刀具磨损原因分析及改善措施

在金属切削过程中,刀具存在不同形式的磨损.通过分析刀具磨损机理,提出改善刀具工作条件、减少刀具磨损、提高刀具寿命的方法.     

PCBN刀具切削蠕墨铸铁的刀具磨损机理模型

开发了一种基于PCBN超硬刀具切削蠕墨铸铁时的主要磨损方式的刀具磨损机理模型,用于预测不同工艺参数条件下的刀具磨损曲线。基于刀具磨损机理建立了相应的磨损率函数,利用切削理论获得后刀面接触区域的应力和平均温度,通过刀具磨损实验校正模型参数获得适用于PCBN超硬刀具切削蠕墨铸铁的刀具磨损机理模型。根据此模型,可以精确预测在不同工艺参数条件下的刀具磨损曲线,通过分析得到刀具磨损随着工艺参数增加而加快。     

微织构刀具连续磨损建模与仿真研究

建立切削过程中的微织构刀具连续磨损有限元模型一直是难点。为分析微织构刀具连续磨损问题,基于能量损失法,在有限元分析软件中建立了硬质合金刀具微切削Ti6Al4V钛合金材料过程中的微织构刀具连续磨损模型,并在刀具前刀面不同位置分别设计单个和多个微织构模型,分析微织构对刀具前刀面月牙洼磨损和后刀面磨损的影响。有限元分析结果表明,前刀面的微织构数量和位置对刀具的磨损程度有严重影响;刀具表面温度是影响月牙洼磨损深度的关键因素;而月牙洼磨损中心到切削刃刀尖的磨损与刀具的Mises应力有直接关系;影响刀具的后刀面磨损主要因素是刀具压力。     

基于车铣技术的刀具磨损和破损分析

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。研究表明，车铣铝合金的刀具磨损机理主要以刀具表层材料的黏结磨损为主，而车铣不锈钢的刀具磨损机理主要以刀具表层材料的疲劳-剥落磨损为主。车铣不锈钢时，刀具的损坏形式常常以微崩刃、前刀面的剥落和碎断等破损形态为主。     

PCD刀具加工铝合金刃口磨损与对策

对PCD刀具刃口磨损的形式进行了分析,确定PCD刀具在加工铝合金中磨损的主要形式为聚晶层破损,针对聚晶层破损这一磨损的方式,提出了应对策略。     

硬质合金刀具切削各向同性热解石墨磨损机理

针对各向同性热解石墨切削过程中刀具磨损过快的问题,采用硬质合金刀具进行了切削加工试验,研究了切削过程中硬质合金刀具的磨损形式、磨损过程以及刀具磨损对表面加工质量的影响。通过对试验结果的研究分析表明:硬质合金刀具磨损形式为后刀面磨损以及切削刃崩刃破损,刀具磨损机理为磨粒磨损。刀具的有效切削距离仅为250m左右,已加工表面分布着形状和深度大小不一的凹坑,表面加工质量差,硬质合金刀具作为各向同性热解石墨的切削刀具具有一定的局限性。     

高速铣削刀具磨损研究进展

介绍高速切削的特点、应用现状和加工时刀具遇到的问题,针对刀具磨损机理(刀具材料、切削参数、冷却方式、铣削方式)和刀具磨损监测等方面,综述了近几年高速铣削难加工材料时刀具磨损的研究进展,并对其发展趋势进行了讨论。     

切削奥氏体不锈钢的涂层刀具磨损机理探讨

通过不锈钢0Cr18Ni9切削加工的刀具寿命试验,探讨涂层刀具的磨损形态和机理,提出涂层刀具切削奥氏体不锈钢时刀具的磨损形态主要为边界磨损的观点,并验证了YBG202涂层刀具适合切削奥氏体不锈钢。     

PCBN刀具切削镍基高温合金刀具磨损的试验研究

通过PCBN刀具对镍基高温合金GH4169进行了车削试验,深入研究了切削时的刀具磨损形态及磨损原因,得出如下结论:通过试验观察看出刀具的磨损形态有前刀面磨损、后刀面磨损、沟槽磨损和刀具破损。其中沟槽磨损最为显著,是刀具失效的主要原因。沟槽磨损的产生原因主要有粘结磨损、扩散磨损和塑性侧流产生的切屑毛刺划擦作用,其中粘结磨损为磨损前期的主要原因,而扩散磨损是磨损后期的主要原因。     

纯钒车削刀具磨损表面形态及机理研究

对金刚石刀具、涂层刀具及硬质合金刀具车削纯钒时刀具磨损形态及其磨损机理进行观察和分析.结果表明,在所选取条件下,不同刀具材料对工件材料切削时表现出的刀具磨损形态主要为前刀面磨损、后刀面磨损、微崩刃、剥落和粘结等.刀具的前刀面主要是沿切屑流出方向的沟槽形月牙洼磨损,而后刀面以粘结磨损为主.CD10刀具和H10非涂层刀具具有较佳的切削性能,而H13A非涂层刀具和GC1025涂层刀具不适于纯钒车削.     

CBN tool wear in hard turning: a survey on research progresses

Direct machining steel parts at a hardened state, known as hard turning, offers a number of potential benefits over traditional grinding in some applications. In addition, hard turning has several unique process characteristics, e.g., segmented chip formation and microstructural alterations at the machined surfaces, fundamentally different from conventional turning. Hard turning is, therefore, of a great interest to both the manufacturing industry and research community. Development of superhard materials such as polycrystalline cubic boron nitride (known as CBN) has been a key to enabling hard turning technology. A significant pool of CBN tool wear studies has been surveyed, in an attempt to achieve better processing and tooling applications, and discussed from the tool wear pattern and mechanism perspectives. Although various tool wear mechanisms, or a combination of several, coexist and dominate in CBN turning of hardened steels, it has been suggested that abrasion, adhesion (possibly complicated by tribochemical interactions), and diffusion may primarily govern the CBN tool wear in hard turning. Further, wear rate modeling including one approach developed in a recent study, on both crater and flank wear, is discussed as well. In conclusion, a summary of the CBN tool wear survey and the future work are outlined.     

微铣削刀具磨损研究现状

在微制造领域,微铣削因具有加工材料的多样性和能实现三维曲面加工的独特优势而受到越来越多学者的关注,但是微铣刀的快速磨损严重影响了微铣削技术的应用.研究表明微铣刀的磨损主要发生在刀尖部位,刀具磨损呈现显著的尺度效应.分析了微铣刀的磨损机理、刀具磨损的影响因素和改善措施以及刀具磨损状态的监控,并指出了今后研究值得注意的发展方向.     

硬质合金刀具铣削Ti6Al4V时刀具磨损及切削力研究

对硬质合金刀具在干切削状态下铣削Ti6Al4V磨损机理和切削力进行了试验研究,切削速度分别为40、80、120、160m／min。分析了切削力与切削方式、切削速度、切削路程的关系。最后,对刀具的磨损形态和磨损机理进行了探讨。结果表明粘结、扩散是硬质合金铣刀的主要磨损机理。     

A predictive modeling scheme for wear in tribometers

Study of wear in complex micro-mechanical components is often accomplished experimentally using a pin-on-disc and twin-disc tribometer. The present paper proposes an approach that involves a computationally efficient incremental implementation of Archard's wear model on the global scale for modeling sliding and slipping wear in such experiments. It will be shown that this fast simplistic numerical tool can be used to identify the wear coefficient from pin-on-disc experimental data and also predict the wear depths within a limited range of parameter variation. Furthermore, it will also be used to study the effect of introducing friction coefficient into the wear model and also to model water lubricated experiments. A similar tool is presented to model wear due to a defined slip in a twin-disc tribometer. The resulting wear depths from this tool is verified using experimental data and two different finite element based numerical tools namely, the Wear-Processor, which is a FE post-processor, and a user-defined subroutine UMESHMOTION in the commercial FE package ABAQUS. It will be shown that the latter two tools have the potential for use in predicting wear and the effective life span of any general tribosystem using the identified wear coefficient from relevant tribometry data.     

Hybrid Learning for Tool Wear Monitoring

In automated manufacturing systems such as flexible manufacturing systems (FMSs), one of the most important issues is the detection of tool wear during the cutting process. This paper presents a hybrid learning method to map the relationship between the features of cutting vibration and the tool wear condition. The experimental results show that it can be used effectively to monitor the tool wear in drilling. First, a neural network model with fuzzy logic (FNN), responding to learning algorithms, is presented. It has many advantageous features, compared to a backpropagation neural network, such as less computation. Secondly, the experimental results show that the frequency distribution of vibration changes as the tool wears, so the r.m.s. of the different frequency bands measured indicates the tool wear condition. Finally, FNN is used to describe the relationship between the characteristics of vibration and the tool wear condition. The experimental results demonstrate the feasibility of using vibration signals to monitor the drill wear condition.     

Use of Ceramic Tools in Hard Turning of Hardened AISI M2 Steel

Tool wear and machining performance of hardened AISI M2 steel in hard turning has been studied. Ceramic tools were used in the cutting tests without coolants, and the workpiece was heat treated to increase its hardness up to Re 60. Cutting forces, temperature, and tool wear were measured in the experiments and the effects of cutting conditions on these were investigated. Important aspects from the research are summarized as follows: 1. Flank wear was the dominant wear mode on the ceramic tool insert in hard turning. In contrast, crater wear was very small due to the ceramics high resistance against chemical reactions at high temperature. A notch was unlikely to be formed in the tool.

2. The initial flank wear rate mainly depends on the feed rate. High feed rates cause a high initial flank wear rate.

3. Depth of cut was the most important cutting parameter to affect cutting force variation, and the cutting force increased due to tool wear.

     

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.     

基于切削力的铣刀磨损在线监测研究

以实验方法研究了高速钢立铣刀后刀面磨损和铣削力之间的关系,建立了表征后刀面磨损和铣削力之间关系的数学模型,并提出了基于切削力的铣刀磨损监测算法。     

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。