面向熔融沉积成型的3D打印机故障声发射监控方法
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摘要
针对熔融沉积成型(FDM)3D打印机中打印喷头容易出现打印材料断丝或耗尽和喷头阻塞的故障模式,分别设计并开展2组实验,研究基于声发射传感器的故障监控方法.为了减小对传感器信号数据进行处理和存储的负担,并提升监控的实时性,使用基于声发射波击(AE hit)的参数化声发射信号处理及特征值提取方法.通过实验采集到了传感器数据并进行信号处理,研究故障模式和特征值之间的联系,得到最敏感的AE hit关键特征值.使用K-means聚类算法对两类故障模式进行同时识别研究.结果表明,在0.2s的时间分辨率下,基于AE hit的绝对能量和击数特征值,提出的监控方法对典型故障的识别准确率分别为94.62%和93.80%.
A monitoring method based on acoustic emission(AE)was proposed aiming at the typical failure modes of material filament breakage or run out and extruder blockage in the extruder of fused deposition modeling(FDM)3Dprinter.Two experiments were designed and conducted accordingly.The AE signals were processed and the related features were extracted parametrically based on AE hits in order to reduce the costs on sensor data computing and storing and improve the real-time monitoring performance.Sensor data from the experiments were collected and analyzed.The relationship between the features of AE hits and failure modes was estimated.The knowledge of the most relevant features of AE hits was obtained.The K-means clustering algorithm was applied to simultaneously identify the two types of failure modes based on the AE features of absolute energy and counts respectively.Clustering results of the proposed monitoring method showed that the accuracy rates were 94.62% and 93.80% under the time resolution of0.2s.
A monitoring method based on acoustic emission(AE)was proposed aiming at the typical failure modes of material filament breakage or run out and extruder blockage in the extruder of fused deposition modeling(FDM)3Dprinter.Two experiments were designed and conducted accordingly.The AE signals were processed and the related features were extracted parametrically based on AE hits in order to reduce the costs on sensor data computing and storing and improve the real-time monitoring performance.Sensor data from the experiments were collected and analyzed.The relationship between the features of AE hits and failure modes was estimated.The knowledge of the most relevant features of AE hits was obtained.The K-means clustering algorithm was applied to simultaneously identify the two types of failure modes based on the AE features of absolute energy and counts respectively.Clustering results of the proposed monitoring method showed that the accuracy rates were 94.62% and 93.80% under the time resolution of0.2s.
引文
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[3]HUANG Y,LEU M C,MAZUMDER J.Additive manufacturing:current state,future potential,gaps and needs,and recommendations[J].Journal of Manufacturing Science and Engineering,2015,137(1):014001.
[4]GUO N,LEU M C.Additive manufacturing:technology,applications and research needs[J].Frontiers of Mechanical Engineering,2013,8(3):215-243.
[5]WOHLERS T T.Wohlers Report 2015:additive manufacturing and 3D printing state of the industry:annual worldwide progress report[M].[S.l.]:Wohlers Associates,2015.
[6]PEI E,IAN CAMPBELLR,DE BEER D.Entry-level RP machines:how well can they cope with geometric complexity?[J].Assembly Automation,2011,31(2):153-160.
[7]TAPIA G,ELWANY A.A review on process monitoring and control in metal-based additive manufacturing[J].Journal of Manufacturing Science and Engineering,2014,136(6):060801.
[8]US DEPARTMENT OF COMMERCE N.Roadmappingworkshop:measurement science for prognostics and health management of smart manufacturing systems[EB/OL].2015-08-01.http://www.nist.gov/el/isd/phm4sms-workshop.cfm.
[9]KANTAROS A,KARALEKAS D.Fiber Bragg grating based investigation of residual strains in ABS parts fabricated by fused deposition modeling process[J].Materials and Design,2013,50:44-50.
[10]LOTT P,SCHLEIFENBAUM H,MEINERS W,et al.Design of an optical system for the in situ process monitoring of selective laser melting(SLM)[J].Physics Procedia,2011,12,Part A:683-690.
[11]RAO P,LIU J,ROBERSON D,et al.Online real-time quality monitoring in additive manufacturing processes using heterogeneous sensors[J].Journal of Manufacturing Science and Engineering,2015,137(6):061007.
[12]REUTZEL E W,NASSAR A R.A survey of sensing and control systems for machine and process monitoring of directed-energy,metal-based additive manufacturing[J].Rapid Prototyping Journal,2015,21(2):159-167.
[13]TANDON N,CHOUDHURY A.A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings[J].Tribology International,1999,32(8):469-480.
[14]YANG Z,WU H,YU Z,et al.A non-destructive surface burn detection method for ferrous metals based on acoustic emission and ensemble empirical mode decomposition:from laser simulation to grinding process[J].Measurement Science and Technology,2014,25(3):035602.
[15]POMPONI E,VINOGRADOV A.A real-time approach to acoustic emission clustering[J].Mechanical Systems and Signal Processing,2013,40(2):791-804.
[16]JAMES M.Some methods for classification and analysis of multivariate observations[C]∥Proceedings of the5th Berkeley Symposium on Mathematical Statistics and Probability.Oakland:[s.n.],1967:281-297.
[17]KHARRAT M,RAMASSO E,PLACET V,et al.A signal processing approach for enhanced acoustic emission data analysis in high activity systems:application to organic matrix composites[J].Mechanical Systems and Signal Processing,2016,70-71:1038-1055.