Three dimensional modeling and finite element simulation of a generic end mill

The geometry of cutting flutes and the surfaces of end mills is one of the crucial parameters affecting the quality of the machining in the case of end milling. These are usually represented by two-dimensional models. This paper describes in detail the methodology to model the geometry of a flat end mill in terms of three-dimensional parameters. The geometric definition of the end mill is developed in terms of surface patches; flutes as helicoidal surfaces, the shank as a surface of revolution and the blending surfaces as bicubic Bezier and biparametric sweep surfaces. The proposed model defines the end mill in terms of three-dimensional rotational angles rather than the conventional two dimensional angles. To validate the methodology, the flat end milling cutter is directly rendered in OpenGL environment in terms of three-dimensional parameters. Further, an interface is developed that directly pulls the proposed three-dimensional model defined with the help of parametric equations into a commercial CAD modeling environment. This facilitates a wide range of downstream technological applications. The modeled tool is used for finite element simulations to study the cutting flutes under static and transient dynamic load conditions. The results of stress distribution (von mises stress), translational displacement and deformation are presented for static and transient dynamic analysis for the end mill cutter flute and its body. The method described in this paper offers a simple and intuitive way of generating high-quality end mill models for use in machining process simulations. It can be easily extended to generate other tools without relying on analytical or numerical formulations.     

A mathematical model for simulating and manufacturing ball end mill

The performance of ball end mill cutters in cutting operations is influenced by the configuration of the rake and clearance faces in the ball component. From the mathematical design of a cutting edge curve, the rake face can be defined by the rake angle and the width of the rake face at each cross section along the cutting edge. We propose the fundamental conditions that must govern the engagement between the grinding wheel and the designed rake face in order to avoid interference while machining a ball end mill. As a result, a new mathematical model for determining the wheel location and a software program for simulating the generation of the rake face of a ball end mill are proposed. In addition, methods for grinding the clearance face in both concave and flat-shapes are introduced. The flute surface generated by a disk wheel during the grinding process is determined on the basis of a tangency condition. The results of the experiment and the simulation are compared to validate the proposed model.     

Simulation of micro-patterns engraved by grinding process with screw shaped wheel

Grinding is an important means of realizing precision and ultra-precision machining of workpiece surface. The surface patterning of workpiece directly affects its mechanical properties such as friction, wear, contact stiffness, fitting property. Therefore, prediction of the geometry of the workpiece surface is very important to evaluate the workpiece quality to perform mechanical function accurately. In this paper using MATLAB simulation, prediction for the geometry pattern of the workpiece according to the developed shape of the grinding wheel dressed by thread cutting was studied. The model for the geometry of the grinding wheel surface was first developed and subsequently, a new simulation model for surface pattern by grinding process was established. The simulation results could be used to optimize the grinding process and to improve the workpiece surface quality or predict the surface pattern by given grinding parameters.     

An EKF observer to estimate semi-autogenous grinding mill hold-ups

A non-linear observer model of a semi-autogenous grinding mill is developed. The observer model distinguishes between the volumetric hold-up of water, solids, and the grinding media in the mill. Solids refer to all ore small enough to discharge through the end-discharge grate, and grinding media refers to the rocks and steel balls. The rocks are all ore too large to discharge from the mill. The observer model uses the accumulation rate of solids and the mill's discharge rate as parameters. It is shown that with mill discharge flow-rate, discharge density, and volumetric hold-up measurements, the model states and parameters are linearly observable. Although instrumentation at the mill discharge is not yet included in industrial circuits because of space restrictions, this study motivates the benefits to be gained from including such instrumentation. An extended Kalman filter is applied in simulation to estimate the model states and parameters from data generated by a semi-autogenous mill simulation model from literature. Results indicate that if sufficiently accurate measurements are available, especially at the discharge of the mill, it is possible to reliably estimate grinding media, solids and water hold-ups within the mill. Such an observer can be used as part of an advanced process control strategy.     

Soft sensor for parameters of mill load based on multi-spectral segments PLS sub-models and on-line adaptive weighted fusion algorithm

The parameters of mill load (ML) not only represent the load of the ball mill, but also determine the grinding production ratio (GPR) of the grinding process. In this paper, a novel soft sensor approach based on multi-spectral segments partial least square (PLS) model and on-line adaptive weighted fusion algorithm is proposed to estimate the ML parameters. At first, frequency spectrums of the shell vibration acceleration signals are obtained. Then the PLS sub-models are constructed with the low, medium and high frequency spectral segments. At last, the PLS sub-models are fused together with a new on-line adaptive weighted fusion algorithm to obtain the final soft sensor models. This soft sensor approach has been successfully applied in a laboratory-scale wet ball mill grinding process.     

Comprehensive aircraft configuration design tool for Integrated Product and Process Development

This paper describes an efficient aircraft geometry design tool which is necessary for design and analysis applications through Integrated Product and Process Development (IPPD). The design process decomposes aircraft geometry into several components to represent it accurately and realistically with a reduced number of shape control parameters. For this purpose, several configuration representation algorithms are thoroughly investigated and discussed. The proposed configuration generation algorithm employs the super ellipse equation with simple analytic distribution functions, Class function/Shape function Transformation (CST) and can represent and manipulate complex shapes accurately with a small number of control parameters. A model of aircraft geometry, represented in this approach, can be applied to conceptual and preliminary stages of aircraft design and development with realistic and accurate configuration data. A Parameter-based Comprehensive Aircraft Design (PCAD) tool which implements a geometry generation process for aircraft design and optimization using customization of commercial computer-aided design software (CATIA V5) and the Product Data Management (Enovia Smarteam). The proposed configuration design tool could be especially efficient when automation, flexibility and rapid changes of geometry are required in a short time and with low computational resources.     

Modelling and prediction of copper concentration of a grinding process

The mathematical model of a grinding process in copper concentrator is presented. With the aid of a dynamic model and a Kalman filter the copper concentration of the output of this process is predicted. The process includes ball mill, autogenous mill and separator. The autogenous mill is working as a secondary grinding mill for the underflow coming from the classifier.To obtain the mathematical model of grinding process several practical tests including screen analysis, puls and impulse tests, were performed. It has been shown that the dynamics of one ball mill can be described with two perfect mixers and plug flow in series and the autogenous mill with two perfect mixers in series. A linear matrix vector model has been used in a Kalman filter for estimating the copper concentration in the input flow of the flotation process.     

球面铣刀修磨几何模型研究

文中给出球面铣刀的制造模型,探讨了运用同一机构磨制邻近规格的不同球面铣刀的可行性问题。为了充分利用现有的磨制机构,提出了斜等距曲面的概念及相应公式,介绍了它在球面铣刀修磨模型中的应用,并给出与制造相关的几何模型,最后用实例验证了模型的可行性与可靠性。该模型为实现复杂刀具的ＣＡＤ／ＣＡＭ提供了方法模型。     

Predicting mill load using partial least squares and extreme learning machines

Online prediction of mill load is useful to control system design in the grinding process. It is a challenging problem to estimate the parameters of the load inside the ball mill using measurable signals. This paper aims to develop a computational intelligence approach for predicting the mill load. Extreme learning machines (ELMs) are employed as learner models to implement the map between frequency spectral features and the mill load parameters. The inputs of the ELM model are reduced features, which are extracted and selected from the vibration frequency spectrum of the mill shell using partial least squares (PLS) algorithm. Experiments are carried out in the laboratory with comparisons on the well-known back-propagation learning algorithm, the original ELM and an optimization-based ELM (OELM). Results indicate that the reduced feature-based OELM can perform reasonably well at mill load parameter estimation, and it outperforms other learner models in terms of generalization capability.     

基于ASOS-ELM的球磨机负荷参数软测量系统设计

球磨机负荷参数是决定磨矿工作质量和效率的重要指标,为了提升球磨机负荷参数软测量系统的运行性能,降低综合测量误差,利用ASOS-ELM算法,从硬件和软件两个方面,设计球磨机负荷参数软测量系统。加设球磨机工作数据采集器,改装处理器与输出显示屏,扩大存储器空间,利用电源电路连接硬件设备,完成硬件系统的设计。根据球磨机的组成结构和工作原理,构建球磨机数学模型,在该模型下,采集球磨机不同工况下的振动信号。利用ASOS-ELM算法,提取振动信号特征,得出球磨机球负荷、料负荷、水负荷参数的软测量结果,实现球磨机负荷参数软测量系统设计。通过系统测试实验结果表明,设计系统负荷参数的综合测量误差降低了约0.064kg,且系统运行性能得到明显提升。     

基于CCD传感器的砂轮轮廓测量系统设计

为实现激光修整砂轮过程中的砂轮轮廓测量,分析了激光三角法的测量原理,并选用基于三角法的CCD激光位移传感器、精密电控移动平台和数字信号处理器(DSP)等搭建了砂轮轮廓测量系统。系统采用DSP设计传感器控制和数据处理电路,以实现其在激光修整砂轮过程中的应用。传感器测量精度验证实验和激光修整砂轮实验结果表明:CCD位移传感器的测量精度达7μm,所设计的轮廓测量系统能准确测量出砂轮轮廓,并成功应用于激光修整砂轮系统,具有良好的实用价值和应用前景。     

Disturbance observer based multi-variable control of ball mill grinding circuits

Ball mill grinding circuits are essentially multi-variable systems characterized with couplings, time-varying parameters and time delays. The control schemes in previous literatures, including detuned multi-loop PID control, model predictive control (MPC), robust control, adaptive control, and so on, demonstrate limited abilities in control ball mill grinding process in the presence of strong disturbances. The reason is that they do not handle the disturbances directly by controller design. To this end, a disturbance observer based multi-variable control (DOMC) scheme is developed to control a two-input-two-output ball mill grinding circuit. The systems considered here are with lumped disturbances which include external disturbances, such as the variations of ore hardness and feed particle size, and internal disturbances, such as model mismatches and coupling effects. The proposed control scheme consists of two compound controllers, one for the loop of product particle size and the other for the loop of circulating load. Each controller includes a PI feedback part and a feed-forward compensation part for the disturbances by using a disturbance observer (DOB). A rigorous analysis is also given to show the reason why the DOB can effectively suppress the disturbances. Performance of the proposed scheme is compared with those of the MPC and multi-loop PI schemes in the cases of model mismatches and strong external disturbances, respectively. The simulation results demonstrate that the proposed method has a better disturbance rejection property than those of the MPC and PI methods in controlling ball mill grinding circuits.     

Feature extraction and selection based on vibration spectrum with application to estimating the load parameters of ball mill in grinding process

Feature extraction and selection are important issues in soft sensing and complex nonlinear system modeling. In this paper, a new feature extraction and selection approach based on the vibration frequency spectrum is proposed to estimate the load parameters of wet ball mill in grinding process. This approach can simplify the modeling process. In this study, the vibration acceleration signals are first transformed into the frequency spectrum by fast Fourier transform (FFT). Then the candidate features are extracted and selected from the frequency spectrum, which include characteristic frequency sub-bands, spectral principal components, and features of local peaks. Mutual information, spectral segment clustering and kernel principal component analysis are used to obtain these candidate features. Finally, a combinatorial optimization method based on adaptive genetic algorithm selects the input sub-set and parameters of the soft sensor model simultaneously. This approach is successfully applied in a laboratory scale wet ball mill. The test results show that the proposed approach is effective for modeling the parameters of mill load.     

起重机轮轨接触分析的参数化建模方法及其应用

为准确快速地对起重机钢制车轮踏面与轨道接触应力的分布状况进行有限元分析,运用ANSYS APDL参数化建模,建立常见的起重机车轮与轨道接触分析的数值模型。该模型通过输入必要的车轮几何参数、网格划分参数和载荷参数即可自行完成实体建模、网格划分、载荷施加和求解。实例应用结果证明整合几何参数化和网格划分参数化的建模方法可大大提高起重机轮轨接触分析效率。     

Remote robotic underwater grinding system and modeling for rectification of hydroelectric structures

A submersible grinding robot has been designed to automate the dam gate metallic structure repair process. In order to measure and control the amount of material removed during the process, an empirical approach for modeling the material removal rate (MRR) of the underwater grinding application is proposed and presented in this paper. The objective is to determine the MRR in terms of the process parameters such as cutting speed and grinding power over a range of variable wheel diameters. Experiments show that water causes drag and a significant loss of power occurs during grinding. An air injector encasing the grinding wheel has been prototyped, and it is shown that power loss can be reduced by up to 80%. A model, based on motor characterization and empirical relations among system and process parameters, is developed for predicting MRR which will be used for the robotic grinding control system. A validation is carried out through experiments, and confirms the good accuracy of the model for predicting the depth of cut for underwater grinding. A comparative study for dry and underwater grinding is also conducted through experiments and shows that the MRR is higher for underwater grinding than in dry conditions at low cutting speeds.     

一种实用的磨矿多模型预测控制策略研究

房矿过程是选矿过程中的重要一环,属多输入多输出系统,并存在严重的扰动变量(诸如硬度等)和时变参数.介绍了一种磨矿控制系统中实用的多变量动态矩阵控制的多模型控制策略.该方法基于不同的矿石硬度对球磨过程建立不同的阶跃模型进行多模型预测控制.仿真证实了该方法的有效性.     

基于群体平衡的球磨机粉磨过程动态仿真

为了进一步弄清球磨机粉磨过程的工艺特性,为过程参数设计、控制和优化工作提供依据,降低控制和优化系统实施的风险,节约设计、开发和调试的成本,根据群体平衡模型和特劳姆曲线建立了球磨机粉磨过程和空气选粉过程的数学模型,对球磨机内部的料流以及物料粒度分布情况进行模拟,、基于Matlab/Simulink仿真平台进行二次开发,采...     

Creep feed grinding optimization by an integrated GA-NN system

The present work is aimed to optimize creep feed grinding (CFG) process by an approach using integrated Genetic Algorithm-Neural Network (GA-NN) system. The aim of this creep feed grinding optimization is obtain the maximal metal removal rate (MRR) and the minimum of the surface roughness (R _a ). For optimization, metal removal rate is calculated with a mathematic formula and a Back Propagation (BP) artificial neural-network have been used to prediction of R _a . The parameters used in the optimization process were reduced to three grinding conditions which consist of wheel speed, workpiece speed and depth of cut. All of other parameters such as workpiece length, workpiece material, wheel diameter, wheel material and width of grinding were taken as constant. The BP neural network was trained using the scaled conjugate gradient algorithm (SCGA). The results of the neural network were compared with experimental values. It shows that the BP model can predict the surface roughness satisfactorily. For optimization of creep feed grinding process, an M-file program has been written in ‘Matlab’ software to integrate GA and NN. After generation of each population by GA, firstly, the BP network predicts R _a and then MRR has been calculated with mathematic formula. In this study, the importance of R _a and MRR is equal in the optimization process. By using this integrated GA-NN system optimal parameters of creep feed grinding process have been achieved. The obtained results show that, the integrated GA-NN system was successful in determining the optimal process parameters.     

Adaptive parameter optimization approach for robotic grinding of weld seam based on laser vision sensor

Automatic robot grinding technology has been widely applied in the modern manufacturing industry. A flexible abrasive belt wheel used to grind the weld can avoid burns on the base material and improve the processing efficiency. However, when the robot grinds a weld seam, the material removal depth does not coincide with the feed depth because of the soft contact and uneven weld height, affecting the weld seam surface uniformity. Given these problems, an adaptive parameter optimization approach for the robotic grinding of a weld seam was proposed based on a laser vision sensor and a material removal model. First, the depth of weld seam removal was obtained by a laser vision sensor based on triangulation in real-time. Then, a macroscopic material removal model considering flexible deformation was established to determine the relationship between the weld height and process parameters, and the model coefficient was experimentally fitted to ensure the accuracy and reliability of the model. In addition, the data of real-time interaction structure between the robot controller and grinding system were obtained and used to unsure that the rotational speed of the belt wheel increased in the convex part and decreased in the concave part, in order to obtain a uniform weld seam surface. Comparative experiments were performed to verify the effectiveness and superiority of the method, and experiments on the surface roughness and weld seam surface height difference were conducted to verify the universality of the method. Experimental results show that the residual height of the weld after grinding can be controlled within 0.2mm, and the maximum removal height difference can be controlled within 0.05mm. The surface roughness Ra of the weld after grinding could reach 0.408 µm.     

基于改进粒子群算法的RBF神经网络磨机负荷预测研究

磨机负荷是评价磨机运行状态和预测磨机行为的重要指标,针对粉磨机磨矿过程中负荷难以检测和不能准确判断负荷状态的问题,提出了一种基于改进型粒子群算法（Improved particle swarm optimization, IPSO）优化径向基神经网络（Radial Basis Function,RBF）参数的磨机负荷预测模型（IPSO-RBF）,使惯性权重因子在迭代过程中非线性下降,平衡局部搜索能力与全局搜索能力之间的矛盾,该算法能快速准确地找到最优解,提高粉磨机磨机负荷的预测精度。通过水泥厂的实测数据实验对比,结果表明,基于IPSO-RBF模型的预测精度最高,其预测结果与真实值相比较,均方根误差（Root Mean Square Error,RMSE）、均方误差（Mean Square Error,MSE）、平均绝对误差（Mean Absolute Error,MAE）、平均绝对百分比误差（Mean Absolute Percentage Error,MAPE）和决定系数（coefficient of determination,）分别为0.210 2、0.044 2、0.161 7、1.778%和0.978 2。