本文選題：熔融沉積成型3D打印 + 故障診斷��； 參考：《浙江大學》2017年碩士論文

【摘要】：論文課題來源于國家基金項目"熔融沉積成型3D打印的聲發(fā)射監(jiān)控理論與方法研究"(編號:51675481)。本文針對熔融沉積成型3D打印過程,以聲發(fā)射技術(shù)作為信號檢測手段,結(jié)合 EEMD(Ensemble Empirical Mode Decomposition)信號處理方法和 HSMM(Hidden Semi Markov Model)故障狀態(tài)識別方法,圍繞產(chǎn)品缺陷檢測以及同步帶齒故障狀態(tài)診斷展開研究,旨在提出一種切實可行的熔融沉積成型3D打印過程故障診斷方法。論文的主要工作內(nèi)容包括:(1)分析了熔融絲料的粘結(jié)機理,在對熔融沉積成型分層破壞力學模型進行分析的基礎(chǔ)上,結(jié)合絲料的粘結(jié)機理研究了產(chǎn)品缺陷狀態(tài)的故障演化過程;對同步帶的受力情況進行分析,得到其應(yīng)力模型,結(jié)合熔融沉積成型3D打印機的工作特點探究了同步帶齒健康狀態(tài)的故障演化過程;對熔融沉積成型3D打印過程中的聲發(fā)射源進行分析,以實際采集到的聲發(fā)射信號說明熔融沉積成型3D打印過程中聲發(fā)射技術(shù)的可用性。(2)闡述了 EMD的基本原理和算法,分析了 EMD方法的缺陷,在對EMD的改進算法EEMD進行研究的基礎(chǔ)上,提出了基于EEMD的熔融沉積成型3D打印過程故障聲發(fā)射信號處理方法,基于試驗采集到的故障聲發(fā)射信號驗證了 EEMD方法的抗混疊性,使用相關(guān)系數(shù)方法對EEMD分解得到的有效IMF分量進行篩選,分析有效IMF分量的能量特征,證明了 EEMD方法分解熔融沉積成型3D打印過程故障聲發(fā)射信號的有效性。(3)研究了 HSMM的基本原理和算法,為了提高HSMM的魯棒性和穩(wěn)定性,對多組觀測序列訓練HSMM的問題進行研究。結(jié)合HSMM和EEMD提出基于HSMM和EEMD的熔融沉積成型3D打印過程故障診斷方法,并使用Java語言對HSMM程序進行了實現(xiàn)。(4)設(shè)計多組對比試驗,對論文中提出的故障診斷方法進行研究,試驗結(jié)果表明,基于HSMM和EEMD的熔融沉積成型3D打印過程故障診斷方法具有很高的診斷準確度,非常適合于熔融沉積成型3D打印過程故障診斷。
[Abstract]:The thesis is based on the National Foundation project, "study on the Theory and method of Acoustic Emission Monitoring for 3D Printing of Melt deposition Molding" (No.: 51675481).In this paper, for the 3D printing process of melt deposition molding, acoustic emission technology is used as signal detection method, combined with EEMD(Ensemble Empirical Mode signal processing method and HSMM(Hidden Semi Markov Model fault state identification method.Based on the research of product defect detection and fault state diagnosis of synchronous belt teeth, a feasible fault diagnosis method for 3D printing process of molten deposition molding is proposed.The main work of this paper includes: (1) analyzing the bonding mechanism of the fused filament. Based on the analysis of the delamination failure model of the melt deposition forming, the fault evolution process of the defect state of the product is studied in combination with the bonding mechanism of the filament.The stress model of the synchronous belt is analyzed and the fault evolution process of the healthy state of the synchronous belt tooth is discussed according to the working characteristics of the melt deposition forming 3D printer.In this paper, the acoustic emission sources in the process of 3D printing of melt deposition are analyzed, and the availability of acoustic emission technology in the process of 3D printing of melt deposition is illustrated with the collected acoustic emission signals. (2) the basic principle and algorithm of EMD are expounded.The defects of EMD method are analyzed. Based on the study of the improved algorithm EEMD of EMD, a method of processing fault acoustic emission signal in 3D printing process of melt deposition molding based on EEMD is proposed.The anti-aliasing property of EEMD method is verified based on the fault acoustic emission signals collected from the experiment. The effective IMF components obtained from EEMD decomposition are screened by correlation coefficient method, and the energy characteristics of the effective IMF components are analyzed.It is proved that the EEMD method is effective to decompose the fault acoustic emission signals in the 3D printing process of melt deposition molding. The basic principle and algorithm of HSMM are studied. In order to improve the robustness and stability of HSMM,The problem of training HSMM with multiple observation sequences was studied.Combined with HSMM and EEMD, a fault diagnosis method based on HSMM and EEMD for 3D printing process of molten deposition molding is put forward, and a multi-group comparative test is designed by using Java language to implement HSMM program. The fault diagnosis method proposed in this paper is studied.The experimental results show that the fault diagnosis method based on HSMM and EEMD for 3D printing process of molten deposition molding has high diagnostic accuracy and is very suitable for fault diagnosis in 3D printing process of melt deposition molding.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP334.8

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本文編號：1734662