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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (10): 2093-2103    DOI: 10.3785/j.issn.1008-973X.2022.10.021
    
Orthogonal experiment of fused deposition molding 3D printing drawing defects
Yong-jian BAI1(),Yun CHEN1,2,*(),Si ZHANG1,Kang CHEN1,Shi-jie SU1,2
1. School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China
2. Jiangsu Key Laboratory of Advanced Manufacturing for Marine Machinery and Equipment, Zhenjiang 212003, China
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Abstract  

An orthogonal analysis method of fused deposition molding (FDM) 3D printing drawing defects based on vibration signals was proposed in order to solve the abnormal working conditions of wire drawing in the printing process in time. The single factor test was conducted, and the time domain analysis of the signal collected by the acceleration vibration sensor was conducted. The correlation kurtosis, peak factor and pulse factor eigenvalues were extracted to determine the relationship between the four printing parameters, namely, nozzle temperature, pullback distance, pullback rate, empty driving speed and drawing defects of the parts. The four-factor and three-level orthogonal test was conducted based on the single-factor test, and the extracted time-domain eigenvalues of vibration signals were taken as the evaluation index. The test data were analyzed by range and variance analysis. The test results show that the on-line monitoring method based on acceleration vibration sensor can identify the drawing defects very well. The drawing distance and nozzle temperature significantly affect the drawing defects when the used material is PLA, while the effects of empty driving speed and drawing speed are not significant. The wire drawing condition of the printing model is the best when the nozzle temperature is 190 ℃, the drawing speed is 50 mm/s, the drawing distance is 12 mm and the empty driving speed is 50 mm/s.

Key words： fused deposition molding (FDM)      3D printing      orthogonal experiment      drawing defect     
Received: 11 October 2021      Published: 25 October 2022
CLC:  TP 334  
Fund:  国家自然科学基金资助项目(51875003)；国家自然科学基金青年科学基金资助项目(51705214)；江苏省自然科学基金青年基金资助项目(BK20170582)
Corresponding Authors: Yun CHEN     E-mail: 1062153533@qq.com;yunchen@just.edu.cn
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Yong-jian BAI
Yun CHEN
Si ZHANG
Kang CHEN
Shi-jie SU
Cite this article:

Yong-jian BAI,Yun CHEN,Si ZHANG,Kang CHEN,Shi-jie SU. Orthogonal experiment of fused deposition molding 3D printing drawing defects. Journal of ZheJiang University (Engineering Science), 2022, 56(10): 2093-2103.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.10.021     OR     https://www.zjujournals.com/eng/Y2022/V56/I10/2093


熔融沉积成型3D打印拉丝缺陷的正交实验研究

为了及时解决打印过程中的拉丝异常工况，提出基于振动信号的熔融沉积成型(FDM)3D打印拉丝缺陷正交分析方法. 开展单因素试验，对加速度振动传感器采集的信号进行时域分析，提取相关峭度、峰值因子和脉冲因子特征值，确定喷头温度、回抽距离、回抽速率和空驶速率4个打印参数与零件拉丝缺陷的关系. 在单因素试验的基础上进行四因素三水平正交试验，以提取的振动信号时域特征值为评价指标，通过极差与方差分析对试验数据进行分析. 试验结果表明，利用该方法可以对拉丝缺陷进行识别，其中在使用材料为PLA的情况下，回抽距离和喷头温度对零件拉丝缺陷的影响显著，空驶速率和回抽速率影响不显著，当喷头温度为190 ℃、回抽速率为50 mm/s、回抽距离为12 mm、空驶速度为50 mm/s时，打印模型的拉丝情况最好.


关键词： 熔融沉积成型(FDM),  3D打印,  正交试验,  拉丝缺陷 
Fig.1 3D printing vibration monitoring platform
水平 VW/(mm·s?1) t/℃ LW/mm VL/(mm·s?1)
1 10 190 1 10
2 30 195 3 30
3 50 200 6 50
4 60 205 9 60
5 80 210 12 80
Tab.1 Single factor test level table
试验编号 t/℃ VW/(mm·s?1) LW/mm VL/(mm·s?1)
S1 190 10 1 10
S2 190 50 6 50
S3 190 80 12 80
S4 200 10 6 80
S5 200 50 12 10
S6 200 80 1 50
S7 210 10 12 50
S8 210 50 1 80
S9 210 80 6 10
Tab.2 Orthogonal design test table
Fig.2 Wire drawing phenomenon of different factors
Fig.3 Time domain diagram of sensor Ⅲ different factors and horizontal vibration signals
Fig.4 Relationship between different factor levels and kurtosis indexes
Fig.5 Relationship between different factor levels and peak factor indexes
Fig.6 Relationship between different factor levels and impulse factor index
Fig.7 Printed physical pictures of 9 groups of orthogonal tests
试验编号 传感器Ⅰ 传感器Ⅱ 传感器Ⅲ
K C I K C I K C I
S1 6.38 5.54 5.89 5.17 3.82 5.18 4.83 5.48 6.70
S2 4.88 4.64 5.73 4.94 4.32 5.05 5.71 4.37 5.71
S3 6.58 5.47 7.98 6.62 4.95 6.33 6.64 5.77 7.58
S5 7.24 7.98 8.26 7.01 6.07 6.96 6.86 6.03 6.45
S6 4.48 4.40 5.13 3.90 3.92 5.27 3.87 3.79 4.54
S7 6.05 5.20 6.58 5.30 5.32 6.12 5.07 5.32 7.02
S8 4.35 4.81 5.36 4.56 3.62 5.34 4.39 4.83 5.54
S9 3.65 4.32 5.02 3.73 3.29 5.08 4.14 3.83 4.96
Tab.3 Characteristic indexes of vibration signals of three sensors
指标 因素 K1 K2 K3 $ \overline {{K_1}}$ $ \overline {{K_2}} $ $ \overline {{K_3}} $ R 水平主次 因素主次
峭度 A 17.85 17.40 14.04 5.95 5.80 4.68 1.27 1 2 3 C > A > B > D
B 18.09 16.47 14.70 6.03 5.49 4.90 1.13 1 2 3
C 15.21 14.19 19.86 5.07 4.73 6.62 1.89 3 1 2
D 17.28 15.42 16.59 5.76 5.14 5.53 0.62 1 3 2
峰值因子 A 15.66 18.75 14.34 5.22 6.25 4.78 1.47 2 1 3 A > C > D > B
B 17.10 17.43 14.19 5.70 5.81 4.73 1.08 2 1 3
C 14.76 15.33 18.66 4.92 5.11 6.22 1.30 3 2 1
D 17.85 14.25 16.65 5.95 4.75 5.55 1.20 1 3 2
脉冲因子 A 19.59 19.44 16.95 6.53 6.48 5.65 0.88 1 2 3 C > A > D > B
B 18.54 19.35 18.12 6.18 6.45 6.04 0.41 2 1 3
C 16.38 16.80 22.83 5.46 5.60 7.61 2.15 3 2 1
D 19.17 17.43 19.41 6.39 5.81 6.47 0.66 3 1 2
Tab.4 Analysis of index range under sensor Ⅰ
Fig.8 Horizontal trend diagram of various sensor indicators and factors
指标 方差来源 df SS MS F
传感器Ⅰ峭度 A 2 8.53 4.27 5.47
B 2 5.00 2.50 3.21
C 2 17.34 8.67 11.13
D 2 1.85 0.93 1.19
传感器Ⅱ峭度 A 2 9.94 4.97 7.42
B 2 5.88 2.94 4.39
C 2 7.49 3.75 5.59
D 2 5.01 2.51 3.74
传感器Ⅲ峭度 A 2 5.56 2.78 4.09
B 2 1.03 0.52 0.76
C 2 26.59 13.29 19.59
D 2 2.05 1.02 1.51
Tab.5 Analysis of variance of kurtosis index
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