凸曲面拼接模具球头铣刀的瞬时铣削力预测

在曲面模具拼接区域球头铣刀铣削过程中,刀具载荷变化大,瞬态铣削力有突变现象,影响模具拼接区域的加工精度和表面质量。为了预测拼接区域球头铣刀的瞬态铣削力,首先,建立考虑冲击振动的球头铣刀三维次摆线轨迹方程,得到瞬时未变形切屑厚度模型;然后,基于铣削微元的思想,建立凸曲面双硬度拼接模具球头铣刀的瞬态铣削力模型,该模型能够综合考虑拼接区冲击振动、硬度变化、刀具工件切触角度变化对瞬态铣削力的影响;最后,进行凸曲面拼接区域球头铣刀铣削加工实验。实验结果表明,预报的瞬态铣削力和实验测量结果在幅值上和变化趋势上具有一致性,在平稳切削时最大铣削力预测误差值基本在15%以内,验证了该模型能有效地预报凸曲面模具拼接区域球头铣刀的瞬态铣削力。     

气体风冷高速铣削的刀具磨损研究

立铣刀高速铣削方式加工可显著提高淬硬钢模具的加工效率,但同时也存在切削温度高、刀具磨损快的问题.对刀具与工件的切削接触区域进行充分冷却及润滑是解决此问题的有效方法.对气体风冷高速切削展开刀具磨损研究,涉及两种气体冷却方法—压缩气体冷却法以及涡流管冷却法.实验结果表明,涡流管冷却法相比压缩气体冷却法可降低切削区域温度,有...     

自适应滤波在近红外无创生化分析中的应用

提出用血流容积差光谱相减法来消除近红外无创生化分析中组织背景的干扰.为提高光谱相减中所需获得的脉搏波近红外光谱信号的信噪比,研究了自适应滤波处理方法.介绍了最小均方算法(LMS)自适应滤波的基本原理,在此基础上提出了一种适用于处理本实验脉搏波光谱信号的自适应滤波方法;采用实验室自行研制的16元近红外脉搏波采集系统,获得人体脉搏波光谱信号;最后,利用提出的自适应滤波方法处理脉搏波光谱信号并分析其滤波效果.结果表明,利用该方法处理采集的脉搏波信号,可使血流容积光谱相减后血液光谱吸光度噪声由800 μAU降低至12 μAU,相邻波长的脉搏波相关系数由0.994 0提高至0.999 9.分析结果说明该自适应滤波方法可以有效地应用于近红外无创生化分析中.     

The effect of surface roughness on contact and stress states of spur gears

The contact characteristics of spur gears are analysed, taking into consideration real surface micro-geometry, and using numerical algorithms. The contact pressure distribution, contact area, and rigid body rotation of the gears have been calculated for different types of gear surface according to machining: shaping, grinding, or milling. Finally, the sub-surface stress state is evaluated for different pressure distributions, by finite element method.     

薄壁件铣削过程仿真与分析

针对薄壁件加工过程中易产生变形等问题，提出了利用有限元法对铣削过程进行三维仿真的方法，重点研究了LS-DYNA的动态接触算法，建立了薄壁件铣削加工的有限元模型，对工件变形及切削力的变化规律进行了分析。最后，利用分析结果对铣削参数进行调整与优化，可以减小工件变形，保证加工精度。     

Effect of machining parameters on surface integrity of silicon carbide ceramic using end electric discharge milling and mechanical grinding hybrid machining

A novel hybrid process that integrates end electric discharge (ED) milling and mechanical grinding is proposed. The process is able to effectively machine a large surface area on SiC ceramic with good surface quality and fine working environmental practice. The polarity, pulse on-time, and peak current are varied to explore their effects on the surface integrity, such as surface morphology, surface roughness, micro-cracks, and composition on the machined surface. The results show that positive tool polarity, short pulse on-time, and low peak current cause a fine surface finish. During the hybrid machining of SiC ceramic, the material is mainly removed by end ED milling at rough machining mode, whereas it is mainly removed by mechanical grinding at finish machining mode. Moreover, the material from the tool can transfer to the workpiece, and a combination reaction takes place during machining.     

自由曲面等高加工铣削载荷预报

实际生产过程中球头铣刀主要用于曲面加工,但是关于球头铣刀曲面加工切削力的研究很少.本文针对曲面铣削加工中球头铣刀切削力预报的问题,通过离散近似将动态的切削过程分解为一系列进给方向和刀具与工件接触区不断变化的微小瞬态阶段,将剪切和犁切双重作用的静态球头铣刀切削力模型引入到自由曲面铣削载荷预报中,并选取一自由曲面工件进行铣...     

CHMM for tool condition monitoring and remaining useful life prediction

To solve the problems of tool condition monitoring and prediction of remaining useful life, a method based on the Continuous Hidden Markov Model (CHMM) is presented. With milling as the research object, cutting force is taken as the monitoring signal, analyzed by wavelet packet theory to reduce noise and extract the energy feature of the signal as a basis for diagnosis. Then, CHMM is used to diagnose tool wear state. Finally, a Gaussian regression model is proposed to predict the milling tool’s remaining useful life after the test sample data are verified to be consistent with the Gaussian distribution based on a reliable identification of the milling tool wear state. The probability models of tool remaining useful life prediction could be established for tools with different initial states. For example, when an unknown state of milling force signal is delivered to the milling tool online diagnostic system, the state and the existing time of this state could be predicted by the established prediction model, and then, the average remaining useful life from the present state to the tool failure state could be obtained by analyzing the transfer time between each state in the CHMM. Compared to the traditional probabilistic model, which requires a large amount of test samples, the experimental cost is effectively reduced by applying the proposed method. The results from the experiment indicate that CHMM for tool condition monitoring has high sensitivity, requires less training samples and time, and produces results quickly. The method using the Gaussian process to accurately predict remaining life has ample potential for application to real situations.     

An artificial neural network approach for the control of the laser milling process

Laser milling (LM) can be classified as a layer manufacturing process in which the material is removed by a laser beam by means of the ablation mechanism. It is a laser machining process which uses a laser beam to produce 3D shapes into a wide variety of materials. It is also known as laser ablation. It shows clear advantages versus the traditional milling such as the unlimited choice of materials, the direct use of computer-aided design structure data, the high geometric flexibility, and the touchless tool. LM requires the selection of optimal machining parameters for the job. Unlike the mechanical milling and the mechanical incision, the depth of the single removed layer is chosen at the beginning as input parameter of the process. In LM, the ablated depth depends from the process parameters such as laser power, scan speed, pulse duration, and pulse frequency. This work aims to develop an algorithm that can predict the parameters necessary to execute the material removal with a preset ablation depth. Using the results of an experimental campaign, the laser milling process was modeled by means of a back-propagation artificial neural network. Then, an iterative algorithm, based on the previous trained neural network, permitted to calculate the scanning velocity and pulse frequency that approached for the best the preset ablation depth. The developed approach represents a mean for the rational selection of laser ablation process parameters. It can be performed in an intuitive manner since it uses simple artificial intelligence like the artificial neural network.     

高硬度合金铣削力预测研究进展

高硬度合金材料因其较高的比强度、优异的耐腐蚀和耐热性能,广泛应用于各个领域。但在实际加工过程中,由于热导系数低、弹性模量小、单位面积切削力大、刀-屑接触面积小等特性,其加工一度成为研究的难点与热点。从传统铣削和微铣削的不同加工机理出发,对高硬度合金材料铣削力建模并进行探讨。阐述了铣削力系数、铣削速度、刀刃圆弧半径、刀具跳动和挠性变形对铣削力预测的影响,分析了高硬度合金材料铣削力预测中的一些热点与难点以及目前铣削力建模存在的问题。     

Generation of collision-free cutter location data in five-axis milling using the potential energy method

In five-axis milling, optimal cutter location data (CL-data) should be generated to have advantages over three-axis milling in terms of accuracy and efficiency. This paper presents an algorithm for generating collision-free CL-data for five-axis milling using the potential energy method. By virtually charging the cutter and part surfaces with static electricity, global collision as well as local interference is eliminated. Moreover, machining efficiency is simultaneously improved by minimising the curvature difference between the part surface and tool swept surface at a cutter contact point (CC-point).     

基于连续小波和多类球支持向量机的颤振预报

研究了一种应用连续小波特征和多类球支持向量机进行铣削系统颤振预报的方法,该方法基于连续小波变换提取铣削振动信号的特征,利用多类球支持向量机对正常铣削状态、颤振孕育状态和颤振爆发状态的振动信号进行三分类识别,通过识别颤振孕育状态预测颤振爆发。试验结果表明,在铣削颤振识别与预测中,铣削振动信号的连续小波特征与多类球支持向量机相结合具有良好的识别颤振孕育状态和颤振爆发状态的能力,颤振孕育状态的识别正确率达95.0%,颤振爆发状态的识别正确率达97.5%。     

采用小波包能量熵的铣削振动状态分析方法研究

颤振是影响机床加工质量的重要原因之一。为实现切削颤振的实时在线识别与评价,采用加速度传感器,获取主轴振动信号,以小波包能量熵值为指标,对铣削加工的稳定状态及振动形式进行识别。通过多传感器对加工过程进行监测,确定加工的稳定性;对主轴振动信号进行频谱分析,了解不同加工状态下的信号频谱特点,分析其振动形式。对信号进行小波包分解,发现在不同的振动状态下,信号的能量分布有显著规律。试验表明,切削从稳定状态到不稳定状态,本质上是强迫振动和颤振的能量强度和分布发生了变化。能量熵描述能量分布的变化,是识别切削状态和振动状态变化的有效方法。     

EQUIVALENT NORMAL CURVATURE APPROACH MILLING MODEL OF MACHINING FREEFORM SURFACES

A new milling methodology with the equivalent normal curvature milling model machining freeform surfaces is proposed based on the normal curvature theorems on differential geometry. Moreover, a specialized whirlwind milling tool and a 5-axis CNC horizontal milling machine are introduced. This new milling model can efficiently enlarge the material removal volume at the tip of the whirlwind milling tool and improve the producing capacity. The machining strategy of this model is to regulate the orientation of the whirlwind milling tool relatively to the principal directions of the workpiece surface at the point of contact, so as to create a full match with collision avoidance between the workpiece surface and the symmetric rotational surface of the milling tool. The practical results show that this new milling model is an effective method in machining complex three- dimensional surfaces. This model has a good improvement on finishing machining time and scallop height in machining the freeform surfaces over other milling processes. Some actual examples for manufacturing the freeform surfaces with this new model are given.     

The avoidance of cutter gouging in five-axis machining with a fillet-end milling cutter

This paper proposes a new method of automatic detection and elimination of cutter gouging when using the fillet-end milling cutter to produce a complex surface on the five-axis computer numerical control machine tool. To avoid local cutter gouging at the point where the cutter and part surface make contact with each other, the method of exact curvature matching between the cutter and part surface is presented. The size of cutter radii is more easily determined by this method. To detect if a rear cutter gouging occurs near the contact point, a square grid with horizontal and vertical points is used for illustrating the checking area and checking points. The technique of automatic generation of the square grid points and the method of detection and avoidance of rear cutter gouging are investigated throughout this article. In the end, the studied methodology and algorithms are inspected and verified by using an example of nonuniform rational B spline surface.     

Noncontact excitation of sound in metals by a video pulse of electric field

An acoustic signal that is excited without acoustic contact by an excitation electrode in a metal sample has been studied. A constant electric voltage close to the breakdown value was applied between the electrode and the sample. A video pulse of electric field with a duration of 0.4 μs was used to excite sound in the sample. The ratio k of signals that were registered with noncontact and contact excitation proved surprisingly large (k = 0.14) when compared to the value expected from the literature data. A strong dependence of the noncontact acoustic signal on the filling frequency f of the excitation pulse has been observed. For f > 10 MHZ, the signal dropped to the level of noises. An explanation for this frequency dependence is proposed. It is based on the assumption of quasi-atomic thickness of the charged metal layer that is created by the electric field.     

基于正交试验的铣削振动影响因素分析

切削振动对零件加工表面质量影响较大,为了控制切削振动,提高加工表面质量,利用正交试验进行不同工艺条件下的铣削试验,采集振动信号并提取信号特征值,通过方差分析等方法研究铣削参数和刀具磨损对铣削振动的影响规律,确定影响铣削振动的显著因素,为切削振动控制研究及铣削工艺参数的合理选择提供理论参考。     

铣削508Ⅲ钢硬质合金刀具敏感性分析

508Ⅲ钢材料应用于核岛AP1000蒸发器水室封头中,是一种高强度、高硬度和高断面收缩率的低碳合金钢。硬质合金刀具在切削508Ⅲ钢时,会产生较大的切削力以及切削振动,从而影响刀具使用寿命。本文进行硬质合金刀具铣削508Ⅲ钢试验,探究切削力以及切削振动信号对刀具磨损敏感性的变化趋势的影响,并运用互相关函数分析切削力以及切削振动信号对硬质合金刀具磨损形态的敏感程度。试验结果表明:切削力较切削振动相比,对刀具磨损形态的影响较大,并在切削速度为298m/min时,切削力、切削振动对刀具磨损形态互相关程度较高。为进一步研究通过切削力等信号检测刀具磨损状态提供试验及理论参考。     

Predictive synchronization and restoration of corrupted velocity samples

Incremental pulse encoders are widely applied to digital speed measuring. The velocity samples obtained by measuring the pulse length contain different types of error. This combined error can be considered as a corrupting noise component that needs to be reduced before the velocity information is utilized, e.g., for feedback control. To ensure smooth and rapid response of the control system, the noisy velocity signal has to be filtered without delaying the primary component. Conventional low-pass filters always cause lag into the primary signal, and thus they cannot be used if fast tracking of the reference velocity is required. To overcome this problem, predictive filters are used in this work to realize a novel signal processing method, capable of synchronizing and restoring the corrupted and unevenly spaced samples. A cascade predictor structure is proposed for good overall noise attenuation. A software implementation of the proposed algorithm has been developed and tested on the TMS320C25 signal processor.     

基于奇异值分解的铣削力信号处理与铣床状态信息分离

利用连续截断信号构造矩阵,通过奇异值分解可以将信号表示为一系列分量信号的简单线性叠加,证明了各分量之间是两两正交的,且具有零相位偏移特性.根据分量信号的信息量可以确定合理的矩阵结构.对铣削力信号的处理实例表明,奇异值分解方法分离出机床主轴旋转基频近乎完整的时域波形,分辨出两个频率很接近的信号分量,发现信号中隐藏的调幅现象,证实机床的爬行并确定爬行频率.最后与小波变换的结果进行比较,表明这一方法对铣削力信号的分离效果优于小波变换.