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.     

Incorporating prior model into Gaussian processes regression for WEDM process modeling

Sufficient sampling is usually time-consuming and expensive but also is indispensable for supporting high precise data-driven modeling of wire-cut electrical discharge machining (WEDM) process. Considering the natural way to describe the behavior of a WEDM process by IF-THEN rules drawn from the field experts, engineering knowledge and experimental work, in this paper, the fuzzy logic model is chosen as prior knowledge to leverage the predictive performance. Focusing on the fusion between rough fuzzy system and very scarce noisy samples, a simple but effective re-sampling algorithm based on piecewise relational transfer interpolation is presented and it is integrated with Gaussian processes regression (GPR) for WEDM process modeling. First, by using re-sampling algorithm encoded derivative regularization, the prior model is translated into a pseudo training dataset, and then the dataset is trained by the Gaussian processes. An empirical study on two benchmark datasets intuitively demonstrates the feasibility and effectiveness of this approach. Experiments on high-speed WEDM (DK7725B) are conducted for validation of nonlinear relationship between the design variables (i.e., workpiece thickness, peak current, on-time and off-time) and the responses (i.e., material removal rate and surface roughness). The experimental result shows that combining very rough fuzzy prior model with training examples still significantly improves the predictive performance of WEDM process modeling, even with very limited training dataset. That is, given the generalized prior model, the samples needed by GPR model could be reduced greatly meanwhile keeping precise.     

Application of ANFIS for modeling of layer thickness of chromium carbonitride coating

In the present work, layer thickness of duplex coating made from thermo-reactive deposition and diffusion has been predicted by Adaptive network-based fuzzy inference systems (ANFIS). A duplex surface treatment on five steels has been developed involving nitrocarburizing and followed by chromium thermo-reactive deposition (TRD) techniques. The TRD process was performed in molten salt bath at 550, 625 and 700 °C for 1–30 h. The process formed a thickness up to 9.5 μm of chromium carbonitride coatings on a hardened diffusion zone. A model based on ANFIS for predicting the layer thickness of duplex coating of the specimens has been presented. To build the model, training and testing using experimental results from 84 specimens were conducted. The data used as inputs in ANFIS models are arranged in a format of twelve parameters that cover the chemical composition (C, Mn, Si, Cr, Mo, V, W), the pre-nitriding time, ferro-chromium particle size, ferro-chromium weight percent, salt bath temperature and coating time. According to these input parameters, in the Adaptive network-based fuzzy inference system models, the layer thickness of duplex coating of each specimen was predicted. The training and testing results in ANFIS models have shown a strong potential for predicting the layer thickness of duplex coating.     

A standard deviation based firefly algorithm for multi-objective optimization of WEDM process during machining of Indian RAFM steel

Non-conventional machining processes always suffer due to their low productivity and high cost. However, a suitable machining process should improve its productivity without compromising product quality. This implies the necessity to use efficient multi-objective optimization algorithm in non-conventional machining processes. In this present paper, an effective standard deviation based multi-objective fire-fly algorithm is proposed to predict various process parameters for maximum productivity (without affecting product quality) during WEDM of Indian RAFM steel. The process parameters of WEDM considered for this study are: pulse current (I), pulse-on time (T _on), pulse-off time (T _off) and wire tension (WT).While, cutting speed (CS) and surface roughness (SR) were considered as machining performance parameters. Mathematical models relating the process and response parameters had been developed by linear regression analysis and standard deviation method was used to convert this multi objective into single objective by unifying the responses. The model was then implemented in firefly algorithm in order to optimize the process parameters. The computational results depict that the proposed method is well capable of giving optimal results in WEDM process and is fairly superior to the two most popular evolutionary algorithms (particle swarm optimization algorithm and differential evolution algorithm) available in the literature.

     

Prediction and optimization of performance measures in electrical discharge machining using rapid prototyping tool electrodes

In this work, the performance of rapid prototyping (RP) based rapid tool is investigated during electrical discharge machining (EDM) of titanium as work piece using EDM 30 oil as dielectric medium. Selective laser sintering, a RP technique, is used to produce the tool electrode made of AlSi10Mg. The performance of rapid tool is compared with conventional solid copper and graphite tool electrodes. The machining performance measures considered in this study are material removal rate, tool wear rate and surface integrity of the machined surface measured in terms of average surface roughness (Ra), white layer thickness, surface crack density and micro-hardness on white layer. Since the machining process is a complex one, potentiality of application of a predictive tool such as least square support vector machine has been explored to provide guidelines for the practitioners to predict various machining performance measures before actual machining. The predictive model is said to be robust one as root mean square error in the range of 0.11–0.34 is obtained for various performance measures. A hybrid optimization technique known as desirability based grey relational analysis in combination with firefly algorithm is adopted for simultaneously optimizing the performance measures. It is observed that peak current and tool type are the significant parameters influencing all the performance measures.

     

MACHINED SURFACE ROUGHNESS PREDICTION USING ADAPTIVE NEUROFUZZY INFERENCE SYSTEM

This work considers the effect of the depth of cut, feed, and number of revolutions on the roughness of the machined surface. The results obtained by experimentally investigating the workpiece “diving manifold” were used to model the input/output data plan for the adaptive neurofuzzy inference system (ANFIS). Those data were used to generate a fuzzy inference system that made it possible to predict the output (surface roughness) based on the given inputs (feed, number of revolutions, and depth of cut). The surface roughness results obtained by the fuzzy inference system (FIS) were compared with the surface roughness results obtained by neural networks, moving linear least square method and moving linear least absolute deviation method on the same set of experimental data. These methods and systems for prediction of surface roughness are helpful when solving practical technological problems in a manufacturing process, first by determining the cutting parameter values that will add to the demanded quality of a product, and later when optimizing the technological process.     

Analysis and optimization of surface roughness in the ball burnishing process using response surface methodology and desirabilty function

In the present study, an optimization strategy based on desirability function approach (DFA) together with response surface methodology (RSM) has been used to optimize ball burnishing process of 7178 aluminium alloy. A quadratic regression model was developed to predict surface roughness using RSM with rotatable central composite design (CCD). In the development of predictive models, burnishing force, number of passes, feed rate and burnishing speed were considered as model variables. The results indicated that burnishing force and number of passes were the significant factors on the surface roughness. The predicted surface roughness values and the subsequent verification experiments under the optimal conditions were confirmed the validity of the predicted model. The absolute average error between the experimental and predicted values at the optimal combination of parameter settings for surface roughness was calculated as 2.82%.     

Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC)

In this study, 39 sets of hard turning (HT) experimental trials were performed on a Mori-Seiki SL-25Y (4-axis) computer numerical controlled (CNC) lathe to study the effect of cutting parameters in influencing the machined surface roughness. In all the trials, AISI 4340 steel workpiece (hardened up to 69 HRC) was machined with a commercially available CBN insert (Warren Tooling Limited, UK) under dry conditions. The surface topography of the machined samples was examined by using a white light interferometer and a reconfirmation of measurement was done using a Form Talysurf. The machining outcome was used as an input to develop various regression models to predict the average machined surface roughness on this material. Three regression models – Multiple regression, Random forest, and Quantile regression were applied to the experimental outcomes. To the best of the authors’ knowledge, this paper is the first to apply random forest or quantile regression techniques to the machining domain. The performance of these models was compared to ascertain how feed, depth of cut, and spindle speed affect surface roughness and finally to obtain a mathematical equation correlating these variables. It was concluded that the random forest regression model is a superior choice over multiple regression models for prediction of surface roughness during machining of AISI 4340 steel (69 HRC).     

The experimental investigation of the effects of uncoated,PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks

In this study the machining of AISI 1030 steel (i.e. orthogonal cutting) uncoated, PVD- and CVD-coated cemented carbide insert with different feed rates of 0.25, 0.30, 0.35, 0.40 and 0.45 mm/rev with the cutting speeds of 100, 200 and 300 m/min by keeping depth of cuts constant (i.e. 2 mm), without using cooling liquids has been accomplished. The surface roughness effects of coating method, coating material, cutting speed and feed rate on the workpiece have been investigated. Among the cutting tools—with 200 mm/min cutting speed and 0.25 mm/rev feed rate—the TiN coated with PVD method has provided 2.16 μm, TiAlN coated with PVD method has provided 2.3 μm, AlTiN coated with PVD method has provided 2.46 μm surface roughness values, respectively. While the uncoated cutting tool with the cutting speed of 100 m/min and 0.25 mm/rev feed rate has yielded the surface roughness value of 2.45 μm. Afterwards, these experimental studies were executed on artificial neural networks (ANN). The training and test data of the ANNs have been prepared using experimental patterns for the surface roughness. In the input layer of the ANNs, the coating tools, feed rate (f) and cutting speed (V) values are used while at the output layer the surface roughness values are used. They are used to train and test multilayered, hierarchically connected and directed networks with varying numbers of the hidden layers using back-propagation scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) algorithms with the logistic sigmoid transfer function. The experimental values and ANN predictions are compared by statistical error analyzing methods. It is shown that the SCG model with nine neurons in the hidden layer has produced absolute fraction of variance (R²) values about 0.99985 for the training data, and 0.99983 for the test data; root mean square error (RMSE) values are smaller than 0.00265; and mean error percentage (MEP) are about 1.13458 and 1.88698 for the training and test data, respectively. Therefore, the surface roughness value has been determined by the ANN with an acceptable accuracy.     

Predictive Modeling of the Ti6Al4V Alloy Surface Roughness

The prediction of surface roughness is important for all materials that undergo manufacturing processes. The Ti6Al4V titanium alloy is commonly used in aerospace, automotive and power generation industries but also in the manufacturing of medical implants, mainly because of its biocompatibility. Here we study the relationship of Ti6Al4V’s surface roughness with critical machining parameters and conditions based on experimental input (machining parameters)-output (surface roughness) data derived during the turning operation. The experimental findings are converted into polynomial models through the Response Surface Methodology (RSM) and into a fuzzy logic system through the Adaptive Neuro-Fuzzy Inference System (ANFIS). The ability of these two methodologies to predict Ti6Al4V’s surface roughness when machined is presented and compared. It is observed that the ANFIS predicts surface roughness with less error mainly when the data used for evaluation are not completely different to those used for training.     

轨道车辆车轮辐板阻尼层对其降噪效果的影响分析*

采用模态叠加法求得阻尼车轮导纳特性,利用已建立的轮轨滚动噪声预测模型,以轮轨表面粗糙度为激励,分析了辐板阻尼层与其厚度对阻尼车轮振动与声辐射特性的影响规律.首先,建立了阻尼车轮三维实体有限元模型,采用Block Lanczos方法计算车轮模态特征;其次,利用模态叠加法求得车轮在单位荷载激励下的频响函数;然后,利用虚拟激励法求得车轮在粗糙度谱激励下的频域振动特性;最后,依据车轮动态响应通过解析的方法求得车轮声辐射频域特性.计算结果表明:(1)车轮辐板敷设阻尼层对车轮1000Hz以下频率的振动与噪声的抑制作用不明显,而对车轮1600Hz以上的高频振动具有良好的抑制作用;(2)车轮辐板双侧敷设阻尼的降噪效果优于单侧阻尼;(3)阻尼层可以有效抑制车轮振动,且车轮辐板敷设阻尼层厚度越厚效果越明显.     

Backcalculation of pavement layer thickness using data mining

Pavement deflection data are often used to evaluate a pavement’s structural condition nondestructively. Pavement layers are important parameters in view of bearing capacity. Pavement layer thickness may be known from the design project or site investigation. At the same time, using backcalculation analysis, flexible pavement layer thicknesses together with in situ material properties can also be backcalculated from the measured field data through appropriate analysis techniques. Data mining (DM) process has not been used as a backcalculation tool before. In this study, DM process is used in backcalculating the pavement layer thickness from deflections measured on the surface of the flexible pavements. Experimental deflection data groups from NDT are used to show the capability of the DM process in backcalculating the pavement layer thickness and compared each other. Performing the study, modeling with Kstar method gives fine results with respect to other DM modeling techniques. Backcalculation of pavement layer thickness using DM process has been carried out for the first time.     

Helical surface creation by wire electrical discharge machining for micro tools

In mechanical micromachining, micro tooling is one of the key factors affecting the finished geometrical accuracy and surface quality. To overcome the serious tool wear caused by relatively longer micromachining time, micro tools are usually made of ultra-hard materials such as polycrystalline diamond (PCD) or cubic boron nitride (CBN). Wire Electrical discharge machining (WEDM) is a good choice for efficient fabrications of micro tools made of ultra-hard materials. Considering the traces of wire motions form ruled surfaces, in this paper, typical custom micro milling tools with helical surfaces are generated by ruled surfaces. The simulation shows that the selection of guide lines and generating lines for ruled surface is the key point relating to the final geometrical accuracy and machining efficiency in custom micro tool fabrications by WEDM. Based on the mathematical models built in this paper, overcut can be avoided in the process planning stage for complicated helical surfaces. Furthermore, wire locations can be created conveniently by the introduced mathematical models for post processing in dedicated CAM systems.     

Nano‐domains formation on photoalignment thin film by self‐organized dewetting

A novel discontinuous photoalignment surface with nano‐domains for liquid crystal is developed. The formation of the discontinuous structure is created by self‐organized dewetting, which is regarded as one of the most promising bottom‐up approaches to fabricate nano‐structure. Different dewetting conditions, such as surface roughness, thickness and viscosity, have been investigated. Such discontinuous photoalignment layer can be fabricated on top of another continuous alignment layer to form a new kind of heterogeneous nano‐structured alignment surface – stacked alignment layers. This heterogeneous alignment surface can be used to produce arbitrary pretilt angles for the liquid crystal display. Simulation model has been built to understand the dewetting mechanism. Experiments using photo‐aligned and photo‐polymerisable polymer have been done to verify the dewetting theory. The produced stacked alignment layers are proved to be robust. Moreover, the dewetting processing is a fully controllable process and is compatible with existing manufacturing techniques.     

Development of a three-dimensional,regional, coupled wave,current, and sediment-transport model

We are developing a three-dimensional numerical model that implements algorithms for sediment transport and evolution of bottom morphology in the coastal-circulation model Regional Ocean Modeling System (ROMS v3.0), and provides a two-way link between ROMS and the wave model Simulating Waves in the Nearshore (SWAN) via the Model-Coupling Toolkit. The coupled model is applicable for fluvial, estuarine, shelf, and nearshore (surfzone) environments. Three-dimensional radiation-stress terms have been included in the momentum equations, along with effects of a surface wave roller model. The sediment-transport algorithms are implemented for an unlimited number of user-defined non-cohesive sediment classes. Each class has attributes of grain diameter, density, settling velocity, critical stress threshold for erosion, and erodibility constant. Suspended-sediment transport in the water column is computed with the same advection–diffusion algorithm used for all passive tracers and an additional algorithm for vertical settling that is not limited by the CFL criterion. Erosion and deposition are based on flux formulations. A multi-level bed framework tracks the distribution of every size class in each layer and stores bulk properties including layer thickness, porosity, and mass, allowing computation of bed morphology and stratigraphy. Also tracked are bed-surface properties including active-layer thickness, ripple geometry, and bed roughness. Bedload transport is calculated for mobile sediment classes in the top layer. Bottom-boundary layer submodels parameterize wave–current interactions that enhance bottom stresses and thereby facilitate sediment transport and increase bottom drag, creating a feedback to the circulation. The model is demonstrated in a series of simple test cases and a realistic application in Massachusetts Bay.     

Statistical analysis on the effect of calendering process parameters on the geometry and conductivity of printed patterns

The quality of printed electronics products depends largely on geometrical and morphological characteristics of printed pattern, such as pattern thickness and surface roughness. In this study, we employ the calendering process in order to control the pattern thickness, surface roughness, and conductivity of printed pattern. Both pressure and heat are used to change micro structure as well as morphology of printed layer during the calendering process. The effect of process parameters including temperature, pressure, and speed are investigated by using statistical techniques. Individual effects and interaction effects of the parameters are analyzed with Analysis of Variance (ANOVA) method. The results show that the pattern thickness, surface roughness, and resistivity of printed pattern decrease with increase of temperature and pressure. We also report that temperature is the most influential factor among the parameters. This study demonstrates that the calendering process can be used to enhance the quality of printed electronics products by controlling the surface quality of printed layer.     

A mathematical model to predict surface roughness and pattern thickness in roll-to-roll gravure printed electronics

The roll-to-roll printing processes have recently been applied for the manufacturing of printed electronics due to their advantages, such as their high-throughput capabilities and low associated costs. In a roll-to-roll printing machine, a web or substrate is moved from an unwinding roll to a winding roll. During printing, the operating tension is important for correct substrate handling to prevent substrate defects, such as wrinkles, scratches and breaks. Accordingly, the operating conditions of the moving web can affect the quality of the printed pattern. In this study, a mathematical model has been developed to predict the thickness and surface roughness of printed patterns. Because the dynamics of roll-to-roll printing systems are complicated and non-linear, a statistical model is preferred. A full factorial method has been used with four independent variables: operating tension, print speed, ink viscosity and theoretical transfer volume. This model accurately predicted the surface roughness and thickness of the printed pattern.     

熔融沉积制造表面粗糙度参数显著性及预测模型研究

为了确定影响熔融沉积制造(FDM)打印件表面粗糙度的显著性因素,设计了基于 温度、打印速度和层厚的9 组正交实验。通过探针式粗糙度仪测量打印件表面粗糙度,并进行 了信噪比计算和波动分析,确定了影响表面粗糙度的显著性因素。利用田口法、多元回归方程 和指数方程对表面粗糙度进行预测,确定FDM 打印件最小表面粗糙度的参数组合。分析结果 表明：层厚对于表面粗糙度的影响程度最大,温度次之,打印速度最小;为了验证其有效性和 适用性,针对不同打印模型和FDM 打印机进行了验证性实验。实验结果表明：在预测模型方 面,多元回归方程的预测结果优于指数方程和田口法。并且,上述结论对不同打印模型和FDM 打印机具有较为宽泛的适用性。     

基于BP网络的中速走丝切割加工参数优选研究

为了提高电火花线切割的工艺精度和加工效率,提出基于BP神经网络的中速走丝二次切割加工参数优选方法。通过对中速走丝两次切割过程进行正交试验,且在二次切割加工参数中加入二次切割的偏移量,进一步分析各因素对加工表面粗糙度和加工速度的影响,得出二次切割的最佳参数组合,最后运用BP神经网络构建加工参数优选模型并对实验结果进行分析。实验结果表明,该方法具有较高的预测精度,能够实现加工参数的优化选择,在实际生产过程中具有重要的参考作用。     

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.