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| 1 | Chatter identification of thin-walled parts for intelligent manufacturing based on multi-signal processing显示文摘Machine chatter is still an unresolved and challenging issue in the milling process,and developing an online chatter identification and process monitoring system towards smart manufacturing is an urgent requirement.In this paper,two indicators of chatter detection are investigated.One is the real-time variance of milling force signals in the time domain,and the other one is the wavelet energy ratio of acceleration signals based on wavelet packet decomposition in the frequency domain.Then,a novel classification concept for vibration condition,called slight chatter,is proposed and integrated successfully into the designed multi-classification support vector machine(SVM)model.Finally,a mapping model between image and chatter indicators is established via a distance threshold on the image.The multi-SVM model is trained by the results of three signals as an input.Experiment data and detection accuracy of the SVM model are verified in actual machining.The identification accuracy of 96.66%has proved that the proposed solution is feasible and effective.The presented method can be used to select optimized milling parameters to improve machining process stability and strengthen manufacturing system monitoring. | Dong-Dong Li Wei-Min Zhang Yuan-Shi Li Feng Xue Jürgen Fleischer | 2021 | Advances in Manufacturing2021，9，1： | 2 |
| 2 | 薄壁件铣削颤振特征提取的GA-PE-VMD和MSE方法显示文摘在高速铣削航空零件时,由于薄壁结构刚度较低,容易产生颤振,颤振导致表面质量差,尺寸误差,降低刀具和机器寿命,是性能的主要限制之一。因此,需要一种可靠的检测方法来识别颤振。针对薄壁结构铣削过程中的颤振检测问题,提出一种基于优化变分模态分解和多尺度样本熵的薄壁件颤振特征提取方法。首先,为了解决变分模态分解参数选择问题,提出一种基于遗传算法优化和最小排列熵的参数自适应方法。其次,计算分解信号的能量比作为挑选IMFs的原则,从而进行信号重构。为了解决单尺度样本熵不能很好地反映颤振发生时铣削力信号特征,引入多尺度样本熵对铣削颤振进行检测,并进行了实验验证。结果表明,采用优化变分模态分解算法对信号进行处理,可以避免因模态混叠而造成的颤振信号难以分离的问题。多尺度样本熵比单尺度样本熵更加有利于颤振检测,随着尺度因子的增大,铣削信号的MSE有减小的趋势,且尺度因子为10时的MSE更有利于颤振检测。 | 王瀚彬 李茂月 刘献礼 王志学 孟博洋 | 2023 | 哈尔滨理工大学学报2023，28，2： | 1 |
| 3 | 基于IES的切削颤振孕育期信号降噪方法显示文摘切削颤振孕育期介于稳定切削与颤振爆发之间,该阶段切削力信号中颤振特征具有典型微弱信息特性。采用基于总体经验模态分解(ensemble empirical mode decomposition,简称EEMD)与奇异值分解(singular value decomposition,简称SVD)相结合的方法对颤振孕育期信号进行降噪时,大多存在噪声剔除不充分或微弱目标特征信息失真等问题。首先,通过引入功率谱密度(power spectral density,简称PSD)与常相干函数(common coherency function,简称CCF)对EEMD降噪机制进行改进,使微弱目标特征所在本征模态函数(intrinsic mode function,简称IMF)分量得到有效提取;其次,借助池化原理(pooling principle,简称PP)降低IMF分量复杂度,并联合SVD对其实施分块降噪,以实现对微弱目标特征中所含噪声进行有效消减;最后,耦合上述改进并重构信号,可面向微弱目标特征信号形成基于改进EEMD-SVD(improved EEMD-SVD,简称IES)的降噪方法。分别利用IES与EEMD-SVD对Rossler混沌信号进行降噪处理,并通过比较信噪比、均方误差及平滑度等降噪评价指标,对所提方法在降噪有效性及信息保真度方面的优势进行量化验证。在此基础上,再次借助所提IES方法对变轴向切深铣削实验中颤振孕育期铣削力信号进行降噪分析。结果表明,该方法能显著抑制颤振孕育期信号噪声,并能有效避免微弱颤振特征信号失真问题。 | 郑华林 高炜祥 胡腾 王虎 阳红 | 2022 | 振动.测试与诊断2022，42，6： | 0 |
| 4 | An integrated machine-process-controller model to predict milling surface topography considering vibration suppression显示文摘Surface topography is an important factor in evaluating the surface integrity and service performance of milling parts.The dynamic characteristics of the manufacturing system and machining process parameters significantly influence the machining precision and surface quality of the parts,and the vibration control method is applied in high-precision milling to improve the machine quality.In this study,a novel surface topography model based on the dynamic characteristics of the process system,properties of the cutting process,and active vibration control system is theoretically developed and experimentally verified.The dynamic characteristics of the process system consist of the vibration of the machine tool and piezoelectric ceramic clamping system.The dynamic path trajectory influenced by the processing parameters and workpiece-tool parameters belongs to the property of the cutting process,while different algorithms of active vibration control are considered as controller factors.The milling surface topography can be predicted by considering all these factors.A series of experiments were conducted to verify the effectiveness and accuracy of the prediction model,and the results showed a good correlation between the theoretical analysis and the actual milled surfaces. | Miao-Xian Guo Jin Liu Li-Mei Pan Chong-Jun Wu Xiao-Hui Jiang Wei-Cheng Guo | 2022 | Advances in Manufacturing2022，10，3： | 0 |
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