The form error prediction in side wall machining considering tool deflection

In this research, an effective method for the form error prediction in side wall machining with a flat end mill is suggested. The form error is predicted directly from the tool deflection without surface generation by cutting edge locus with time simulation. The developed model can predict the surface form error accurately about 300 times faster than the previous method. Cutting forces and tool deflection are calculated considering tool geometry, tool setting error and machine tool stiffness. The characteristics and the difference of generated surface shape in up milling and down milling are discussed. The usefulness of the presented method is verified from a set of experiments under various cutting conditions generally used in die and mold manufacturing. This study contributes to real time surface shape estimation and cutting process planning for the improvement of form accuracy.     

Hybrid Model of High Speed Machining Centre Headstock

A hybrid high-speed machining centre headstock model based on two computation methods: the finite element method and the finite difference method is presented. The model allows one to calculate precisely the headstock's indices on the basis of which its optimal operating characteristics can be determined. The presented modelling methods allow one to evaluate a design from thermal, stiffness and durability points of view. By way of illustration, the behaviour of three machining centre headstocks with: an electrospindle on rolling bearings, a conventional spindle and an electrospindle on aerostatic bearings are modelled using the hybrid model.     

A new thermomechanical model of cutting applied to turning operations. Part II. Parametric study

In Part I of this work, Molinari and Moufki [Int. J. Mach. Tools Manufact., this issue], an analytical model of three-dimensional cutting is developed for turning processes. To analyse the influences of cutting edge geometry on the chip formation process, global effects such as the chip flow direction and the cutting forces, and local effects such as the temperature distribution and the surface contact at the rake face have been investigated. In order to accede to local parameters, the engaged part in cutting of the rounded nose is decomposed into a set of cutting edge elements. Thus each elementary chip, produced by a straight cutting edge element, is obtained from an oblique cutting process defined by the corresponding undeformed chip section and the local cutting angles. The present approach takes into account the fact that for each cutting edge element the local chip flow is imposed by the global chip movement. The material characteristics such as strain rate sensitivity, strain hardening and thermal softening, the thermomechanical coupling and the inertia effects are considered in the modelling. A detailed parametric study is provided in this paper in order to analyse the effects of cutting speed, depth of cut, feed, nose radius and cutting angles on cutting forces, global chip flow direction and temperature distribution at the rake face. The influence of friction at the tool–chip interface is also discussed.     

Modeling of tool wear in drilling by statistical analysis and artificial neural network

The useful life of a cutting tool and its operating conditions largely control the economics of the machining operations. Hence, it is imperative that the condition of the cutting tool, particularly some indication as to when it requires changing, to be monitored. The drilling operation is frequently used as a preliminary step for many operations like boring, reaming and tapping, however, the operation itself is complex and demanding.

Back propagation neural networks were used for detection of drill wear. The neural network consisted of three layers input, hidden and output. Drill size, feed, spindle speed, torque, machining time and thrust force are given as inputs to the ANN and the flank wear was estimated. Drilling experiments with 8 mm drill size were performed by changing the cutting speed and feed at two different levels. The number of neurons in the hidden layer were selected from 1, 2, 3, …, 20. The learning rate was selected as 0.01 and no smoothing factor was used. The estimated values of tool wear were obtained by statistical analysis and by various neural network structures. Comparative analysis has been done between statistical analysis, neural network structures and the actual values of tool wear obtained by experimentation.     

Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites

The challenges of machining, particularly milling, glass fibre-reinforced polymer (GFRP) composites are their abrasiveness (which lead to excessive tool wear) and susceptible to workpiece damage when improper machining parameters are used. It is imperative that the condition of cutting tool being monitored during the machining process of GFRP composites so as to re-compensating the effect of tool wear on the machined components. Until recently, empirical data on tool wear monitoring of this material during end milling process is still limited in existing literature. Thus, this paper presents the development and evaluation of tool condition monitoring technique using measured machining force data and Adaptive Network-Based Fuzzy Inference Systems during end milling of the GFRP composites. The proposed modelling approaches employ two different data partitioning techniques in improving the predictability of machinability response. Results show that superior predictability of tool wear was observed when using feed force data for both data partitioning techniques. In particular, the ANFIS models were able to match the nonlinear relationship of tool wear and feed force highly effective compared to that of the simple power law of regression trend. This was confirmed through two statistical indices, namely r² and root mean square error (RMSE), performed on training as well as checking datasets.     

Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.     

基于Web的达梦数据库交互式查询工具的实现

本系统主要功能：对用户输入的单句或多句SQL语句进行处理,及时返回查询结果或者操作信息。本系统具有以下特点：采用主流的Web设计语言JSP设计客户端界面,用JDBC实现对数据库的无缝连接;使用JDBC扩展包中的行集来缓存结果集;使用javacc工具自动生成语法分析器;这些后台操作大部分采用了JavaBeans进行封装。     

Certification of software for real-time safety-critical systems: state of the art

This paper presents an overview and discusses the role of certification in safety-critical computer systems focusing on software, and partially hardware, used in the civil aviation domain. It discusses certification activities according to RTCA DO-178B “Software Considerations in Airborne Systems and Equipment Certification” and touches on tool qualification according to RTCA DO-254 “Design Assurance Guidance for Airborne Electronic Hardware.” Specifically, certification issues as related to real-time operating systems and programming languages are reviewed, as well as software development tools and complex electronic hardware tool qualification processes are discussed. Results of an independent industry survey done by the authors are also presented.     

Five-axis tool path generation for a flat-end tool based on iso-conic partitioning

Traditionally, for the flat-end tool, due to the intertwined dependence relationship between its axis and reference point, most 5-axis tool-path generation algorithms take a decoupled two-stage strategy: first, the so-called cutter contact (CC) curves are placed on the part surface; then, for each CC curve, tool orientations are decided that will accommodate local and/or global constraints such as minimum local gouging and global collision avoidance. For the former stage, usually simplistic “offset” methods are adopted to determine the cutter contact curves, such as the iso-parametric or iso-plane method; whereas for the latter, a common practice is to assign fixed tilt and yaw angle to the tool axis regardless the local curvature information and, in the case of considering global interference, the tool orientation is decided solely based on avoiding global collision but ignoring important local machining efficiency issues. This independence between the placement of CC curves and the determination of tool orientations, as well as the rigid way in which the tilt and yaw angle get assigned, incurs many undesired problems, such as the abrupt change of tool orientations, the reduced efficiency in machining, the reduced finishing surface quality, the unnecessary dynamic loading on the machine, etc. In this paper, we present a 5-axis tool-path generation algorithm that aims at alleviating these problems and thus improving the machining efficiency and accuracy. In our algorithm, the CC curves are contour lines on the part surface that satisfy the iso-conic property — the surface normal vectors on each CC curve fall on a right small circle on the Gaussian sphere, and the tool orientations associated to a CC curve are determined by the principle of minimum tilt (also sometimes called lead) angle that seeks fastest cutting rate without local gouging. Together with an elaborate scheme for determining the step-over distance between adjacent CC curves that seeks maximum material removal, the presented algorithm offers some plausible advantages over most existing 5-axis tool-path generation algorithms, particularly in terms of reducing the angular velocity and acceleration of the rotary axes of the machine. The simulation experiments of the proposed algorithm and their comparison with a leading commercial CAM software toolbox are also provided that demonstrate the claimed advantages.     

利用Photoshop CS制作心形相框

Photoshop是由Adobe公司开发的专业平面设计软件,具有强大的图像设计及处理功能,本文主要介绍如何利用中文Photoshop CS中的工具及菜单指令制作心形相框。