Machine-vision-based electrode wear analysis for closed loop wire EDM process control

The purpose of this study was to develop a closed-loop machine vision system for wire electrical discharge machining (EDM) process control. Excessive wire wear leading to wire breakage is the primary cause of wire EDM process failures. Such process interruptions are undesirable because they affect cost efficiency, surface quality, and process sustainability. The developed system monitors wire wear using an image-processing algorithm and suggests parametric changes according to the severity of the wire wear. Microscopic images of the wire electrode coming out from the machining zone are fed to the system as raw images. In the proposed method, the images are pre-processed and enhanced to obtain a binary image that is used to compute the wire wear ratio (WWR). The input parameters that are adjusted to recover from the unstable conditions that cause excessive wire wear are pulse off time, servo voltage, and wire feed rate. The algorithm successfully predicted wire breakage events. In addition, the alternative parametric settings proposed by the control algorithm were successful in reducing the wire wear to safe limits, thereby preventing wire breakage interruptions.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00373-y     

用ANN法对房间空调冷负荷进行预测的讨论

通过影响空调负荷的参数的研究,认为空调负荷是一个动态过程;结合神经网络的内在特点和功能,对某一空调系统的冷负荷进行了预测,结果能满足计算要求.在这基础上考虑了为提高神经网络预测空调负荷准确性还应进一步开展的工作.     

Feasibility analysis and process characteristics of selective laser ablation assisted milling Inconel 718

Laser-assisted machining (LAM), as one of the most efficient ways, has been employed to improve the machinability of nickel-based superalloys. However, the conventional LAM process usually used high power laser with large spot size, easily leading to high processing costs and overheating of bulk materials. In this paper, a new approach of selective laser ablation assisted milling (SLA-Mill) process for nickel-based superalloys was proposed, in which low power laser with small spot size was used to selectively ablate the uncut surface in front of the cutting tool, resulting in plentiful surface defects emerging. Such defects would significantly weaken the mechanical strength of difficult-to-cut materials, which was different from the thermal "softening" principle of conventional LAM. Thus, the laser ablation effect with low power and small spot size was first studied. The relationship between process parameters (e.g., laser power, cutting speed and cutting depth) and process characteristics of SLA-Mill (e.g., chip morphology, tool wear and surface integrity) was systematically discussed. Moreover, the chip formation mechanism in the SLA-Mill process was indepth analyzed. Results show that the SLA-Mill process is an effective approach for enhancing the machinability of nickel-based superalloys. The resultant cutting force has a reduction of about 30% at laser power of 60 W, cutting speed of 90 m/min, and cutting depth of 0.1 mm. Furthermore, the chip formation, tool wear, and surface integrity have improved significantly. In general, this paper provides a new route for the application of LAM technology.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00384-9     

A framework for determining MIMO process parameters by a neuro-DM&ACO approach

A framework combining artificial neural network (ANN) modelling technique, data mining and ant colony optimisation (ACO) algorithm is proposed for determining multiple-input multiple-output (MIMO) process parameters from the initial chemical-mechanical planarisation (CMP) processes used in semiconductor manufacturing. Owing to the invisibility of the ANN in the solution procedures, the decision tree approach of data mining is adopted to provide the necessary information for a real-valued ACO. The simulation result demonstrates that the proposed method can be an efficient tool for selecting properly defined parameter combination with the CMP process.     

Wind turbine fault detection based on SCADA data analysis using ANN

Wind energy is one of the fast growing sources of power production currently, and there is a great demand to reduce the cost of operation and maintenance. Most wind farms have installed supervisory control and data acquisition(SCADA) systems for system control and logging data. However, the collected data are not used effectively. This paper proposes a fault detection method for main bearing wind turbine based on existing SCADA data using an artificial neural network(ANN). The ANN model for the normal behavior is established, and the difference between theoretical and actual values of the parameters is then calculated. Thus the early stage of main bearing fault can be identified to let the operator have sufficient time to make more informed decisions for maintenance.     

基于神经网络专家系统的供应商竞争力分析

供应链管理(SCM)是在IT技术广泛应用的基础上产生的一种先进、新颖的管理哲学与方法。在供应链中,合理地分析供应商的竞争力是优化选择具有敏捷性和相容性合作伙伴的关键。本文提出了供应商竞争力的分析指标体系,构建了一个基于人工智能神经网络的专家系统,较好地解决了对供应商竞争力进行分析评估的问题。     

Evaluation and analysis of cutting speed,wire wear ratio,and dimensional deviation of wire electric discharge machining of super alloy Udimet-L605 using support vector machine and grey relational analysis

The current study investigates the behavior of wire electric discharge machining (WEDM) of the super alloy Udimet-L605 by employing sophisticated machine learning approaches.The experimental work was designed on the basis of the Taguchi orthogonal L27 array,considering six explanatory variables and evaluating their influences on the cutting speed,wire wear ratio (WWR),and dimensional deviation (DD).A support vector machine (SVM) algorithm using a normalized poly-kernel and a radial-basis flow kernel is recommended for modeling the wire electric discharge machining process.The grey relational analysis (GRA) approach was utilized to obtain the optimal combination of process variables simultaneously, providing the desirable outcome for the cutting speed, WWR,and DD.Scanning electron microscope and energy dispersive X-ray analyses of the samples were performed for the confirmation of the results.An SVM based on the radial-basis kernel model dominated the normalized polykernel model.The optimal combination of process variables for a mutually desirable outcome for the cutting speed,WWR,and DD was determined as T_on1,T_off2,I_P1, WT3,SV1,and WF3.The pulse-on time is the significant variable influencing the cutting speed,WWR,and DD.The largest percentage of copper (8.66%) was observed at the highest cutting speed setting of the machine compared to 7.05% of copper at the low cutting speed setting of the machine.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0192-7     

Strain rate analyses during elliptical vibration cutting of Inconel 718 using finite element analysis,Taguchi method,and ANOVA

The high strain rate in metal cutting significantly affects the mechanical properties of the work piece by altering its properties. This study outlines the material strain rates during elliptical vibration cutting. The finite element analysis, Taguchi method, and analysis of variance (ANOVA) were employed to analyze the effects and contributions of cutting and vibration process parameters (feed rate, rake angle, tangential amplitude, and frequency of vibration) on the variation of strain rates during machining of Inconel 718. Taguchi signal-to-noise analysis on an L₁₈ (2¹×3³) orthogonal array was used to determine the optimum parametric combination for the maximum strain rate, and ANOVA was applied to evaluate the significance of control parameter factors on the strain rate. The results of the finite element analysis under different conditions illustrated that the feed rate and rake angle were negatively related to the strain rate, whereas the tangential amplitude and frequency had a positive response. Furthermore, ANOVA results indicated that the effect of the feed rate, tool rake angle, vibration frequency, and tangential amplitude on the strain rate were all statistically significant, with a reliability level of 95%. Of these, the dominant parameter affecting the strain rate was the feed rate, with a percentage contribution of 40.36%. The estimation of the optimum strain rate and confirmation tests proved that the Taguchi method could successfully optimize the working conditions to obtain the desired maximum strain rate.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00315-0     

Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs

It is especially significant for a manufacturing company to select a proper maintenance policy because maintenance impacts not only on economy,reliability and availability but also on personnel safety.This article reports on research in the backlash error data interpretation and compensation for intelligent predictive maintenance in machine centers based on artificial neural networks(ANNs).The backlash error,measurement system and prediction methods are analyzed in detail.The result indicates that it is possible to predict and compensate for the backlash error in both forward and backward directions in machine centers.     

Application of artificial neural network to study the performance of multi-gravity separator (MGS) treating iron ore fines

The present article describes an attempt made to study the possibility of beneficiating low-grade iron ore fines of Barbil Area of Orissa state, India, using multi-gravity separator (MGS) after grinding the ?10 mm fines to < 75 micron size and prepare a pellet feed of 65% Fe content. For the performance analysis, an artificial neural network (ANN) mathematical modeling approach was attempted. A three-layer feedforward neural network with a backpropagation method has been adopted, considering the three significant parameters of MGS, mainly drum inclination, drum speed, and shake amplitude, were varied and the results were evaluated for grade, recovery, and separation efficiency. The results of beneficiation studies showed that good recovery of hematite is possible with simultaneous increase in Fe(T) grade from 50.74% to 65.26% with 71.25% recovery. The predicted value obtained by ANN shows good agreement with the experimental values.     

基于稳态ANN模型和模糊PD控制的变速直膨式空调系统温湿度同时控制方法

直膨式空调系统温湿度控制过程高度耦合,造成传统方法下室内空气温湿度的同时精确控制较难实现。本研究基于模糊PD控制逻辑,利用稳态ANN模型建立新型温湿度同时控制算法,根据实时温湿度的控制误差计算所需的显、潜冷量,输出风机、压缩机转速,实现温湿度的同时控制。针对建立的新型控制算法,进行了控制性能验证实验,命令跟随实验结果表明,在新型控制算法的控制下,空气干球与湿球温度设定值改变后在720 s内被稳定在新的设定值,误差在±0.2℃以内;负荷干扰实验结果表明,在有负荷扰动的条件下,控制器在干湿球温度偏离设定值0.5℃后迅速响应,并在600 s内将干湿球温度控制到设定值,波动不超过0.2℃。因此本文建立的新型控制方法可以实现使用变速直膨式系统进行室内空气温湿度同时控制。     

Prediction of Mechanical Properties of Hot Rolled, Low-Carbon Steel Strips Using Artificial Neural Network

Artificial intelligence (AI) has been used in many application areas of engineering. In the present work, an artificial neural network (ANN)-based model is developed to predict the mechanical properties such as yield strength (YS), ultimate tensile strength (UTS), and elongation (EL) of the hot rolled (HR) steel strips/coils. Different network topologies have been investigated to find the appropriate network to simulate the problem. Finally, the best network was chosen as the one with 7-19-3 topology-7 neurons in the input layer, 19 in the hidden layer, and 3 in the output layer. It has been shown that a single network with three output neurons is sufficient to address the problem. The model has been tested with 121 unknown patterns, and the match between the actual values and the simulated ones is found to be very good. The model has been implemented in the hot strip mill (HSM) of Tata Steel, India. This paper describes the methodology adopted to develop the model.     

Modeling and Analysis of Machinability Evaluation in the Wire Electrical Discharge Machining (WEDM) Process of Aluminum Oxide-Based Ceramic

The wire electrical discharge machining (WEDM) allowed success in the manufacture of the hard, fragile, and materials difficult to cut, especially for electroconductive ceramic materials. In this study, the mathematical models of material removal rate (MRR) and surface roughness (SR) used for the machinability evaluation in the WEDM process of aluminum oxide-based ceramic material (Al₂O₃ + TiC) have been carried out. The experimental plan adopts the face centered central composite design (CCD). The mathematical models using the response surface methodology (RSM) are developed so as to investigate the influences of four machining parameters, including the peak current, pulse on time, duty factor, and wire speed, on the performance characteristics of MRR and SR. It has been proved that the proposed mathematical models in this study would fit and predict values of the performance characteristics, which would be close to the readings recorded in experiment with a 95% confidence level. The significant parameters that critically affect the performance characteristics are examined.     

改进MDSMOTE与PSO-SVM在汽车组合仪表分类预测中的应用

汽车组合仪表生产过程中质检项目多且检测时间长,这在一定程度上制约了其生产效率的进一步提升。为此,提出一种基于改进最远点合成少数类过采样技术（max distance synthetic minority over-sampling technique,MDSMOTE）的支持向量机（support vector machine, SVM）分类预测方法。首先,结合专家经验对汽车组合仪表的原始生产数据进行特征筛选,并在MDSMOTE中引入类不平衡率I_R,以对所筛选的特征数据进行扩充;然后,利用粒子群优化（particle swarm optimization, PSO）算法对SVM的误差惩罚因子C和核函数参数γ进行优化;最后,建立优化的SVM分类预测模型,并对汽车组合仪表进行分类。通过与其他分类预测模型在不同数据集上的预测结果进行对比可知,基于改进MDSMOTE的SVM分类预测模型的准确率、F值和几何平均值等评价指标均优于其他模型。所提出方法在汽车仪表产品分类上表现出较强的泛化能力和稳定性,可为仪表制造企业生产效率的提升提供有效参考。     

Preparation and evaluation of high drug content particles

To determine how to prepare high drug content particles using a Wurster fluidized bed to determine realizing the miniaturization of solid dosage forms, aspirin was selected as the model drug and granulated without any additive. In this study, the emphasis was on evaluating the key operation factors of airflow rate and atomizing flow volume. The properties of the resulting particles, such as the average diameter, particle strength, appearance, and compressibility using different airflow rates and atomizing flow volumes, were investigated. Furthermore, detailed optimization of the operation conditions was conducted by artificial neural network (ANN) analysis. The relationship between the controlling factors (powder supplied, concentration of spray liquid, the amount of consumed spray liquid, and spray rate) and the response variables (product yield, median diameter, angle of repose, and degradation of aspirin) was investigated after evaluating the airflow rate and atomizing flow volume effects. The resulting granules under optimum operation conditions showed excellent physicochemical properties such as particle size uniformity, flowability, and compressibility.     

Prediction of the concrete compressive strength by means of core testing using GMDH-type neural network and ANFIS models

Assessment of insitu concrete strength by means of cores cut from hardened concrete is accepted as the most common method, but may be affected by many factors. Group method of data handling (GMDH) type neural networks and adaptive neuro-fuzzy inference systems (ANFIS) were developed based on results obtained experimentally in this work along with published data by other researchers. Genetic algorithm (GA) and singular value decomposition (SVD) techniques are deployed for optimal design of GMDH-type neural networks. Samples incorporated six parameters with core strength, length-to-diameter ratio, core diameter, aggregate size and concrete age considered as inputs and standard cube strength regarded as the output. The results show that a generalized GMDH-type neural network and ANFIS have great ability as a feasible tool for prediction of the concrete compressive strength on the basis of core testing. Moreover, sensitivity analysis has been carried out on the model obtained by GMDH-type neural network to study the influence of input parameters on model output.     

Optimization of material removal rate and surface roughness during electric discharge machining of ultra-fine grained Al6082 using Taguchi technique

In the current work, the statistical analysis of various electric discharge machining parameters on Al6082 ultra-fine grained aluminium alloy using Taguchi method has been presented. Repetitive corrugation and straightening (RCS) method was employed to obtain ultra-fine grained aluminium alloy. The electric discharge machining studies were carried out for test variables – pulse off time, pulse on time and current (I). The specimens were machined in dielectric medium with current range of 3 A to 9 A in step of 3 A. Machining features of the samples analysed statistically by adopting the Taguchi's - design of experiments (DOE) methodology. Impact of parameters on material removal rate (MRR) and surface roughness (SR) were examined via signal-to-noise ratio (S/N ratio, expressed in decibel, dB) as well as analysis-of-variance (ANOVA). Outcomes disclose that every selected response explicitly surface roughness (SR) and material removal rate was significantly influenced by parameters. The material removal rate was found to rise with discharge current and decrease with the duration of pulse on time and the duration of pulse off time. On the other hand, the surface roughness increased with increase in peak current and decreased with pulse on time and pulse off time especially. The machining mechanisms were examined by scanning electron microscopy.     

融合时频域特征的舰船识别方法及实验研究

文章提出了一种融合舰船辐射噪声时频域特征的识别方法,将舰船辐射噪声的线谱特征和线性预测倒谱特征作为输入,分别利用反向传播（Back Propagation,BP）神经网络进行训练、降维及初步判别,并采用加权投票方式,引入置信度算法和拒判机制实现决策级融合识别。实验结果表明,对比基于舰船单一特征的识别方法,利用舰船辐射噪声时频域特征的互补性进行融合识别,减小了单一识别方法误判对总识别率的影响,具有较强的鲁棒性,可有效提高对目标的识别率。     

Back analysis of model parameters in geotechnical engineering by means of soft computing

In this paper, a parameter identification (PI) method for determination of unknown model parameters in geotechnical engineering is proposed. It is based on measurement data provided by the construction site. Model parameters for finite element (FE) analyses are identified such that the results of these calculations agree with the available measurement data as well as possible. For determination of the unknown model parameters, use of an artificial neural network (ANN) is proposed. The network is trained to approximate the results of FE simulations. A genetic algorithm (GA) uses the trained ANN to provide an estimate of optimal model parameters which, finally, has to be assessed by an additional FE analysis. The presented mode of PI renders back analysis of model parameters feasible even for large‐scale models as used in geotechnical engineering. The advantages of theoretical developments concerning both the structure and the training of the ANN are illustrated by the identification of material properties from experimental data. Finally, the performance of the proposed PI method is demonstrated by two problems taken from geotechnical engineering. The impact of back analysis on the actual construction process is outlined. Copyright © 2003 John Wiley & Sons, Ltd.     

Effects of process parameters on fatigue behavior and surface integrity of tool steel produced by electrical discharge turning

Electrical discharge machining is used in the production of countless parts with complex geometries and micro dimensions, from many elements of industrial molds to parts of motors and pumps. Also, most of these parts are cylindrical and it is always more meaningful to study their rotational fatigue behavior to predict their response during their operation. This study concentrated on the impacts of machining parameters on the surface quality and fatigue behavior of tool steel shaped by electrical discharge turning. The results based on Taguchi methodology have shown that discharge current affects R_a and R_z more, and pulse duration more affects the mean spacing of profile irregularities, S_m. As a result of the heat affected depth in the machined region, which changes in proportional with the processing parameters, the microhardness decreased from the sample surface to the core. The maximum hardness was measured at current of 12 A, pulse duration of 3 μs and pulse interval of 7 μs. According to the fatigue tests, it was found that the fatigue life decreased with the increase in R_z and S_m values. Moreover, high microhardness and thick recast layer reduced the fatigue strength of the samples with relatively smooth surface topography.