深孔钻削状态监测与分析

应用多传感器综合检测技术，设计了一种多传感器的检测装置，分别采集深孔钻削轴向力、切向力矩、垂直方向振动和水平方向振动的信号，通过数据采集器将信号送到计算机中进行处理。对信号的分析，采用时域和频域分析的方法，提取反映刀具磨损的特征量，最后用模式识别的方法进行状态分类，得出判断样本。     

精密数控机床铣刀故障监测方法的研究

刀具在加工过程中不可避免的存在着磨损和破损现象,刀具的消耗直接导致工件精度下降和生产成本增加。开展了一系列实验,深入研究刀具状态监测方法,构建了新型铣削过程刀具磨损监测试验系统。通过振动传感器和声发射传感器对铣削过程中不同磨损程度刀具的信号进行检测、采集、分析。选择对刀具磨损状态反映敏感的特征量。采用BP神经网络,建立刀具磨损特征向量与刀具磨损状态之间的非线性映射关系。     

基于主轴电流和振动信号的数控机床刀具磨损在线预测

为了实现数控机床加工过程中刀具磨损状态的在线预测，提高数控机床智能化水平，提出一种基于主轴电流和振动信号的数控机床刀具磨损在线预测方法。这一在线预测方法采集能够反映刀具磨损状态的主轴电流和振动信号，对信号进行频域、时频分析处理，采用小波包分解和经验模态分解两种方法进行特征提取，得到与刀具磨损状态变化密切相关的特征值，按照递增或递减趋势进行保序回归操作，使用指数平滑方法进行平滑处理，由此建立基于遗传算法参数寻优的支持向量回归模型，用于预测刀具磨损量。试验及应用表明，应用这一在线预测方法，刀具磨损预测的平均误差在25μm以内，满足企业加工要求。     

异形螺杆铣削过程刀具磨损建模研究

介绍了一种螺杆铣削过程刀具磨损建模的方法。该方法针对螺杆加工中变切削参数的工况，提取了振动信号和功率信号的刀具磨损特征值，并建立了信号特征值与刀具磨损量之间的映射关系，从而得到刀具磨损模型。实验证明，由此建立的刀具磨损模型。能够排除切削参数变化的干扰，可以较好地反映加工中刀具磨损状态。同时也为具有时变切削参数特性的加工过程刀具磨损状态监控提供了新的研究方法。     

用振幅经检测刀具磨损状况

切削机床刀具磨损在线检测,由于测试及信号处理的难度,致使有信号的撮较难。本文通过对GA6140特征频段内的振动信号的振幅比值X／Z的试验与分析,得出监测刀具磨损的特征状况的特征函数,避免了其它方法的复杂伯数学建模工作,具该法直观性强;抗干扰能力强;可靠性高;可有效地检测刀具磨损状况,是实现刀具磨损在线监测的一种好方法。     

用振幅比检测刀具磨损状况

切削机床刀具磨损在线检测,由于测试及信号处理的难度,致使有用信号的提取较难.本文通过对CA6140特征频段内的振动信号的振幅比值X/Z的试验与分析,得出监测刀具磨损的特征状况的特征函数,避免了其它方法的复杂的数学建模工作,且该法直观性强;抗干扰能力强;可靠性高;可有效地检测刀具磨损状况,是实现刀具磨损在线监测的一种好方法.     

螺杆包张中铣削过程刀具磨损状态的小波分析

利用异形螺杆包络铣削过程中产生的振动信号,采用小波变换对其进行精确的细分,提取出加工过程中刀具磨损的特征信息,据此分析该加工过程的刀具磨损状况,为刀具磨损的状态检测和实时补偿提供了准确的依据.     

基于长短期记忆神经网络的刀具磨损状态监测

刀具磨损状况的实时检测是目前机床加工状态监测的难点,而对刀具的振动信号分析的常用方法是利用神经网络模型来判断刀具磨损状态。为解决循环神经网络(RNN)模型训练过程中梯度容易消亡的现象,提出基于长短期记忆神经网络的刀具磨损状态在线监测。刀具在进行切削加工时,首先通过加速度传感器采集刀具振动信号,然后对振动信号小波包变换进行分解是让信号通过不同的滤波器进行有条件的选择,由此形成不同的能量值,用作为长短期记忆神经网络的特征输入,从而诊断出刀具磨损状态的3种状态故障;最后利用长短期记忆神经网络模型对处理时间序列的数据有比较好的效果,它可以捕捉长期的依赖关系和非线性动态变化。此外,通过与多层(BP)神经网络和(BP)神经网络故障诊断方法进行比较,结果表明,LSTM网络对刀具磨损状态在线监测更加有效。     

深孔钻削监测系统的研究

通过采用一种新研制的应变式传感器和振动传感器,采集四路信号共同监测深孔加工过程中刀具的磨损状态。在大量实验的基础上,分别对深孔钻削过程中的总轴向力、切向扭矩、垂直方向振动和水平方向振动四种信号进行分析,提取反映刀具磨损的特征量,最后用模式识别的方法进行状态分类,得出监测结果。     

基于振动法的铣刀破损特征量提取

建立了铣削加工中振信号的检测系统，并介绍了利用振动信号进行铣刀破损试验的整个试验过程。根据试验数据，对切削过程中产生的振动信号进行了分析与处理，提出了能过反映刀具破损的特征量。为后续的刀具破损系统辩识帮好了充分准备。     

A Feature Extraction Method for the Wear of Milling Tools Based on the Hilbert Marginal Spectrum

Abstract

The Hilbert–Huang transform (HHT) can adaptively delineate complex non-linear, non-stationary signals when used as the Hilbert–Huang marginal spectrum through empirical mode decomposition (EMD) and the Hilbert transform, to highlight local features of signals. Characterized by high resolution, the Hilbert marginal spectrum has been widely applied in mechanical signal processing and fault diagnosis. In the research, an HHT based on the improved EMD was proposed to analyze the cutting force, vibration acceleration (AC), and acoustic emission (AE) signals during tool wear in the milling process. At first, the collected signals were subjected to range analysis, which revealed that tool wear was closely related to the signals collected during the cutting process. Then, EMD was applied to the signals, followed by variance analysis after calculating the energies of each intrinsic mode function (IMF) component. Afterwards, the IMF components significantly influenced by wear degree, while slightly influenced by the three cutting factors (cutting velocity, feed per tooth, and cutting depth), were selected as IMF sensitive to the degree of wear. The HHT was finally applied to the sensitive IMF components of signals containing major tool wear information, thus obtaining the Hilbert marginal spectra of the signals, which were able to reflect the changes in signal amplitude with frequency. On the basis of the Hilbert marginal spectrum, the method defined the feature energy function which was then used as the eigenvector for predicting tool wear in milling processes. The analysis of signals in four tool wear states indicated that the method can extract salient tool wear features.     

基于长短时记忆卷积神经网络的刀具磨损在线监测模型

为了提高机械加工过程中刀具磨损在线监测的准确性,提出了一种基于长短时记忆卷积神经网络（LSTM-CNN）的刀具磨损在线监测模型。在该监测模型中,通过振动、力、声发射传感器对刀具切削过程中的振动、力和声发射信号进行采集,采集的数据其本质为时间序列数据。考虑采集数据的序列和多维度特性,采用LSTM-CNN网络对采集的数据进行序列和多维度特征提取,利用线性回归实现特征到刀具磨损值的映射。通过实验验证了该模型的有效性和可行性,模型的精度较其他几种方法有了较大的提高。     

基于力信号的低频振动制孔刀具磨损状态分析

为分析碳纤维增强树脂基复合材料(CFRP)/钛合金(TC4)叠层材料低频振动制孔工艺下刀具磨损状态,开展基于切削力信号的制孔刀具磨损状态研究.通过采集CFRP/TC4叠层材料低频振动制孔过程中的切削力信号,进行时域和频域分析,探讨各信号特征量与刀具磨损状态之间的联系.研究结果表明:CFRP/TC4叠层材料低频振动制孔轴...     

Estimation of tool wear during CNC milling using neural network-based sensor fusion

Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations.     

电连接器微动磨损的超声识别与性能退化模型

针对振动环境下电连接器易产生微动磨损而接触性能降低这一问题，采用超声检测方法监测微动磨损过程中电连接器接触件间磨屑的特征值，研究了不同振动条件下接触件的磨损程度及接触性能的退化规律。结果表明，振动频率、振动加速度和振动次数对接触面磨屑的堆积和接触电阻的波动都有正向累积效应；电连接器轴向振动时，磨屑累积效应最为明显。接触电阻和磨屑特征值总量在高振频及加速度下呈现极高的相关性。以磨屑特征值构建的麻雀搜索算法优化BP神经网络性能退化模型的平均绝对误差小于5%。     

Effect of tool edge wear on the cutting forces and vibrations in high-speed finish machining of Inconel 718: an experimental study and wavelet transform analysis

High-speed machining has been receiving growing attention and wide applications in modern manufacture. Extensive research has been conducted in the past on tool flank wear and crater wear in high-speed machining (such as milling, turning, and drilling). However, little study was performed on the tool edge wear??the wear of a tool cutting edge before it is fully worn away??that can result in early tool failure and deteriorated machined surface quality. The present study aims to fill this important research gap by investigating the effect of tool edge wear on the cutting forces and vibrations in 3D high-speed finish turning of nickel-based superalloy Inconel 718. A carefully designed set of turning experiments were performed with tool inserts that have different tool edge radii ranging from 2 to 62???m. The experimental results reveal that the tool edge profile dynamically changes across each point on the tool cutting edge in 3D high-speed turning. Tool edge wear increases as the tool edge radius increases. As tool edge wear dynamically develops during the cutting process, all the three components of the cutting forces (i.e., the cutting force, the feed force, and the passive force) increase. The cutting vibrations that accompany with dynamic tool edge wear were analyzed using both the traditional fast Fourier transform (FFT) technique and the modern discrete wavelet transform technique. The results show that, compared to the FFT, the discrete wavelet transform is more effective and advantageous in revealing the variation of the cutting vibrations across a wide range of frequency bands. The discrete wavelet transform also reveals that the vibration amplitude increases as the tool edge wear increases. The average energy of wavelet coefficients calculated from the cutting vibration signals can be employed to evaluate tool edge wear in turning with tool inserts that have different tool edge radii.     

基于SOM神经网络的摩擦状态识别研究

为提取摩擦振动的特征和实现摩擦副摩擦状态的识别,在往复摩擦磨损试验机进行摩擦副混合摩擦和干摩擦状态的摩擦磨损试验。应用谱减法对试验采集的摩擦振动信号进行降噪,计算降噪后的摩擦振动15个特征参数。应用自组织映射(Self-organizing map, SOM)神经网络对摩擦副不同摩擦状态的摩擦振动特征参数进行分析,得到摩擦振动的SOM神经网络神经元分类。研究结果表明,谱减法能消除摩擦磨损试验机的背景噪声,SOM神经网络算法能够有效分析摩擦振动信号的特征,实现摩擦副摩擦状态的识别。     

基于特征相位段的柴油机活塞缸套磨损故障诊断

在柴油机缸盖振动信号中，活塞缸套磨损的特征信号通常被燃烧爆发、气阀关闭及相邻缸的振动所淹没。通过分析振动信号各成份在一个周期内的分布情况，提取了干扰较少的排气冲程阶段信号，并在消除了相邻缸的振动影响后，作为活塞缸套磨损的特征相位段。选取峰峰值、平均振值等6个参数作为诊断特征参数，建立了诊断模型。实例表明，该方法能够有效地诊断出柴油机活塞缸套的磨损故障。     

基于多源同步信号与深度学习的刀具磨损在线识别方法

为提高刀具状态监测系统的实用性、避免实际加工过程中工序变换产生的信号干扰,提出一种基于多源同步信号与深度学习的刀具磨损在线识别方法。该方法利用自动触发的方式实现了机床运行在特定工序时的刀具振动、主轴功率、数控系统参数等多源信号的同步在线采集,保证信号同步性的同时有效避免了因工序变换而产生的信号波动干扰;进一步利用高频振动特征实现了 “切削过程”与“切削间隙”采集样本的准确划分,并基于皮尔逊积矩相关系数筛选出强关联特征,保证了多源监测信号融合样本的可用性;最后基于一维卷积神经网络建立了刀具磨损在线识别模型。实验结果表明,该方法无论从识别精度还是诊断效率,均能实现实际加工过程中刀具磨损状态的在线识别。     

Hybrid Learning for Tool Wear Monitoring

In automated manufacturing systems such as flexible manufacturing systems (FMSs), one of the most important issues is the detection of tool wear during the cutting process. This paper presents a hybrid learning method to map the relationship between the features of cutting vibration and the tool wear condition. The experimental results show that it can be used effectively to monitor the tool wear in drilling. First, a neural network model with fuzzy logic (FNN), responding to learning algorithms, is presented. It has many advantageous features, compared to a backpropagation neural network, such as less computation. Secondly, the experimental results show that the frequency distribution of vibration changes as the tool wears, so the r.m.s. of the different frequency bands measured indicates the tool wear condition. Finally, FNN is used to describe the relationship between the characteristics of vibration and the tool wear condition. The experimental results demonstrate the feasibility of using vibration signals to monitor the drill wear condition.