刀具磨损

基于RBF网络的刀具磨损状态预测技术研究

Study on Prediction of The State of Tool Wear Based on Rbf Network

刀具磨损的偏最小二乘回归分析与建模

Partial Least Squares Regressive Analysis and Modeling for Tool Wear

CNC车削中刀具磨损实时监控的试验研究

Study of Tool Wear On-line Monitoring in CNC Turning

超声振动切削SiCP/Al复合材料的刀具磨损形态研究

Study on Form of Tool Wear with Ultrasonic Vibration Cutting SiC_P / Al Composites

SiCP/2024复合材料切削力与刀具磨损的试验研究

Experimental study on the cutting force and wear of lathe tool for SiC p / 2024 Al Composite

FMS环境下刀具磨损寿命预测研究

A study of the methods of controlling tool wear life prediction in FMS

利用小波包分解技术对信号进行分析，得到有效的特征量作为BP神经网络的输入样本，并对网络进行学习训练，完成对刀具磨损状态的有效识别。

The selected features are then considered as inputs to BP neural network to complete recognition of the status of the cutting tool .

基于Hough变换的刀具磨损监测加工表面纹理特征提取

Feature Extraction of Machined Surface Texture for Tool Wear Monitoring Based on Hough Transform

基于Lipschitz指数的刀具磨损补偿问题的研究

Study on tool wear compensation based on lipschitz index

CBN刀具磨损机理的研究

A Study of Wear Mechanism of CBN Tools

PCD刀具磨损形式分析

Analysis of Wearing Form of PCD Tool

ZrO2/CePO4复合陶瓷钻削试验中刀具磨损机理的研究

Study on Wear Mechanism of Twist Drill in Machining ZrO_2 / CePO_4 Composite Ceramics

工件表面的塑性变形在刀具磨损整个过程中都会发生，加工表面白层在刀具后刀面磨损剧烈时产生，但X射线分析没有发现相变。

Surface plastic deformation always occurs during the whole tool wear process . The drastic wear of the flank surface on cutting tool produces white layer in workpiece surface .

自动监测刀具磨损、分析刀具磨损状况是FMS和CIMS中迫切需要解决的问题。

Auto-monitoring of the tool wear and analysis on the wearing status have become the exigent problems in FMS and CIMS .

采用试验数据，对切削数据库中刀具磨损等曲线用Matlab数值计算及图形处理软件与Visualc++混合编程进行可视化图形处理，得到能够脱离Matlab环境运行的曲线图形模块的方法。

Using Matlab and Visual C + + to process wearing curves in cutting database , we can get a method of running visually graphics database without the environment of Matlab .

考察了Al2O3短纤维和C短纤维含量、纤维位向、钻削速度及进给量对钻削力、刀具磨损和钻削精度的影响，并进行了分析。

The influences of quantity used in cutting and orientation of fiber on drilling force , tool wear and drilling precision were investigated , and the mechanism was analyzed .

最后根据提取的特征参数，建立基于人工神经网络的RBF神经网络系统来对刀具磨损状态进行识别。

Finally , according to the feature extraction , we build tool condition monitoring system based on RBF neural network to identify the tool wear state .

预测滚刀寿命，根据刀具磨损规律把握最好的换刀时机对隧道掘进机（TBM）来说是极其重要的。

It is very important to change the cutter at the right time for TBM according to the cutter life prediction .

介绍了以VC为平台设计开发的一套基于切削力信号分析与处理的刀具磨损实时监测和补偿系统。

Using VC as a design platform , a real-time detecting and compensation system for tool wear based the analyzing and disposing for signals of cutting force is introduced .

最后用EDS分析了刀具磨损表面及切屑的成份，并观察了刀具的磨损形貌。

Last , EDS analysis of surface composition of cutters and cuttings was done . The wear patterns of cutters were also observed .

以两种具有不同数学特性的智能识别模型ANN和SVM，分别对刀具磨损状态进行识别，并将识别结果进行融合得出最终决策。

Two intelligent models with different mathematical characteristics ( ANNs and SVM ) are applied separately to recognize tool wear states and the results are fused to make the final decision .

随着FMS、CIMS等自动化技术的发展，迫切需要新型、实用、可靠的刀具磨损监控系统。

With the development of FMS , CIMS and other automation techniques , the new tool wears monitoring system which is more practical and reliable in critical demand .

在提取作为刀具磨损特征量的AR模型参数时，考虑了切削用量对模型参数的影响，提出了特征量选取的准则，使所提取的特征量更加实用化。

The influence of cutting parameters was considered when the features sensitive to tool flank wear are extracted from the time series AR model . The principle of feature extraction was proposed .

实际切削加工时，选用的切削参数过低，刀具磨损厉害是制约高效率和低成本铣削SY钢的重要因素。

The low cutting parameters and serious tool wear are key factors constraining the high-efficiency , low-cost while milling the SY steel .

研究了用立方氮化硼（CBN）刀片车削粉末高温合金的刀具磨损特征及刀具磨损机理。

The feature and the mechanism of tool wear for turning powder superalloy with cubic boron nitride ( CBN ) bite are researched in this paper .

介绍了刀具磨损状态的检测方法和分数布朗运动（FBM）的基本理论。

The methods of tool 's state monitoring and the basic theory of fractional Brown movement ( fBm ) were introduced .

本文将计算机视觉检测（AVI）技术应用于刀具磨损的检测。

This article introduces a new computer vision inspection technology , Automated Visual Inspection ( AVI ), which is used in tool wear inspection .

在车铣加工中心上，分别采用硬质合金和TiN涂层硬质合金刀片，对铝合金和不锈钢工件进行了车铣加工的刀具磨损试验，研究分析了车铣刀具的磨损和破损特征。

Separately adopted carbide and TiN coated carbide to machine aluminum alloy and stainless steel , a series of turn-milling tool wear experiments had been done on a turn-milling machining center to analyze the tool wear and fracture .

着重讨论了钛合金TC4高速铣削过程铣削力的问题，研究了切削参数、刀具磨损及刀具材料等对铣削力的影响。

The effects of cutting parameters , cutter wears and materials on cutting force are also focused on in the paper .

TBM刀具磨损的预测和异常磨损识别方法的确定，对于保障TBM的安全掘进以及提高其利用率和经济性具有重要意义。

In order to decrease the cutter consume and the time loss of machine standing , to enhance the utilization of TBM , the abnormal cutter wear of TBM needs to be recognized in time .