车削加工中工件的模态分析

车削加工中工件的振动会严重影响零件的尺寸精度及表面粗糙度。为分析工件系统的动特性,分别用有限元法和试验法（频响函数法）对系统进行模态分析。结果表明两种结果之差低于10％,因此有限元求解比较精确。     

Stability analysis of turning process with tailstock-supported workpiece

This paper proposes an analytical scheme for stability analysis in turning process by considering the motion of tailstock-supported workpiece using a compliance model of tool and work. A dynamic cutting force model based on relative motion between the cutting tool and workpiece is developed to study the chatter stability. Linear stability analysis is carried out in the frequency domain and the stability charts are obtained with and without considering workpiece flexibility. Variations of stability limits with workpiece dimensions and cutter position as well as the effects of cutting tool dynamics are studied and wherever possible results are compared with existing models. Experimental analysis is conducted on tailstock-supported workpiece to examine the correctness of the proposed stability model.     

车削物理仿真工件表面质量模型的研究

分析了动态车削过程中工件表面粗糙度的影响因素,建立了由切削材料微观硬度差异为主要干扰因素的车削动态物理仿真系统的工件表面质量分析与预测模型,并且就具体零件的物理仿真过程给出了工件表面质量仿真的输出结果。     

薄壁圆筒件车削温度测量方法研究

在归纳各种切削温度测温方法的原理和优缺点的基础上,针对薄壁圆筒件回转车削的特殊性,结合实际情况分析了各种方法的适用性.     

An accurate interpolator for FTS diamond turning of optical free-form surface

The ultra-precision components with freeform surfaces are used in a wide range of areas such as automotive, aerospace, optical, metrology artifacts, and data storage. Fast tool servo (FTS) diamond turning has been considered as one of the leading techniques to produce such components. We present four points mean value interpolator for FTS diamond turning. Performance is compared between the NURBS interpolations with the proposed interpolation using Matlab, which shows that the proposed interpolation not only improves the surface accuracy of the freeform surface, but also meets the real-time requirement.     

基于模糊系统的车削工件直径误差预测方法

为满足特定轴类工件的加工精度要求,根据车削过程中工件直径误差的特点,提出了用模糊系统预测车削工件直径误差的方法.通过设计车削实验,得到训练数据和测试数据.在分析梯度下降算法和传统遗传算法优缺点的基础上,将梯度下降算法嵌入传统遗传算法形成混合遗传算法.通过训练数据分别用梯度下降算法、传统遗传算法和混合遗传算法训练Mamdani型模糊系统,混合遗传算法的收敛效果优于梯度下降算法和传统遗传算法,用测试数据对三种算法训练的模糊系统进行测试,混合遗传算法的预测效果也是三种算法中最好的.预测结果表明,在一定的工件结构和工况条件下,用混合遗传算法训练的Mamdani型模糊系统进行车削工件直径误差的预测是可行的.     

Improved measure of workpiece surface deterioration during turning using non-contact vision method

The surface finish quality of a machined workpiece is commonly measured using the average roughness parameter, R_a. This parameter, however, is insensitive to the lateral changes undergone by the surface in the feed direction as a consequence of tool wear. In this work, the effectiveness of four methods of workpiece surface analysis, namely autocorrelation, cross-correlation, and two new methods, called lateral material shift (LMS) ratio and profile slope ratio (PSR) analyses are investigated. Dry machining experiments were carried out on 316 stainless steel. Images of tool nose and workpiece profiles were captured using digital camera, and the edges were extracted using sub-pixel edge detection. In the autocorrelation approach, each workpiece profile was correlated with a shifted version of the same profile. In the cross-correlation approach, the workpiece profiles at different stages of machining were correlated with a reference profile generated using the unworn tool edge. In the LMS ratio method, the material shift ratios were determined from each waveform on the workpiece profile at various stages of tool wear, while in the PSR method the slopes at the right and left part of the waveform were compared. Among the four methods, the LMS ratio method produced the best correlation with tool flank wear with the maximum R-squared value of 0.9461, while average roughness R_a showed no correlation at all with both major and nose flank wear.     

小尺寸高精度零件平面磨削的研究与实现

小尺寸高精度零件平面磨削一直采用研磨的方式磨削,但该方法对操作人员的技能要求高、报废率高、效率低下.设计基于涂附模具的小尺寸高精度零件平面磨削方法,并制作模具1套.实例证明,该方法具有精度高、操作方便、效率高等特点.     

用热源叠加原理研究圆孔车削温度场

基于移动热源法及热源叠加原理,对圆孔车削温度场进行研究。提出局部温度场和总温度场的分析方法,推导出瞬时圆环热源温度场的描述方程,为圆孔工件的工程实际热问题提供了理论计算方法。     

数控铣床实现小型零件车削的研究

本文从数控铣床的加工现状，研究扩展其加工功能，在加工方法，数控编程，刀具的选择，夹具的设计及定位，加工精度等相关技术要求上，探索实现小型零件车削的可行方案及使用功能，在加工设备的灵活使用上建立一种综合应用的思维方式。     

Virtual workpiece: workpiece representation for material removal process

In this paper, an efficient methodology to generate a virtual workpiece (VWP) is presented. VWP is a workpiece in a virtual environment in which the geometric, kinematic, and thermo-mechanical effects of the process and resources can be reflected. VWP encompasses not only the macro-information corresponding to the shape of the “virtually” machined intermediate workpiece, but also the micro-information, such as the surface roughness, scallop heights, chatter mark, etc. To represent VWP, swept volume (SV) of geometrically defined cutters is generated first by envelope profiles which are calculated by the intersection of the Tool map with the Contact map of the tool moving direction. Then SV is tessellated to conduct elementary 1D Boolean subtraction of SVs from the IPW. The Boolean subtraction is realized by means of an efficient ray-triangle intersection test using Barycentric coordinates. Finally, VWP is reconstructed as a triangular mesh (STL, stereolithography data format) from the orthogonal triple-dexel model (TDM) which predicts machined surface quality, such as surface roughness, gouging and sharp edges and is reused for further operations, e.g., tool path generation, simulation and geometric metrology, etc. To demonstrate the validity of VWP modeling, several material removal processes, e.g., milling and micro-EDM operations, have been tested and the proposed approach has been proven to be applicable to enhance the quality of NC simulation and verification.     

Prediction of surface roughness and dimensional deviation of workpiece in turning: a machine vision approach

In the past, roughness values measured directly on machined surfaces were used to develop mathematical models that are used in predicting surface roughness in turning. This approach is slow and tedious because of the large number of workpieces required to obtain the roughness data. In this study, 2-D images of cutting tools were used to generate simulated workpieces from which surface roughness and dimensional deviation data were determined. Compared to existing vision-based methods that use features extracted from a real workpiece to represent roughness parameters, in the proposed method, only simulated profiles of the workpiece are needed to obtain the roughness data. The average surface roughness R _a, as well as dimensional deviation data extracted from the simulated profiles for various feed rates, depths of cut, and cutting speeds were used as the output of response surface methodology (RSM) models. The predictions of the models were verified experimentally using data obtained from measurements made on the real workpieces using conventional methods, i.e., surface roughness tester and a micrometer, and good correlation between the two methods was observed.     

Tool wear, chip formation and workpiece surface issues in CBN hard turning: A review

Steel parts that carry critical loads in everything from automotive drive trains and jet engines to industrial bearings and metal-forming machinery are normally produced by a series of processes, including time-consuming and costly grinding and polishing operations. Due to the advent of super-hard materials such as polycrystalline cubic boron nitride (PCBN) cutting tools and improved machine tool designs, hard turning has become an attractive alternative to grinding for steel parts. The potential of hard turning to eliminate the costs associated with additional finishing processes in conventional machining is appealing to industry. The objective of this paper, is to survey the recent research progress in hard turning with CBN tools in regard of tool wear, surface issues and chip formation. A significant pool of CBN turning studies has been surveyed in an attempt to achieve better understanding of tool wear, chip formation, surface finish, white layer formation, micro-hardness variation and residual stress on the basis of varying CBN content, binder, tool edge geometry, cooling methods and cutting parameters. Further important modeling techniques based on finite element, soft computing and other mathematical approaches used in CBN turning are reviewed. In conclusion, a summary of the CBN turning and modeling techniques is outlined and the scope of future work is presented.     

Selection of preliminary locating and clamping positions on a workpiece for an automatic fixture design system

This paper focuses on the development of heuristic algorithms for selecting the locating and clamping positions on an automatic fixture configuration for a given workpiece for an automatic fixture design (AFD) system. It discusses several issues regarding (i) an informationally complete product model, and (ii) the development of necessary computational modules coupled with a knowledge based system to reason about the workpiece's geometric characteristics for determining the locating and clamping positions. The motivation of the work is to develop a fixturing subsystem as an integral part of the complete CIM environment. A prototype AFD system has been completed in the object oriented programming platform of Wisdom's Concept Modeller. It has used several geometric reasoning mechanisms based on the traditional fixture design principles, and on the principles of collision-free assembly. The system exploits the power of Wisdom's advanced geometric modelling (AGM), and it implements the basic architecture of the proposed knowledge based fixture design system.     

轴承密封槽直径尺寸车削用测量塞规结构改进

通过对轴承密封槽直径尺寸车削测量塞规结构的改进,使塞规制造简便,易于互换,制造成本降低。经生产验证,经济、实用、便于管理。     

Characterisation of the workpiece dilatation phenomenon during machining using the neural network method. Application to NC turning

Dilatation of workpieces during machining is a major source of defects. With the current trend for re-treatment of cutting and cleaning fluids becoming compulsory, lubrication by a stream of oil and dry machining are becoming more widely used in aluminium alloy machining. Indeed, this makes it easier to recycle chippings and greatly simplifies the cleaning and grease removal phases for workpieces that are compulsory before any finishing surface treatment. However, the workpiece’s deformation during machining must be taken into account. This is especially true for NC turning of machining diameters with very tight tolerances. Here we propose a method based on the use of a neural network intended to model changes in the workpiece’s dimensions to correct tool paths. This study covered machining of workpieces made of 2017 T4 aluminium alloy during the turning phase. We first conducted preliminary tests on a workpiece to highlight workpiece dilatation. We then implemented a neural network to predict this deformation to be able to compensate for it. The results of a first test campaign gave us knowledge of the network then a second test campaign was used to validate that network. To finish off, we machined a test workpiece in order to test and analyse network performance.     

Assessment of flank wear and nose radius wear from workpiece roughness profile in turning operation using machine vision

Although literature on the measurement of flank wear and crater wear in single-point turning tools using machine vision is well documented, the study on the effect of nose radius wear on the roughness profile and dimensional changes of workpiece is less explored. The measurement of flank wear using the 2-D profile of the tool nose region or the roughness profile of the workpiece has also not been attempted in the past. In this work, the nose radius wear of cutting tools and roughness profile of turned parts in a lathe operation were measured using the machine vision method. The flank wear width VB_C in the nose area was determined from the nose radius wear using the tool setup and machining geometry. The nose radius wear was also determined from the roughness profile of the workpiece and used in calculating the flank wear width. Comparison between the maximum flank wear width VB_Cmax determined from the roughness profile and that obtained using a toolmaker’s microscope showed a mean deviation of 5.5%. This result indicates that flank wear can be determined fairly accurately from the workpiece roughness profile if the tool and machining geometry are known.     

An electron microprobe and cathodoluminescence study of chemical reactions between tool and workpiece when turning steel with alumina-based ceramics

Crater wear of alumina-based ceramic cutting tools when machining steel is predominantly dependent upon superficial plastic deformation. Such tool surface deformation may be greatly affected by chemical reactions with workpiece material. Crater surfaces of worn alumina-based ceramic tools in Coromant grade CC 620 (a pure ceramic, containing Al₂O₃ and ZrO₂) have been analysed by electron microprobe and cathodoluminescence after turning steel SS 2541 (similar to AISI 4337). It was found that the deformed surface layer had increased concentrations of iron and magnesium. Both these elements were probably present as spinel phases FeO. Al₂O₃ and MgO. nAl₂O₃ in solid solution. The spinel phases have higher yield strengths and probably also higher ductility than alumina itself, which may explain why wear rates are reduced when such compounds are found at the alumina tool-chip interface.