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数控机床刀具磨损监测实验数据处理方法研究 总被引:3,自引:0,他引:3
数控机床刀具磨损监测对于提高数控机床利用率,减小由于刀具破损而造成的经济损失具有重要意义.有针对性地回顾了国内外各种分析刀具磨损信号方法的研究工作,详细叙述了功率谱分析法、小波变换、人工神经网络以及多传感器信息融合技术的实现形式.通过比较各种数据处理方法的优缺点,提出基于混合智能多传感器信息融合技术是数控机床刀具磨损监测实验数据处理的未来发展的主要方向. 相似文献
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准确监测加工过程刀具磨损状态有助于避免因刀具失效导致的产品质量问题。 建立不同工况的刀具磨损监测模型,往
往需要对每组工况调参以保证精度。 为减少调参并保证预测精度,结合深度森林的超参数少、参数对模型不敏感和训练过程自
适应等优点,利用深度森林建立了多传感器信号及多工况下自主特征选择的刀具磨损状态预测模型。 基于 3 组不同工艺参数
下 TC18 铣削过程的多传感器及磨损数据,以及预测与健康管理(PHM)学会 2010 年高速数控机床刀具健康预测竞赛的开放数
据,深度森林在 3 组工况的预测精度分别为 95. 35% 、96. 63% 和 97. 06% ,在 PHM 数据上为 98. 95% ,验证了深度森林对多工况
下刀具磨损预测的高精度和适用性,为在线监测技术提供了有力的指导。 相似文献
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数控机床刀具磨损与破损的声发射监测法 总被引:1,自引:0,他引:1
蒙斌 《机械工程与自动化》2010,(6)
数控切削加工过程中刀具的磨损与破损是数控机床常见的故障之一,而刀具的磨损与破损程度直接影响零件的加工质量.所以对刀具状态的实时监测就显得十分关键,阐述了用声发射法对其进行在线监测的方法. 相似文献
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基于神经网络多传感器融合的刀具摩损定量监测的研究 总被引:5,自引:0,他引:5
研究了前馈神经网络(FNN)的自构造型学习算法,提出了基于神经网络多传感器融合的一般结构及刀具磨损监测方法,讨论了多传感器的选择、多传感器信号的采集与预处理以及多传感器信号的特征选择与正规化处理,并就铣削过程的刀具磨损监测进行了实验研究,结果表明,所提出的方法可获得93%的识别率。 相似文献
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刀具磨损监测是推动数控机床稳定运行的有效手段。为实现刀具健康实时监测,设计一种基于长短记忆网络的刀具磨损监测方法。将实时采集的切削力信号进行去噪、降维等预处理;用堆叠自编码器进行特征提取,获得影响刀具磨损的本质特征;用长短记忆网络构建刀具磨损监测模型,实现加工过程中刀具磨损监测。利用铣削实验数据进行实例验证,获得磨损量平均绝对误差为0.060 80的铣刀磨损量监测模型,验证了该方法的有效性。 相似文献
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基于传统机器学习的刀具磨损监测模型监测精度低且计算复杂度高,难以满足智能制造的发展需求。而基于深度学习的刀具磨损监测模型数据处理和特征提取能力较强,可明显提高监测精度,使加工过程更智能化,因此广泛应用于刀具磨损监测。根据采用的模型,将基于深度学习的刀具磨损监测分为基于卷积神经网络的刀具磨损监测、基于稀疏自编码网络的刀具磨损监测、基于深度置信网络的刀具磨损监测、基于长短时记忆网络的刀具磨损监测和基于混合模型的刀具磨损监测,介绍了各种深度学习模型的基础理论及基本结构,总结了国内外基于深度学习模型的刀具磨损监测方法,分析了存在的问题,并指出了未来的发展方向。 相似文献
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Somkiat Tangjitsitcharoen Haruetai Lohasiriwat 《The International Journal of Advanced Manufacturing Technology》2018,99(9-12):2219-2230
In order to realize an intelligent CNC machine, this research proposed the in-process tool wear monitoring system regardless of the chip formation in CNC turning by utilizing the wavelet transform. The in-process prediction model of tool wear is developed during the CNC turning process. The relations of the cutting speed, the feed rate, the depth of cut, the decomposed cutting forces, and the tool wear are investigated. The Daubechies wavelet transform is used to differentiate the tool wear signals from the noise and broken chip signals. The decomposed cutting force ratio is utilized to eliminate the effects of cutting conditions by taking ratio of the average variances of the decomposed feed force to that of decomposed main force on the fifth level of wavelet transform. The tool wear prediction model consists of the decomposed cutting force ratio, the cutting speed, the depth of cut, and the feed rate, which is developed based on the exponential function. The new cutting tests are performed to ensure the reliability of the tool wear prediction model. The experimental results showed that as the cutting speed, the feed rate, and the depth of cut increase, the main cutting force also increases which affects in the escalating amount of tool wear. It has been proved that the proposed system can be used to separate the chip formation signals and predict the tool wear by utilizing wavelet transform even though the cutting conditions are changed. 相似文献
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为实现在正常生产条件下进行刀具磨损的长期在线监测,提出了基于主轴电流信号和粒子群优化支持向量机模型(PSO-SVM)的刀具磨损状态间接监测方法。首先对数控机床主轴电机电流信号进行分析,将与刀具磨损相关的主轴电流信号多个特征参数和EMD能量熵进行特征融合作为输入特征向量;其次,通过粒子群寻优算法(PSO)对支持向量机模型(SVM)参数进行优化,建立基于主轴电流信号融合特征和PSO-SVM理论的刀具磨损状态识别模型;最后,通过实验采集某立式加工中心主轴在刀具不同磨损状态下电流信号进行验证,并与传统SVM模型、BP神经网络模型进行了对比分析。结果表明,所提出的方法具有较高的准确率和较好的泛化能力。能够实现正常生产条件下对刀具磨损的长期在线监测。 相似文献
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Kang-Jae Lee Taik-Min Lee Min-Yang Yang 《The International Journal of Advanced Manufacturing Technology》2007,32(1-2):8-17
A tool wear monitoring system is indispensable for better machining productivity, with the guarantee of machining safety by
informing of the time due for changing a tool in automated and unmanned CNC machining. Different from monitoring methods using
other signals, the monitoring of the spindle current has been used without requiring additional sensors on the machine tools.
For reliable tool wear monitoring, only the current signal from tool wear should be extracted from the other parameters to
avoid exhaustive analyses on signals in which all of the parameters are fused together. In this paper, the influences of force
components from different parameters on the measured spindle current are investigated, and a hybrid approach to cutting force
regulation is employed for tool wear signal extraction from the spindle current. Finally, wear levels are verified with experimental
results by means of real-time feedrate aspects, varied to regulate the force component from tool wear. 相似文献
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刀具是数控机床的重要零部件,其性能直接影响着生产加工精度。为实现机床刀具磨损程度的在线分级评估,提出一种刀具磨损状态的评估方法,该方法结合随机森林与主成分分析模型,建立不同工况下主轴电机电流传感器信号样本与刀具磨损等级的非线性映射关系。在不同加工条件下进行刀具性能试验,采集主轴电机的电流信号和铣削加工参数。对信号利用小波包分解、时域统计、频域分析提取特征,利用随机森林得到刀具磨损的分级评估结果。该方法可有效解决样本不平衡的问题,与常见的组合分类方法AdaBoost所得结果相比,该模型能准确地反映刀具的磨损程度,鲁棒性更好。该方法仅利用数控机床内置传感器实现,无需改动机床结构,不影响主轴动态加工性能,可广泛应用于工业数控机床刀具的磨损评估。 相似文献
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Tool wear monitoring in drilling using force signals 总被引:3,自引:0,他引:3
Utilization of force signals to achieve on-line drill wear monitoring is presented in this paper. A series of experiments were conducted to study the effects of tool wear as well as other cutting parameters on the cutting force signals and to establish the relationship between force signals and tool wear as well as other cutting parameters when drilling copper alloy. These experiments involve four independent variables; spindle rotational speed ranging from 600 to 2400 rev min−1, feed rate ranging from 60 to 200 mm min−1, drill diameter ranging from 5 to 10 mm, and average flank wear ranging from 0.1 to 0.9 mm. A statistical analysis provided good correlation between average thrust and drill flank wear. The relationship between cutting force signals and cutting parameters as well as tool wear is then established. The relationship can then be used for on-line drill flank wear monitoring. Feasibility studies show that the use of force signal for on-line drill flank wear monitoring is feasible. 相似文献
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David Kerr James Pengilley Robert Garwood 《The International Journal of Advanced Manufacturing Technology》2006,28(7-8):781-791
Tool wear monitoring is an integral part of modern CNC machine control. Cutting tools must be periodically checked for possible
or actual premature failures, and it is necessary to record the cutting history for a tool’s full life of utilisation. This
means that an on-line monitoring system would be of great benefit to overall process control in manufacturing systems. Computer
vision has already shown promise as a candidate technology for this task. In this paper, we describe the use of digital image
processing techniques in the analysis of images of worn cutting tools in order to assess their degree of wear and thus remaining
useful life. It is shown that a processing strategy using a variety of image texture measures allows for effective visualisation
and assessment of tool wear, and indicates good correlation with the expected wear characteristics. 相似文献
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Tian-Syung Lan Ming-Yung Wang 《The International Journal of Advanced Manufacturing Technology》2009,41(7-8):820-826
Surface roughness, tool wear, and material removal rate (MRR) are major intentions in the modern computer numerical controlled (CNC) machining industry. In this paper, the ${\text{L}}_9 \left( {3^4 } \right)$ orthogonal array of a Taguchi experiment is selected for four parameters (cutting depth, feed rate, speed, and tool nose runoff) with three levels (low, medium, and high) in optimizing the finish turning parameters on an ECOCA-3807 CNC lathe. The surface roughness (Ra) and tool wear ratio (mm?2) are primarily observed as independent objectives for developing two combinations of optimum single-objective cutting parameters. Additionally, the levels of competitive orthogonal array are then proposed between the two parameter sets. Therefore, the optimum competitive multi-quality cutting parameters can then be achieved. Through the machining results of the CNC lathe, it is shown that both tool wear ratio and MRR from our optimum competitive parameters are greatly advanced with a minor decrease in the surface roughness in comparison to those of benchmark parameters. This paper not only proposes a competitive optimization approach using orthogonal array, but also contributes a satisfactory technique for multiple CNC turning objectives with profound insight. 相似文献
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数控机床刀具中的检测与监控就是采用检测与监控系统通过传感和分析处理相关信息,从而对数控机床运行状态和加工过程进行检测与控制,保证数控机床的加工质量,防止产生废品,减少废品率,提高机械加工生产率,提高加工过程的安全性,优化配置制造资源。而检测与监控系统的成败主要取决于系统所收集的信号即有关监控参数的正确选择,也取决于其物理结构上的传感器和监控器以及硬件和软件系统。 相似文献