The effect of machine stiffness on grinding of silicon nitride

This study investigates the effect of machine stiffness on normal forces, actual depth of cut, and workpiece strength in grinding of silicon nitride. To obtain a grinding system with an adjustable stiffness, a compliant workholder is added to a precision grinder. Single-pass and multi-pass grinding experiments are conducted to evaluate the effect of machine stiffness. Cup-type diamond wheels of two different bond types and three grit sizes are used in the grinding experiments. Static and dynamic simulation is carried out to correlate grinding forces and actual depth of cut with machine stiffness. Since the simulation uses a time-domain model, it can accommodate non-linearities caused by the effect of machine stiffness on grinding forces and actual wheel depth of cut, workpiece regeneration, wheel wear, as well as wheel bond type and grit size effects, etc. Particularly, the model allows simulating grinding instability and the interference phenomenon due to residual material removal in multi-pass grinding. The study concludes that both simulation and experimental results have a good agreement.     

基于LC3000滚齿机的双头蜗轮滚齿误差分析与控制

在详细剖析蜗轮滚齿误差来源的基础上,针对蜗轮滚刀制造误差和滚切包络运动带来的齿形误差影响,通过对比分析,提出利用LC3000数控滚齿机自带的操作功能和数控程序相结合的方法,解决了双头蜗轮滚齿加工齿形误差控制难的问题。研究结果证明:该方法操作简单、效率较高、质量稳定,装配啮合良好。     

CBN高效深磨砂轮主轴的建模分析与实验研究

基于对CBN端面砂轮的研究,提出一种高效率深度端面磨削的砂轮主轴系统.主轴单元的静态特征和影响因素分析了理论计算和建模仿真分析,并验证主轴结构和参数的合理性.基于模态分析理论,砂轮主轴系统的模态测试是由锤击方法,并得到了前6阶模态参数和分析结果.结果表明:该磨削主轴系统满足了高效深磨技术要求.最后,研磨应用开发测试平台应用了磨削主轴单元和一些实验来完成高效深磨的研究.对Cr12Mo1V1工件在砂轮转速为80 ～ 100 m/s时进行了端面磨削实验,其结果为:切削深度0.175～0.5 mm,表面粗糙度0.8～3.2 μm以及圆柱度5-10 μm.     

汽车转向器垂臂失效分析

采用体视检测、显微组织观察以及显微硬度等分析方法,对汽车转向器垂臂早期失效原因进行了分析.结果表明,失效主要原因是:转向垂臂ZG35钢的显微组织、硬度不符合技术要求,同时,转向垂臂内锥齿加工时发生乱齿现象,造成转向轴锥齿和转向垂臂内锥齿之间达不到静配合,以上因素共同作用引起内锥齿塑性变形、早期磨损和断裂.     

On the role of plastic deformation during the mild wear of alumina

Polycrystalline -alumina was worn against Mg-partially stabilized zirconia (Mg-PSZ), using water lubrication, a sliding speed of 0.24 m/s and a load of 10 N. Differential wear between grains (maximum 33 nm) and fine (0.3–1.9 μm diameter) abrasive grooves were found on the worn surface. TEM of back-thinned samples indicated widespread dislocation flow at the surface, heterogeneously distributed between grains, and largely associated with abrasive grooves. Those grains standing proud of the surface invariably contained extensive dislocation damage. The dominant slip system was pyramidal ( , , and ) although occasional basal slip was also found. No prism slip was observed. The pyramidal slip planes were concentrated at angles of 6–33° to the worn surface. Basal slip was frequently associated with basal twinning on planes at 72–73° to the worn surface. Dislocation pile-ups at grain boundaries often coincided with grain boundary cracking. The extent of damage from abrasive grooves varied from grain to grain and was dictated by crystallographic orientation more than the grain height. No evidence of mechanical damage was found in those grains that had suffered the highest wear, indicating that material removal had been controlled by tribochemical mechanisms. The origin of the differential wear between grains is considered and the implications of the experimental observations on the time-dependent transition to severe wear in aluminas are discussed.     

Development of monitoring system on the diamond tool wear

Recently, ultra-precision machining using a single crystal diamond tool has been developing very rapidly, especially in the fields of production processes for optical or magnetic parts such as magnetic discs, laser mirrors, polygon mirrors and copier drums. As a result, it has been successfully extended to machine various soft materials, generating mirror-like surfaces to sub-micron geometric accuracy with the ultra-precision CNC machine and the single crystal diamond tool. With the real cutting operation, the geometric accuracy and the surface finish attainable in machined surfaces are mainly determined by both of the sharpness of a cutting tool and stability of the machine vibration. In this study, for monitoring the progress of machining state for assuring the machining accuracy and the surface quality, a new monitoring method of machining states in face-cutting with diamond tool is proposed, using the frequency response of multi-sensors signal, which includes wear state of tool in terms of the energy within the specific frequency band. A magnetic disc is machined on the ultra-precision lathe.     

The influence of working parameters on the response of a capacitive sensor used in-process for the measurement of tool wear

In a previous research work, a no contact capacitive sensor was obtained which could check superficial characteristics of workpieces during working. This sensor can relate variations of capacity to superficial finishing of the workpiece directly.The present study aims at finding a method for interfacing the capacitive sensor to a system of data acquisition and for deciding the substitution of the tool, automatically. It has been necessary to value the influence of cutting parameters on sensor's response in order to find the threshold value, characterizing by itself an anomalous working state owed to regular or irregular tool wear.     

On-line monitoring of tool wear in turning using a neural network

Tool wear has long been identified as the most undesirable characteristic of the machining operations. Flank wear, in particular directly affects the workpiece dimensions and the surface quality. A reliable and sensitive technique for monitoring the tool wear without interrupting the process, is crucial in realization of the modern manufacturing concepts like unmanned machining centres, adaptive control optimization, etc. In this work an optoelectronic sensor is used in conjunction with a multilayered Neural Network for predicting the flank wear on the cutting tool. The gap sensing system consists of a bifurcated optical fibre, a laser source and a photodiode circuit. The output of the photodiode circuit is amplified and converted to the digital form using an A/D converter. The digitized sensor signal along with the cutting parameters form the inputs to a three layered, feed forward, fully connected Neural Network. The Neural Network, trained off-line using a backpropagation algorithm and the experimental data, is used to predict the flank wear. A geometrical relation is also used to correlate the flank wear on the cutting tool with the change in the workpiece dimension. The values predicted using the Neural Network and those calculated using the geometrical relation are compared with the actual values measured using a tool maker's microscope. Results showed the ability of the Neural Network to accurately predict the flank wear.