Kinematics and Wear of Tool Blades for Scrap Tire Shredding

The wear of tool blades for cost-effective scrap tire shredding is investigated. Rotary disk cutters are widely used for cutting scrap tires into small pieces. The hard, wear-resistant tool blades mounted on the periphery of disk cutters maintain a narrow gap between blades and generate the cutting action. The kinematics of the relative motion of two adjacent disk cutters is derived to model the overlap region on blades during cutting. The model predictions match well with the actual shapes of the worn regions on used tool blades. The wear of tool blades made of AISI D2 and CRU-WEAR (CW) tool steels for scrap tire shredding is evaluated. A coordinate measurement machine was used to measure the tool wear. The wear on the blade surface is not uniform. Regions with high wear rate are explained using the kinematics analysis. The CW blades show a lower wear rate, about half of that of D2 blades, and a potential choice for cost savings.     

Surface Texture Indicators of Tool Wear - A Machine Vision Approach

There has been much research on the automated monitoring of cutting tool wear. This research has tended to focus on three main areas that attempt to quantify the cutting tool condition: monitoring of specific machine tool parameters in order to infer tool condition, direct observations made on the cutting tool; and measurements taken from the chips produced by the tool. However, considerably less work has been performed on the development of surface texture sensors that provide information on the condition of the tool employed in machining the surface. A preliminary experimental study is presented for accomplishing this texture analysis using a machine vision-based sensor system. In particular, an investigation of the condition of a two-flute end mill used in a standard face milling operation is presented. The degree of tool wear is estimated by extracting three parameters from video camera images of the machined surface. The performance of three image-processing algorithms, in estimating the tool condition, is presented: analysis of the intensity histogram; image frequency domain content; and spatial domain surface texture.     

基于ADAMS的PRS-XY型混联机床机构运动学仿真分析

应用ADAMS软件对PRS-XY型混联机床机构进行运动学仿真分析,使用测量工具求得混联机构逆解,然后通过样条曲线和样条函数求得正解。借助于仿真软件快速准确地求出运动学正逆解,为实际的样机调试提供了有意义的借鉴。     

基于ADAMS的PRS—XY型混联机床机构运动学仿真分析

应用ADAMS软件对PRS—XY型混联机床机构进行运动学仿真分析,使用测量工具求得混联机构逆解,然后通过样条曲线和样条函数求得正解。借助于仿真软件快速准确地求出运动学正逆解,为实际的样机调试提供了有意义的借鉴。     

基于神经网络多传感器融合的刀具摩损定量监测的研究

研究了前馈神经网络（ＦＮＮ）的自构造型学习算法，提出了基于神经网络多传感器融合的一般结构及刀具磨损监测方法，讨论了多传感器的选择、多传感器信号的采集与预处理以及多传感器信号的特征选择与正规化处理，并就铣削过程的刀具磨损监测进行了实验研究，结果表明，所提出的方法可获得９３％的识别率。     

一种减轻纯电动中巴前悬轮胎磨损的优化方法

纯电动中巴悬架系统通常以传统中巴悬架为设计基础,但由于其质量及外形结构的变化,易产生轮胎磨损严重等问题.以某纯电动中巴的双横臂前悬架为对象,以减轻轮胎磨损为目标,对其硬点作灵敏度分析,辨识影响悬架性能的关键参数.采用RBF构造响应面,通过遗传算法寻优,对悬架的外倾角、前束角及轮心侧向位移量进行多目标综合优化.结果表明,悬架性能获得显著提升,有效减轻轮胎磨损.     

电火花铣削加工的电极损耗补偿

对电火花铣削加工中电极损耗状态的获得及其补偿技术进行了分析研究,提出了电极损耗的各种补偿策略。     

Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic appli-cations of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bot-tom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regressior layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effective-ness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.     

3-PTT滑块式并联钢锭修磨机床的开发

结合“863”项目实施,开发一种非常适用于我国钢铁企业钢锭修磨N3-PTT并联滑块式机床,该机床运动方程简单,工作空间大,采用PLC作为主控制系统,编程简单,性能可靠,成本低。     

基于神经网络的多特征融合刀具磨损量识别

采用切削力信号监测钻削过程钻头的磨损量 ,分别从时域、频域提取了切削力信号的均值、方差、峭度系数和特定频段能量作为刀具磨损的特征信号 ,讨论了特征信号随着刀具磨损量增加的变化规律 ,并将各个特征信号构成的特征矢量输入多层反传神经网络进行融合 ,实现钻削过程刀具磨损量的智能识别。试验结果表明该方法能有效实现多特征融合 ,但识别精度和推广能力有待进一步提高     

基于多特征量的刀具磨损模糊判决研究

利用信号处理技术分析AE、Ac及切削力信号特征参数随刀具磨损的变化规律,并将这些特征参数组成反映刀具磨损状态的特征矢量。论文提出基于模糊理论的刀具磨损状态识别方法。试验证明,该方法能有效地判断刀具状态,比常用的利用神经网络进行磨损状态分析的方法更具有理论直观性与操作的时效性。     

刀具磨损状态综合识别系统

介绍一种以PC机为后台,以双单片机为前台的刀具磨损状态识别系统。并阐述了系统设计中的关键技术与硬软件工作原理。     

双前桥转向汽车轮胎异常磨损的“内因”与“外因”探析

分析了双前桥转向汽车轮胎异常磨损的"内因"和"外因",并提出了改进措施和探讨方向。     

Characterization of Tool Wear Measurement with Relation to the Surface Roughness in Turning

In turning, an accurate gauging of tool wear condition is an essential part of process control due to adverse effects on dimensional tolerance and surface finish quality. When the surface roughness is the primary concern, the conventional measure of tool wear is found to be imprecise because it provides very little information on the wear patterns in tool nose and flank. A tool wear model, developed in this study, represents the wear condition more comprehensively and accurately with relation to the surface roughness. Experimental results validate the model, showing 92% accuracy between the predicted surface roughness and the actual measurements.     

振动信号监测在刀具磨损故障诊断中的应用

以典型高档数控机床DL-20M H型车削加工中心为试验对象,采用加速度传感器对振动信号进行监测。信号分析过程中,应用时域分析、频域分析实现了刀具磨损量与振动信号的关联,解决了生产过程中由于刀具突然损坏导致的产品质量下降问题,从而降低生产成本。     

Characterization of Tool Wear Measurement with Relation to the Surface Roughness in Turning

Abstract

In turning, an accurate gauging of tool wear condition is an essential part of process control due to adverse effects on dimensional tolerance and surface finish quality. When the surface roughness is the primary concern, the conventional measure of tool wear is found to be imprecise because it provides very little information on the wear patterns in tool nose and flank. A tool wear model, developed in this study, represents the wear condition more comprehensively and accurately with relation to the surface roughness. Experimental results validate the model, showing 92% accuracy between the predicted surface roughness and the actual measurements.     

图像处理技术在刀具磨损检测中的应用

针对传统刀具磨损检测的缺点,设计了一套结合图像处理的方法来检测磨损的方案。首先对磨损图像进行滤波、二值化等处理,其次根据设计的检测磨损算法,得到刀具的磨损量。通过实验验证,本方法具有自动化程度高、测量速度快、测量误差小等优点,可以应用于实际生产中。     

信息融合的刀具磨损状态智能识别系统设计与研究

介绍了一种以PC机为后台,以80C196KC单片机为前台的刀具磨损状态识别系统。阐述了系统中多路信号采集装置硬软件工作原理与设计中的关键技术,以及具有智能识别功能的上位数据处理计算机的软件工作流程。     

车铣复合加工的刀具磨损强度分析

在车铣复合加工中心上,进行了车铣加工高强度钢工件材料的刀具磨损强度实验,分析了车铣切削用量对刀具磨损强度的影响.研究表明,在影响车铣刀具磨损的切削用量中,切削速度对车铣刀具的磨损强度影响最大.并以车铣刀具的磨损实验为基础,以切削速度为变量,建立了车铣高强度钢的刀具磨损强度分析模型.