A new approach to analysing machined surfaces by ball-end milling, part II:

This paper presents a procedure for the evaluation of maximum surface roughness using the ridges analysed in part 1 of this paper. Maximum surface roughness is predicted for the plane cutting mode of ball-end milling. Two types of cutting modes, i.e., unidirectional mode and bidirectional mode, are investigated. The various cutting conditions in plane cutting are simplified employing the concept of the path interval ratio, f_p/f_t. In addition, mathematical expressions for calculating maximum surface roughness have been described for each case. The predicted results show that the geometrical surface roughness of the bidirectional mode is usually larger than that of the unidirectional mode differing from the results of the conventional roughness model. Maximum roughness and the shapes of the cut remainder are affected by path interval, feedrate and cutting mode. Experiments are carried out at various cutting conditions for both cutting modes. A high level of agreement between the expected surface roughness and measured value confirms the validity of the proposed method.     

A new approach to analysing machined surfaces by ball-end milling, part I:

Since productivity and product quality are always regarded as important issues in manufacturing technologies, a reliable method for predicting machining errors is essential to meeting these two conflicting requirements. However, the conventional roughness model is not suitable for the evaluation of machining errors for highly efficient machining conditions. Therefore, a different approach is needed for a more accurate calculation of machining errors. This study deals with the geometrical surface roughness in ball-end milling. In this work, a new method, called the ridge method, is proposed for the prediction of the machined surface roughness in the ball-end milling process. In Part I of this two-part paper, a theoretical analysis for the prediction of the characteristic lines of the cut remainder are generated from a surface generation mechanism of a ball-end milling process, and three types of ridges are defined. The trochoidal trajectories of cutting edges are considered in the evaluation of the cut remainder. The predicted results are compared with the results of a conventional roughness model.     

深度网络与FSVM集成学习的卫星云图云分类

准确的云分类模型对气象监测有重要的意义,传统机器学习云分类模型依赖手工特征提取,容易受噪声数据影响,模型泛化能力较差.深度网络分类模型能自动学习图像深度特征,但是对于图像边缘与细节分类效果不佳.本文针对上述问题进行研究.首先提取Himawari-8卫星云图光谱特征、纹理特征用以训练模糊支持向量机(Fuzzy Suppo...     

BDLR: lncRNA identification using ensemble learning

Long non-coding RNAs (lncRNAs) play an important role in many life activities such as epigenetic material regulation, cell cycle regulation, dosage compensation and cell differentiation regulation, and are associated with many human diseases. There are many limitations in identifying and annotating lncRNAs using traditional biological experimental methods. With the development of high-throughput sequencing technology, it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning method. Based on the Bagging method and Decision Tree algorithm in ensemble learning, this paper proposes a method of lncRNAs gene sequence identification called BDLR. The identification results of this classification method are compared with the identification results of several models including Byes, Support Vector Machine, Logical Regression, Decision Tree and Random Forest. The experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61% in the human test set and 90.34% in the mouse for lncRNAs, which is more than the identification results of the other methods. Moreover, the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning.     

Optimum surface roughness prediction in face milling by using neural network and harmony search algorithm

This paper presents a new approach to determine the optimal cutting parameters leading to minimum surface roughness in face milling of X20Cr13 stainless steel by coupling artificial neural network (ANN) and harmony search algorithm (HS). In this regard, advantages of statistical experimental design technique, experimental measurements, analysis of variance, artificial neural network and harmony search algorithm were exploited in an integrated manner. To this end, numerous experiments on X20Cr13 stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness was created using a feed forward neural network exploiting experimental data. The optimization problem was solved by harmony search algorithm. Additional experiments were performed to validate optimum surface roughness value predicted by HS algorithm. The obtained results show that the harmony search algorithm coupled with feed forward neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.     

Optimization of milling parameters using artificial neural network and artificial immune system

The present paper is an attempt to predict the effective milling parameters on the final surface roughness of the work-piece made of Ti-6Al-4V using a multi-perceptron artificial neural network. The required data were collected during the experiments conducted on the mentioned material. These parameters include cutting speed, feed per tooth and depth of cut. A relatively newly discovered optimization algorithm entitled, artificial immune system is used to find the best cutting conditions resulting in minimum surface roughness. Finally, the process of validation of the optimum condition is presented.     

Specific cutting force coefficients modeling of end milling by neural network

In a high precision vertical machining center, the estimation of cutting forces is important for many reasons such as prediction of chatter vibration, surface roughness and so on. The cutting forces are difficult to predict because they are very complex and time variant. In order to predict the cutting forces of end-milling processes for various cutting conditions, their mathematical model is important and the model is based on chip load, cutting geometry, and the relationship between cutting forces and chip loads. Specific cutting force coefficients of the model have been obtained as interpolation function types by averaging forces of cutting tests. In this paper the coefficients are obtained by neural network and the results of the conventional method and those of the proposed method are compared. The results show that the neural network method gives more correct values than the function type and that in the learning stage as the omitted number of experimental data increase the average errors increase as well.     

Trajectory control of electro-hydraulic excavator using fuzzy self tuning algorithm with neural network

This paper presents the trajectory control of a 2DOF mini electro-hydraulic excavator by using fuzzy self tuning with neural network algorithm. First, the mathematical model is derived for the 2DOF mini electro-hydraulic excavator. The fuzzy PID and fuzzy self tuning with neural network are designed for circle trajectory following. Its two links are driven by an electric motor controlled pump system. The experimental results demonstrated that the proposed controllers have better control performance than the conventional controller. This paper was recommended for publication in revised form by Associate Editor Kyongsu Yi Le Duc Hanh received the B. S. degree in the department of Mechanical Engineering from Hochiminh City University of Technology in 2006, the M.Sc. degree in Mechanical and Automotive Engineering from University of Ulsan in 2008. His research interests are electro-hydraulic excavator, remote control, intelligent control. Kyoung Kwan Ahn received the B. S. degree in the department of Mechanical Engineering from Seoul National University in 1990, the M. Sc. degree in Mechanical Engineering from Korea Advanced Institute of Science and Technology (KAIST) in 1992 and the Ph.D. degree with the title “A study on the automation of out-door tasks using 2 link electro-hydraulic manipulator from Tokyo Institute of Technology in 1999, respectively. He is currently a Professor in the school of Mechanical and Automotive Engineering, University of Ulsan, Ulsan, Korea. His research interests are hybrid excavator, fluid power control, design and control of smart atuator using smart material, rehabilization robot and active damping control. He is a member of IEEE, ASME, SICE, RSJ, JSME, KSME, KSPE, KSAE, KFPS, and JFPS. Bao Kha Nguyen received the B. S. and M. S. degree from Hochiminh City University of Technology in 2001 and 2003, respectively, all in Automatic Control Engineering and the Ph.D. degree from University of Ulsan in 2006. His research interests focus on intelligent control, modern control theory and their applications, design and control of smart actuator systems. WooKeun Jo received the B.S. degree in the department of Mechanical and Automotive Engineering from University of Ulsan in 2007. And he matriculated M.S. at University of Ulsan. Currently, he’s syudying on it. His research interests focus on fluid control, welfare vehicle, mobile robot     

应用BP神经网络预测高速铣削表面粗糙度

表面粗糙度的预测是切削加工质量分析的重要研究方向,为了在保证铣削的同时预测加工表面的粗糙度、提高生产率,将人工神经网络技术应用于铣削加工领域。应用BP神经网络建立高速铣削加工表面粗糙度预测模型,将预报结果与试验真值进行对比验证,结果表明该方法能够得到较好的预测精度,对高速铣削参数的选择和表面质量的控制具有指导意义。     

Delamination analysis of the helical milling of carbon fiber-reinforced plastics by using the artificial neural network model

As carbon fiber-reinforced plastics are widely used in aeronautical and aerospace industries, the improvement of their processing quality is a crucial task. In recent years, helical milling, a brand new machining process that results in better hole quality with one-time machining, has been attracting increasing attention. Based on full factor experimental design, helical milling experiments were performed by using a special cutter. Using the data obtained from the experiments, the correlation between the delamination and the process parameters was established by developing an artificial neural network (ANN) model. MATLAB ANN Toolbox was used for modeling. The effects of the process parameters on delamination at the exit of the machined holes were analyzed by using this model and the predicted results. The significance of the process parameters in the improvement of the hole quality in helical milling was also assessed.     

The selection of milling parameters by the PSO-based neural network modeling method

During the past decade, polymer nanocomposites have emerged relatively as a new and rapidly developing class of composite materials and attracted considerable investment in research and development worldwide. An increase in the desire for personalized products has led to the requirement of the direct machining of polymers for personalized products. In this work, the effect of cutting parameters (spindle speed and feed rate) and nanoclay (NC) content on machinability properties of polyamide-6/nanoclay (PA-6/NC) nanocomposites was studied by using high speed steel end mill. This paper also presents a novel approach for modeling cutting forces and surface roughness in milling PA-6/NC nanocomposite materials, by using particle swarm optimization-based neural network (PSONN) and the training capacity of PSONN is compared to that of the conventional neural network. In this regard, advantages of the statistical experimental algorithm technique, experimental measurements artificial neural network and particle swarm optimization algorithm, are exploited in an integrated manner. The results indicate that the nanoclay content on PA-6 significantly decreases the cutting forces, but does not have a considerable effect on surface roughness. Also the obtained results for modeling cutting forces and surface roughness have shown very good training capacity of the proposed PSONN algorithm in comparison to that of a conventional neural network.     

Prediction of surface roughness in turning operations by computer vision using neural network trained by differential evolution algorithm

In recent years, the measurement of surface roughness of a workpiece plays a vital role since the roughness of a surface has a considerable influence on the product quality and the functional aspects. In this work, a differential evolution algorithm (DEA)-based artificial neural network (ANN) has been used for the prediction of surface roughness in turning operations. Cutting speed, feed rate, depth of cut, and average gray level of the surface image of workpiece, acquired by computer vision, were taken as the input parameters and surface roughness as the output parameter. The results obtained from the DEA-based ANN model were compared with the backpropagation (BP)-based ANN. It is found that the error percentage is very close, and it is also observed that the convergence speed for the DEA-based ANN is higher than the BP-based ANN.     

Force control of hybrid actuator using learning vector quantization neural network

Hydraulic actuators are important in modern industry due to high power, fast response, and high stiffness. In recent years, hybrid actuation system, which combines electric and hydraulic technology in a compact unit, can be adapted to a wide variety of force, speed and torque requirements. Moreover, the hybrid actuation system has dealt with the energy consumption and noise problem existed in the conventional hydraulic system. Therefore, hybrid actuator has a wide range of application fields such as plastic injection-molding and metal forming technology, where force or pressure control is the most important technology. In this paper, the solution for force control of hybrid system is presented. However, some limitations still exist such as deterioration of the performance of transient response due to the variable environment stiffness. Therefore, intelligent switching control using Learning Vector Quantization Neural Network (LVQNN) is newly proposed in this paper in order to overcome these limitations. Experiments are carried out to evaluate the effectiveness of the proposed algorithm with large variation of stiffness of external environment. In addition, it is understood that the new system has energy saving effect even though it has almost the same response as that of valve controlled system.     

双刀铣削较长齿条的研究及其应用

介绍为解决较长齿条的铣削,充分利用卧式铣床工作台的纵向行程,同时又能适用于多种型号的卧式铣床而进行的工装设计,并通过直接转动工作台纵向丝杠的简单分度法和双刀铣削显著提高生产率。     

基于实例的学习矢量量化神经网络诊断方法及其应用

基于CBR良好的可扩充性、可移植性和神经网络极强的分类能力,提出了基于实例的学习矢量量化神经网络诊断方法。该方法应用于机械故障诊断系统中,可以减小实例搜索空间,提高实例检索效率。论述了系统的设计方法和应用步骤。     

Adaptive modelling of the milling process and application of a neural network for tool wear monitoring

An adaptive signal processing scheme that uses a low-order autoregressive time series model is introduced to model the cutting force data for tool wear monitoring during face milling. The modelling scheme is implemented using an RLS (recursive least square) method to update the model parameters adaptively at each sampling instant. Experiments indicate that AR model parameters are good features for monitoring tool wear, thus tool wear can be detected by monitoring the evolution of the AR parameters during the milling process. The capability of tool wear monitoring is demonstrated with the application of a neural network. As a result, the neural network classifier combined with the suggested adaptive signal processing scheme is shown to be quite suitable for in-process tool wear monitoring     

Classification of impinging jet flames using convolutional neural network with transfer learning

Journal of Mechanical Science and Technology - Depending on the equivalence ratio and the Reynolds number, impinging jet flames exhibit several modes of thermoacoustic oscillation. In this study,...     

Application of neural network technique to high energy milling process for synthesizing ZnO nanopowders

An artificial-neural-network (ANN) model was developed to estimate the crystalline size of ZnO nanopowder as a function on the milling parameters such as milling times and balls to powder ratio. This nanopowder was synthesized by high energy mechanical milling and the required data for training were collected from the experimental results. The synthesized ZnO nanoparticles are characterized by X-ray diffraction (XRD) and scanning electron microcopy (SEM). It was found that artificial neural network was very effective providing a perfect agreement between the outcomes of ANN modeling and experimental results with an error by far better than multiple linear regressions. An optimization model and this experimental validation of the ball milling process for producing the nanopowder ZnO are carried out.     

Acoustic diagnosis of a pump by using neural network

A fundamental study for developing a fault diagnosis system of a pump is performed by using neural network. Acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. And the codes of pump malfunctions were selected as units of output layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. Neural network trained by acoustic signals can detect malfunction or diagnose fault of a given machine from the results.