基于图像纹理分析的大米加工等级检测方法

利用不同加工等级大米表面纹理不同的特点,提出了基于纹理分析的大米加工等级检测方法。设计了大米的计算机视觉检测系统,获取4个不同加工等级大米标准样的图像,采用灰度梯度共生矩阵的纹理分析方法提取图像的纹理特征值,采用Fisher判别法和PNN神经网络对大米加工等级进行检测判定。试验结果表明:Fisher判别法和PNN神经网络对4种不同加工等级的大米样品检测判定的正确率分别是96.25%和90.00%。     

螺纹铣削工艺与编程研究

针对螺纹加工中存在的主要问题,研究总结螺纹铣削的工艺和编程方法.在此基础上,开发了通用螺纹和锥螺纹的铣削程序,并进行简化,由此解决小直径螺纹、特殊螺纹的加工问题.应用所开发的螺纹铣削程序,可以降低螺纹加工的不合格率,提高加工效率.     

High bandwidth temperature measurement in interrupted cutting of difficult to machine materials

High-speed milling is used across industries from aerospace to electronics. Tool wear can be affected by cutting interruptions in milling that lower tool-chip interface temperatures but also cause thermal and stress cycling. Micro-thermal imaging was used to determine the temperature during interrupted cutting of titanium alloy Ti6Al4V and AISI 4140 steel for percentage of time-in-cut from 100% to 10%. TiAlN/TiN coated carbide milling inserts were used with cutting speeds up to 180 and 640 m min⁻¹. This technique is the first to allow spatial mapping of thermal fluctuations on the tool which may be critical to determining causes for tool failure.     

Surface roughness variation of thin wall milling, related to modal interactions

High-speed milling operations of thin walls are often limited by the so-called regenerative effect that causes poor surface finish. The aim of this paper is to examine the link between chatter instability and surface roughness evolution for thin wall milling. Firstly, the linear stability lobes theory for the thin wall milling optimisation was used. Then, in order to consider the modal interactions, an explicit numerical model was developed. The resulting nonlinear system of delay differential equations is solved by numerical integration. The model takes into account the coupling mode, the modal shape, the fact that the tool may leave the cut and the ploughing effect. Dedicated experiments are carried out in order to confirm this modelling. This paper presents surface roughness and chatter frequency measurements. The stability lobes are validated by thin wall milling. Finally, the modal behaviour and the mode coupling give a new interpretation of the complex surface finish deterioration often observed during thin wall milling.     

Design and implementation of tuned viscoelastic dampers for vibration control in milling

Passive means of vibration attenuation have been employed successfully and efficiently in machining systems such as turning and milling. Traditional approach to controlling vibration in a milling system is to develop control mechanisms for cutting tools or machine spindles. However, due to the nature of milling operations where the cutting tools rotate at high speed, the passive vibration control methods find very limited application with the traditional approach. In order to utilise the potential of the passive vibration control methodology in milling applications, the milling operation should be viewed as a system comprising an elastic structure and operation parameters. Dynamics of this closed-loop system should improve with improvement in dynamics of any of the system components, especially within the elastic structure that comprises the cutting tool, the machine tool, the workholding system and the workpiece. Although the level of improvement will vary depending on which component of the elastic chain is targeted for this purpose. This paper presents the development and testing of tuned viscoelastic dampers (TVDs) for vibration control through their application on a workpiece in milling operations. This work targets workpiece held on a palletised workholding system for the control of unwanted vibration and thus deviates from the traditional approach where cutting tool and/or machine spindles are targeted for vibration control strategies. Palletised workholding systems, due to their compact design, offer an opportunity to design passive damping mechanisms that are easier to implement in the case of a milling system. The TVD developed through this research is based on a commercially available viscoelastic damping polymer. Advantage of such materials is their high damping performance over a wide range of excitation frequencies. The TVD design process has used a unique combination of analytical modelling with experimental FRF data. Modal impact testing showed that the application of the TVD reduced the amplitude of vibration acceleration by 20 dB for the target mode. Since the target mode corresponded to torsional vibration, the TVD was effective in two planar coordinates, i.e. X and Y. In addition, the TVD also significantly reduced the amplitude of a vibration mode far from the mode it was designed for. The system has been tested experimentally to demonstrate significant reduction in vibration amplitudes during a milling process. The milling tests with different combinations of cutting parameters show that multi-TVD approach is always valid regardless of the parameters being used. The only requirement for TVDs to function effectively is that the natural frequency of the system, for which the TVDs are designed, is excited during the milling process.     

Finite element method based machining simulation environment for analyzing part errors induced during milling of thin-walled components

The rigid body motion of the workpieces and their elastic–plastic deformations induced during high speed milling of thin-walled parts are the main root causes of part geometrical and dimensional variabilities; these are governed mainly from the choice of process plan parameters such as fixture layout design, operation sequence, selected tool path strategies and the values of cutting variables. Therefore, it becomes necessary to judge the validity of a given process plan before going into actual machining. This paper presents an overview of a comprehensive finite element method (FEM) based milling process plan verification model and associated tools, which by considering the effects of fixturing, operation sequence, tool path and cutting parameters simulates the milling process in a transient 3D virtual environment and predicts the part thin wall deflections and elastic–plastic deformations during machining. The advantages of the proposed model over previous works are: (i) Performs a computationally efficient transient thermo-mechanical coupled field milling simulation of complex prismatic parts comprising any combination of machining features like steps, slots, pockets, nested features, etc., using a feature based milling simulation approach; (ii) Predicts the workpiece non-linear behavior during machining due to its changing geometry, inelastic material properties and fixture–workpiece flexible contacts; (iii) Allows the modelling of the effects of initial residual stresses (residing inside the raw stock) on part deformations; (iv) Incorporates an integrated analytical machining load (cutting force components and average shear plane temperature) model; and (v) Provides a seamless interface to import an automatic programming tool file (APT file) generated by CAM packages like CATIA V5. The prediction accuracy of the model was validated experimentally and the obtained numerical and experimental results were found in good agreement.     

Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach

Artificial neural networks (ANNs) models were developed for the analysis and prediction of the relationship between the cutting conditions and the corresponding fractal parameters of machined surfaces in face milling operation. These models can help manufacturers to determine the appropriate cutting conditions, in order to achieve specific surface roughness profile geometry, and hence achieve the desired tribological performance (e.g. friction and wear) between the contacting surfaces. The input parameters of the “ANNs” models are the cutting parameters: rotational speed, feed, depth of cut, pre-tool flank wear and vibration level. The output parameters of the model are the corresponding calculated fractal parameters: fractal dimension “D” and vertical scaling parameter “G”. The model consists of three-layered feed-forward back-propagation neural network. ANNs models were utilized successfully for modeling and predicting the fractal parameters “D” and “G” in face milling operations. Moreover, W–M fractal function was integrated with the developed ANNs models in order to generate an artificially fractal predicted profiles at different cutting conditions. The predicted profiles were found statistically similar to the actual measured profiles of test specimens.     

数控龙门铣床伺服进给系统谐振分析及减振方法

分析了大型龙门铣床数控化改造中所遇到的进给系统谐振问题,指出了速度环谐振频率和机械谐振频率对进给系统谐振特性的影响作用,并提出了相应的解决措施。     

一种难加工材料铣削力的计算机辅助建模

为了实现难加工材料的高效切削,本文采用回归正交表设计试验方案,选用回归方法对试验结果进行分析,最终建立了铣削力模型。在实验中借助于程序,迅速、准确地完成了切削参数的选取、实验数据的处理和结果的分析,确保了实验结果的可靠性。这项工作不仅为切削加工提供了依据,也为切削加工的数值模拟提供了数据,同时为优化切削用量和铣刀设计奠定了基础。