrence of an increase in discharge energy, peak current, as well as increases and/or decreases in ignition delay time. The differences observed in the symptoms related to workpiece thickness are also studied. Another contribution of this paper is the analyses of the distribution of the anticipation time for different validation tests.Based on the results of the analyses, this paper contributes to improve the process performance through a novel wire breakage monitoring and diagnosing system. It consists of two well differentiated parts: the virtual instrumentation system (VIS) that measures relevant magnitudes, and the diagnostic system (DS) that detects low quality cutting regimes and predicts wire breakage. It has been successfully validated through a considerable number of experimental tests performed on an industrial WEDM machine for different workpiece thickness. The efficiency of the supervision system has been quantified through an efficiency rate defined in this paper.     

刀具状态在线监测与智能识别方法研究

切削加工中刀具状态是影响加工质量的关键因素,刀具的磨损直接影响工件的加工精度和表面粗糙度.选择加速度传感器监测切削加工中的振动信号,针对刀具状态变化时振动能量分布随之变化的特点,提取不同频段振动能量作为特征量,利用RBF神经网络进行聚类辨识.实验结果表明,该方法具有良好的识别效果和工程应用价值.     

Genetic synthesis of a fuzzy pulse discriminator in electrical discharge machining

In this paper, a sensor system, called a fuzzy pulse discriminator, is developed to classify various discharge pulses in electrical discharge machining (EDM). The fuzzy rules of the pulse discriminator are obtained based on the features of the gap voltage and gap current between the tool workpiece. The membership functions of the fuzzy pulse discriminator are automatically synthesized by using genetic algorithms. The effectiveness of this approach is verified under different cutting parameters.     

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.     

Energy-efficient multi-pass cutting parameters optimisation for aviation parts in flank milling with deep reinforcement learning

Cutting parameters play a major role in improving the energy efficiency of the manufacturing industry. As the main processing method for aviation parts, flank milling usually adopts multi-pass constant and conservative cutting parameters to prevent workpiece deformation but degrades energy efficiency. To address the issue, this paper proposes a novel multi-pass parametric optimisation based on deep reinforcement learning (DRL), allowing parameters to vary to boost energy efficiency under the changing deformation limits in each pass. Firstly, it designs a variable workpiece deformation const.raint on the principle of stiffness decreasing along the passes, based on which it constructs an energy-efficient parametric optimisation model, giving suitable decisions that respond to the varying cutting conditions. Secondly, it transforms the model into a Markov Decision Process and Soft Actor Critic is applied as the DRL agent to cope with the dynamics in multi-pass machining. Among them, an artificial neural network-enabled surrogate model is applied to approximate the real-world machining, facilitating enough explorations of DRL. Experimental results show that, compared with the conventional method, the proposed method improves 45.71% of material removal rate and 32.27% of specific cutting energy while meeting deformation tolerance, which substantiates the benefits of the energy-efficient parametric optimisation, significantly contributing to sustainable manufacturing.     

A thermal model to investigate the wire rupture phenomenon for improving performance in EDM wire cutting

A continuous trend towards automation can be noticed in wire cutting electro discharge machining due to the relatively low machining speed and large investments required. In this respect, the wire rupture phenomenon may be considered a major problem, limiting the overall efficiency of the operation. The thermal load on the wire seems to be a governing factor determining wire rupture. This has been proven by a number of tests using an EDM pulse analyzing system. In order to examine the relative influence of various process parameters on the thermal load of the wire, a mathematical thermal model was developed. The model enables the calculation of the temperature rise in the wire due to heat input by electrical discharges and from Joule's heat. The result of this analysis can be used in an adaptive control optimization system in order to maximize the cutting speed, without enhancing the risk of wire rupture.     

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.     

ForceNet: An offline cutting force prediction model based on neuro-physical learning approach

Accurate cutting force prediction serves as an important reference to the optimization of numerically controlled machining process. Traditional cutting force modeling via theoretical cutting mechanism hampers accurate prediction for actual machining process due to its highly suppressed modeling flexibility. On the other hand, machine learning based modeling approaches demand large amount of diversified labeled samples to achieve comparable prediction results, while collecting these samples can be tedious and costly because the cutter workpiece engagement (CWE) keeps changing during actual process. This paper presents a cutting force prediction model, named ForceNet, which incorporates elementary physical priori into structured neural networks to predict cutting force for end-milling process of complex CWE. The main idea is to use grayscale images to represent CWE geometry, providing a universal input to the ForceNet. Unlike traditional deep neural networks served as an unexplainable black box, the core of the ForceNet is constructed by the vector summation of directional primitive cutting force elements, which are approximated using elementary neural networks. Preliminary results indicate that ForceNet outperformed existing methods not only with greater prediction accuracy in unseen cutting situations, but also with less training data needed thanks to its inherent neuro-physical structure.     

Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm

Modern machining processes are now-a-days widely used by manufacturing industries in order to produce high quality precise and very complex products. These modern machining processes involve large number of input parameters which may affect the cost and quality of the products. Selection of optimum machining parameters in such processes is very important to satisfy all the conflicting objectives of the process. In this research work, a newly developed advanced algorithm named ‘teaching–learning-based optimization (TLBO) algorithm’ is applied for the process parameter optimization of selected modern machining processes. This algorithm is inspired by the teaching–learning process and it works on the effect of influence of a teacher on the output of learners in a class. The important modern machining processes identified for the process parameters optimization in this work are ultrasonic machining (USM), abrasive jet machining (AJM), and wire electrical discharge machining (WEDM) process. The examples considered for these processes were attempted previously by various researchers using different optimization techniques such as genetic algorithm (GA), simulated annealing (SA), artificial bee colony algorithm (ABC), particle swarm optimization (PSO), harmony search (HS), shuffled frog leaping (SFL) etc. However, comparison between the results obtained by the proposed algorithm and those obtained by different optimization algorithms shows the better performance of the proposed algorithm.     

Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model

In a high speed milling operation the cutting tool acts as a backbone of machining process, which requires timely replacement to avoid loss of costly workpiece or machine downtime. To this aim, prognostics is applied for predicting tool wear and estimating its life span to replace the cutting tool before failure. However, the life span of cutting tools varies between minutes or hours, therefore time is critical for tool condition monitoring. Moreover, complex nature of manufacturing process requires models that can accurately predict tool degradation and provide confidence for decisions. In this context, a data-driven connectionist approach is proposed for tool condition monitoring application. In brief, an ensemble of Summation Wavelet-Extreme Learning Machine models is proposed with incremental learning scheme. The proposed approach is validated on cutting force measurements data from Computer Numerical Control machine. Results clearly show the significance of our proposition.     

数控加工仿真系统的研究与应用

介绍数控加工仿真系统的整体设计,提出格栅voxel三维实体建模方法,刀具扫描体的生成算法,实现了刀具切削工件过程的动态仿真,并对碰撞检查算法进行了初步的研究.基于以上方法,建立了蓝天数控系统的加工仿真系统,在加工前对加工程序进行验证,在加工时对刀具轨迹的执行、工件的切削过程等进行实时监控.     

Prediction of workpiece elastic deflections under cutting forces in turning

One of the problems faced in turning processes is the elastic deformation of the workpiece due to the cutting forces resulting in the actual depth of cut being different than the desirable one. In this paper, a cutting mechanism is described suggesting that the above problem results in an over-dimensioned part. Consequently, the problem of determining the workpiece elastic deflection is addressed from two different points of view. The first approach is based on solving the analytical equations of the elastic line, in discretized segments of the workpiece, by considering a stored modal energy formulation due to the cutting forces. Given the mechanical properties of the workpiece material, the geometry of the final part and the cutting force values, this numerical method can predict the elastic deflection. The whole approach is implemented through a Microsoft Excel^© workbook. The second approach involves the use of artificial neural networks (ANNs) in order to develop a model that can predict the dimensional deviation of the final part by correlating the cutting parameters and certain workpiece geometrical characteristics with the deviations of the depth of cut. These deviations are calculated with reference to final diameter values measured with precision micrometers or on a CMM. The verification of the numerical method and the development of the ANN model were based on data gathered from turning experiments conducted on a CNC lathe. The results support the proposed cutting mechanism. The numerical method qualitatively agrees with the experimental data while the ANN model is accurate and consistent in its predictions.     

Increasing CNC machine tool productivity by using tool-part touch control

In this paper a new system for increasing CNC machining productivity is described. The system is based on registering the moment when the cutting tool touches the workpiece during a machining operation. The cutting tool approaches the workpiece with rapid traverse and switches to work feed when it comes in contact with it. In this way, the time for ‘cutting air’ can significantly be reduced.     

An interactive multi-objective artificial neural network approach for machine setup optimization

In this paper, we develop an artificial neural network method for machine setup problems. We show that our new approach solves a very challenging problem in the area of machining i.e. machine setup. A review of machine setup concepts and methods, along with feedforward artificial neural network is presented. We define the problem of machine setup to assessing the values of machine speed, feed and depth of cut (process inputs) for a particular objective such as minimize cost, maximize productivity or maximize surface finish. We use cutting temperature, cutting force, tool life, and surface roughness (process outputs) rather than objective functions to communicate with the decision maker. We show the relationship between process inputs to process outputs. This relationship is used in determining machine setup parameters (speed, feed, and depth of cut). Back propagation neural network is used as a decision support tool. The network maps, the forward relationship, and backward relationship between process inputs and process outputs. This mapping facilitates an interactive session with the decision maker. The process input is appropriately selected. Our method has the advantage of forecasting machine setup parameters with very little resource requirement in terms of time, machine tool, and people. Forecast time is almost instantaneous. Accuracy of the forecast depends on training and a well determined training sample provides very high accuracy. Trained network replaces the knowledge of an experienced worker, hence labor cost can be potentially reduced.     

Modelling of the cutting tool stresses in machining of Inconel 718 using artificial neural networks

This study covers two main subjects: (i) The experimental and theoretical analysis: the cutting forces and indirectly cutting tool stresses, affecting the cutting tool life during machining in metal cutting, are one of very important parameters to be necessarily known to select the economical cutting conditions and to mount the workpiece on machine tools securely. In this paper, the cutting tool stresses (normal, shear and von Mises) in machining of nickel-based super alloy Inconel 718 have been investigated in respect of the variations in the cutting parameters (cutting speed, feed rate and depth of cut). The cutting forces were measured by a series of experimental measurements and the stress distributions on the cutting tool were analysed by means of the finite element method (FEM) using ANSYS software. ANSYS stress results showed that in point of the cutting tool wear, especially from von Mises stress distributions, the ceramic cutting insert may be possible worn at the distance equal to the depth of cut on the base cutting edge of the cutting tool. Thence, this wear mode will be almost such as the notch wear, and the flank wear on the base cutting edge and grooves in relief face. In terms of the cost of the process of machining, the cutting speed and the feed rate values must be chosen between 225 and 400 m/min, and 0.1 and 0.125 mm/rev, respectively. (ii) The mathematical modelling analysis: the use of artificial neural network (ANN) has been proposed to determine the cutting tool stresses in machining of Inconel 718 as analytic formulas based on working parameters. The best fitting set was obtained with ten neurons in the hidden-layer using back propagation algorithm. After training, it was found the R² values are closely 1.     

A time-varying geometry modeling method for parts with deformation during machining process

Deformation due to residual stress is a significant issue during the machining of thin-walled parts with low rigidity. If there are multiple processes with deformation during machining, some process suitability issues will appear. On this occasion, the actual geometric state of the deformed workpiece is needed for process adjustment. However, it is still a challenge to obtain the complete geometry information of deformed workpiece accurately and efficiently. In order to address this issue, a time-varying geometry modeling method, combining cutting simulation and in-process measurement, is proposed in this paper. The deformed workpiece model can be reconstructed via transforming the deformed workpiece with only a small amount of the measurement points by superimposing material removal and workpiece deformation simulation according to a time sequence, which takes advantage of the proposed Curved Surface Mapping based Geometric Representation Model (CSMGRM). Machining experiment of a typical structural part has shown that the deformed geometry model of the whole workpiece can be reconstructed within the error of 0.05mm, which is less than one tenth of the finish machining allowance in general cases, and it is sufficient to meet the accuracy requirements for interference or overcut/undercut analysis and process adjustment.     

An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM

A wire electrical discharge machined (WEDM) surface is characterized by its roughness and metallographic properties. Surface roughness and white layer thickness (WLT) are the main indicators of quality of a component for WEDM. In this paper an adaptive neuro-fuzzy inference system (ANFIS) model has been developed for the prediction of the white layer thickness (WLT) and the average surface roughness achieved as a function of the process parameters. Pulse duration, open circuit voltage, dielectric flushing pressure and wire feed rate were taken as model’s input features. The model combined modeling function of fuzzy inference with the learning ability of artificial neural network; and a set of rules has been generated directly from the experimental data. The model’s predictions were compared with experimental results for verifying the approach.     

Intelligent Fixture Design through a Hybrid System of Artificial Neural Network and Genetic Algorithm

In designing fixtures for machining operations, clamping scheme is a complex and highly nonlinear problem that entails the frictional contact between the workpiece and the clamps. Such parameters as contact area, state of contact, clamping force, wear and damage in the contact area and deformation of the component are of special interest. A viable fixture plan must include the optimum values of clamping forces. Along research efforts carried out in this area, this comprehensive problem in fixture design needs further investigation. In this study, a hybrid learning system that uses nonlinear finite element analysis (FEA) with a supportive combination of artificial neural network (ANN) and genetic algorithm (GA) is discussed. A frictional model of workpart–fixture system under cutting and clamping forces is solved through FEA. Training and querying an ANN takes advantage of the results of FEA. The ANN is required to recognize a pattern between the clamping forces and state of contact in the workpiece–fixture system and the workpiece maximum elastic deformation. Using the identified pattern, a GA-based program determines the optimum values for clamping forces that do not cause excessive deformation/stress in the component. The advantage of this work against similar studies is manifestation of exact state of contact between clamp elements and workpart. The results contribute to automation of fixture design task and computer aided process planning (CAPP).     

Computer aided contouring operation for traveling wire electric discharge machining (EDM)

This paper develops a computational method for numerical control (NC) of traveling wire electric discharge machining (EDM) operation from geometric representation of a desired cut profile in terms of its contours. Normalized arc length parameterization of the contour curves is used to represent the cut profile and a subdivision algorithm is developed together with kinematic analysis to generate the required motions of the machine tool axes. In generating the tool motions for cutting sections with high curvatures such as corners with small radii, a geometric path lifting method is presented that increases the machining gap and prevents gauging or wire breakage.     

Manufacture of a spur tooth gear in Ti-6Al-4V alloy by electrical discharge

This paper proposes a method of manufacturing a spur tooth gear in Ti-6Al-4V alloy (grade 5) using a wire electrical discharge machine (Wire EDM). A geometrical model for the gear is drawn up and implemented using the program MATLAB.The electro-erosion parameters tested for this alloy are applied to an ONA PRIMA S-250. The parameters used (power, pause, voltage, …) are based on the ONA EDM charts. The Taguchi orthogonal array method was chosen to obtain the optimum values for cutting Titanium.The work presented follows established lines for manufacturing mechanical parts using general purpose machines and tools. In this case, the WEDM process was used. The MATLAB program was employed to obtain the interpolation points. This program simplifies the task of solving the equations originated by the mathematical model which allows the wire path to be calculated.The WEDM method used here is a commendable alternative for machining electrically conductible materials which are difficult to work with using conventional machine tools (milling, turning or boring). Furthermore, the WEDM process reduces or even eliminates the need for subsequent polishing processes due to the high-quality finish achieved.