摘要
高新机电产品的发展使微孔应用范围越来越广泛，需要微孔加工的场合越来越多，对微孔加工的表面质量、尺寸精度、位置精度和微孔加工的效率等也提出了更高的要求，微孔的加工面临着巨大的挑战。使用麻花钻的机械钻孔作为传统的孔加工方法，因为其设备简单、操作方便、价格合理而受到大多数加工企业的青睐，并且机械钻孔具有加工效率高、微孔表面质量好、精度高，加工不受材料导电性能的限制和影响等诸多优点，成为在众多微孔加工方法中，生产成本最低、最经济实用的方法。
微钻头直径小，长径比大的结构特点决定了微钻头强度低、刚性差、易折断、入钻困难、易产生入钻偏移、易切屑堵塞等加工缺陷，往往造成微钻头的折断，给生产带来不利影响。微孔钻削加工难度大，微钻头易折断又难以预测，因此在钻削过程中，如何避免微钻头的折断，提高微钻头的使用寿命成为国内外学者普遍关注和致力于解决的问题之一。本文研究旨在通过对微孔钻削在线监测，有效预防和避免微钻头折断，提高微钻头的利用率和加工效率。
钻削力是表征微钻头工作状态的基本信息，通过实时监测钻削轴向力和钻削扭矩两种力信号可实现对微钻头工作状态的在线监测，从而有效预防和避免微钻头的折断。
本文提出了将微钻头接近折断时的钻削力信号归结为两种模式：一种是渐进型的磨损折断模式，另一种是突变型的异常折断模式。构建了监测两种折断模式的双神经网络模型，以微钻头钻削轴向力和扭矩时域最大幅值及其对时间一阶导数作为特征量，构成神经网络的输入层向量，利用实验数据构成训练样本集对双神经网络模型进行网络训练，得到两组监测阈值的参考数据，用于微孔钻削的在线监测。
本文充分发挥LabVIEW软件图形化的编程语言的优势，将其强大的编程功能与钻削力检测的硬件实验装置有机结合，设计了基于虚拟仪器技术的微孔钻削力在线监测系统，该系统由应变式传感器、电荷放大器、A/D数采卡、计算机软件监测系统、单片机控制单元、功率放大器、驱动电源、步进电机、减速传动链、精密钻床等几大部分构成，通过监测软件系统实现对钻削力信号的实时采集和处理、实时显示和历史数据重现、神经网络决策与报警退刀等功能，并实时发出控制信号通过单片机实现对主轴进给运动的伺服控制，执行报警退刀或正常钻削等控制指令。
通过监测系统进行了微孔钻削力在线监测实验，其监测过程为：将实时检测的钻削力信号与预先设定的钻削力监测数值作比较，根据钻削力取值情况来选择双神经网络之一进行计算；将双神经网络输出与预先设置的监测阈值做比较，若双神经网络输出小于监测阈值，意味着微钻头工作正常，工作状态安全，单片机发出控制指令，微钻头继续钻削加工；若网络输出大于或等于监测阈值，则预示微钻头磨损严重或遭遇异常状况，出现工作异常，无法保证正常工作，单片机控制单元发出报警指令，要求步进电机停止工作，不再进给，微钻头退刀。监测实验结果表明，用双神经网络来进行微孔钻削在线监测效果较好，该监测策略具有可行性。
本论文主要创新点如下：
（1）组建了微孔钻削力实验系统，对微孔钻削轴向力和钻削扭矩进行实验研究。结果表明，随着钻孔数量的增多，微钻头磨损程度不断加剧，钻削轴向力和钻削扭矩两种信号幅值也随之增大，其变化趋势与微钻头磨损状况具有关联性，表明钻削力信号的幅值变化对微钻头磨损程度非常敏感，将钻削力信号作为表征微钻头工作状态的特征量是可行的，为微孔钻削在线监测确定了特征参数。实验还表明，微钻头接近折断时钻削力信号呈现出渐变型和突变型两种不同的幅值增长模式，为双神经网络监测模型设计提供依据。
（2）构建了对微钻头接近折断时钻削力信号渐变型和突变型两种模式进行监测的双神经网络模型，并对两种不同折断模式进行网络训练，得到了相对稳定的神经网络，为微孔钻削过程的智能化在线监测提供了一种有效的方法。
（3）采用LabVIEW软件与硬件相结合，设计了基于双神经网络的微孔钻削在线监测系统，进行了单、双神经网络微孔钻削在线监测对比实验，验证了双神经网络比单神经网络更能准确地预报微钻头的工作状态，预防微钻头折断的效果更好。
The development of high-tech products continuously increases the number of microholes and impels more and more wide application of micro holes. Processing quality ofmicro holes, example surface quality, dimensional accuracy, positional accuracy and workingefficiency are taken more and more high requests. Micro holes processing faces enormouschallenge. Drilling process by twist drills, as a traditional holes processing method, ispopular with the major businesses, because of simple equipments, convenient operation,reasonable price. And it has many advantages such as high processing efficiency, goodsurface quality, high precision and processing of no restriction from conductive properties ofmaterials. Micro drilling becomes the method with the lowest production cost and the mosteconomical and practical among many micro holes processing methods.
The structural characteristics of small diameters, large length-diameter ratio of microdrills determines some fatal weaknesses, including low strength, poor rigidity, easy breakagedifficultly drilling, drilling offsetting, chip plugging, and so on. These usually cause microdrills breakage or damage, bring adverse effects in practice. Micro drill is easy to break anddifficult to predict, therefore in the drilling process, in order to prevent their breakage ordamage and improve their life, many experts and scholars have been committed to studymicro drilling. They hope to find some better methods to solve the outstanding problems onmicro drilling. So the focus of this paper are how to safeguard micro drills, give full play tothe function of micro drills, improve micro drilling capacity, productivity and toolsutilization rate.
Force signals are the necessary conditions to represent continuously cutting and thenecessary factors to character working state of micro drills. Real-time monitoring forcesignals can realize on-line monitoring the state of micro drills, so that effectively prevent andavoid their breakage.
Because drilling force signals showed two changes under normal working and microdrills breakage, the drilling force signals are summed up two kinds of modes, one isprogressive type wear broken mode, another is mutant abnormal failure mode. Taking theseas the premise, the double neural network models are constructed, some characteristics of thesignal amplitudes of drilling thrust and torque signals and their first time derivative composes the input layer vectors of the neural network. The networks are trained by usingthe experimental data, two groups of monitoring threshold are obtained and used to on-linemonitor micro drilling.
The paper gives full play to LabVIEW software advantages, combines the G-languageprogramming function and experimental device, a kind of micro drilling force signals on-linemonitoring system is designed based on VI technology. The system is composed of severalparts including train sensors, the charge amplifier, the A/D PCI, the computer softwaremonitoring system, the microprocessor control unit, the power amplifier, the drive power, thestep motor, the drive chain and the precision drilling machine. The system working steps areacquisition of signals, real-time displaying and historical data reproduction, data handling,signal analysis, neural network decision, servo control to the step motor and alarm backcutting tools.
Drilling force monitoring experiments are done by using this system. Specific process isas follows, the first step is to comparing the real-time drilling force signals and thepredetermined values in order to choose the neural network model, second is that thereal-time data are input into the trained network model and train or calculate, third is tocontrast the network output to the given monitoring threshold, micro-drills will continue todrill while the output is less than the threshold, or else micro-drills will been alerted to drawback while the output is greater than or equal the threshold. The results showed that selectingappropriate monitoring threshold could effectively avoid micro-drills breakage. Somonitoring micro-drilling using the double neural network is feasible and available.
In this paper, the main innovations are as follows:
(1) A kind of experiment system is built to collect the drilling thrust and torque signalsby sensors. A lot of experimental results show that the more drilling holes, the more seriousmicro drills wear, the greater the drilling forces are. They also show that the amplitudes ofdrilling forces are sensitive to micro drills’ wear, so drilling forces may represent the wearingstate and cutting state of micro drills, drilling forces are usual as the character parameters. Inthe experiments, the two changes under normal working and micro drills breakage, thedrilling force signals are summed up two kinds of modes, one is progressive type wearbroken mode, another one is mutant abnormal failure mode.
(2) The double neural network model, including the progressive type and the mutantabnormal failure mode, is set up to on-line monitoring micro drilling. The double neuralnetwork is trained by lots of experimental samples, the two stable networks are obtained. Aeffective method is get to intelligent on-line monitoring micro drilling.
(3) A kind of micro drilling force signals on-line monitoring system is designed basedon the combination of the LabVIEW software and lots of hardware. Some contrast tests aredesigned based on between the single neural network and the double neural network, resultsshow that the effect on the double neural network is better than its effect on the single neuralnetwork during micro drilling on-line monitoring.
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