In-situ density measurement for plastic injection molding via ultrasonic technology

doi:10.1007/s11465-022-0714-2

Frontiers of Mechanical Engineering

2022, Vol. 17

Issue (4): 58 https://doi.org/10.1007/s11465-022-0714-2

本期目录

In-situ density measurement for plastic injection molding via ultrasonic technology

Zhengyang DONG^1,², Peng ZHAO^1,²(

), Kaipeng JI^1,², Yuhong CHEN³, Shiquan GAO⁴, Jianzhong FU^1,²

¹. State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
². Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
³. Beijing Institute of Aeronautical Materials, Beijing 100095, China
⁴. Haitian Plastics Machinery Group Co., Ltd., Ningbo 315801, China

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Abstract：

Density variation during the injection molding process directly reflects the state of plastic melt and contains valuable information for process monitoring and optimization. Therefore, in-situ density measurement is of great interest and has significant application value. The existing methods, such as pressure−volume−temperature (PVT) method, have the shortages of time-delay and high cost of sensors. This study is the first to propose an in-situ density measurement method using ultrasonic technology. The analyses of the time-domain and frequency-domain signals are combined in the proposed method. The ultrasonic velocity is obtained from the time-domain signals, and the acoustic impedance is computed through a full-spectral analysis of the frequency-domain signals. Experiments with different process conditions are conducted, including different melt temperature, injection speed, material, and mold structure. Results show that the proposed method has good agreement with the PVT method. The proposed method has the advantages of in-situ measurement, non-destructive, high accuracy, low cost, and is of great application value for the injection molding industry.

Key words： ultrasonic measurement melt density in-situ measurement injection molding

收稿日期: 2022-01-19 出版日期: 2023-01-10

Corresponding Author(s): Peng ZHAO

引用本文:

. [J]. Frontiers of Mechanical Engineering, 2022, 17(4): 58.
Zhengyang DONG, Peng ZHAO, Kaipeng JI, Yuhong CHEN, Shiquan GAO, Jianzhong FU. In-situ density measurement for plastic injection molding via ultrasonic technology. Front. Mech. Eng., 2022, 17(4): 58.

链接本文:

https://academic.hep.com.cn/fme/CN/10.1007/s11465-022-0714-2
https://academic.hep.com.cn/fme/CN/Y2022/V17/I4/58

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2s + 1	Filter window size
b	Intercept of the linear fitting
c	Ultrasonic velocity
f	Frequency
f_c	Central frequency of the transducer
h	Thickness of plastic melt
H(f)	Transfer function of the echo signals
j	Imaginary unit
k	Slope of the linear fitting
K	Proportionality propagation coefficient
m	Coefficient that convert the unit of damping coefficient from Np/cm to dB/cm
P	Melt pressure
$R u 1 u 2$	Correlation function of u₁ and u₂
R₀, R₁	Reflection coefficients of the Material 1/Material 2 surface and Material 2/Material 3 surface, respectively
$Δ t$	Time delay between $u 1 (t)$ and $u 2 (t)$
$T 0$ , $T 0 ′$	Transmission coefficients of the ultrasonic waves passing forward and backward through the Material 1/Material 2 surface, respectively
$T$	Melt temperature
$u (t)$ :	Time-domain signals
$U 0$	Original ultrasonic signal generated ultrasonic transducer
$U 1$ , $U 2$	First and second echo signals reflected from the two surfaces of Material 2, respectively
$U (f)$	Amplitude spectrum of signals
$U 1 (f)$ , $U 2 (f)$	Amplitude spectrum of $U 1$ and $U 2$ , respectively
$V$	Specific volume
$Z 0$ , $Z 1$ , $Z 2$	Acoustic impedances of Materials 1, 2, and 3, respectively
$α$	Damping coefficient
$ρ$	Density

1	C Yang, L J Su, C N Huang, H X Huang, J M Castro, A Y Yi. Effect of packing pressure on refractive index variation in injection molding of precision plastic optical lens. Advances in Polymer Technology, 2011, 30(1): 51–61 https://doi.org/10.1002/adv.20211
2	P Zhao, W M Yang, X M Wang, J G Li, B Yan, J Z Fu. A novel method for predicting degrees of crystallinity in injection molding during packing stage. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2019, 233(1): 204–214 https://doi.org/10.1177/0954405417718593
3	R Hayu, H Sutanto, Z Ismail. Accurate density measurement of stainless steel weights by hydrostatic weighing system. Measurement, 2019, 131: 120–124 https://doi.org/10.1016/j.measurement.2018.08.033
4	A Nemiroski, A A Kumar, S Soh, D V Harburg, H D Yu, G M Whitesides. High-sensitivity measurement of density by magnetic levitation. Analytical Chemistry, 2016, 88(5): 2666–2674 https://doi.org/10.1021/acs.analchem.5b03918
5	S Davidson, M Perkin. An investigation of density determination methods for porous materials, small samples and particulates. Measurement, 2013, 46(5): 1766–1770 https://doi.org/10.1016/j.measurement.2012.11.030
6	X D Zhou, Y Zhang, T Mao, H M Zhou. Monitoring and dynamic control of quality stability for injection molding process. Journal of Materials Processing Technology, 2017, 249: 358–366 https://doi.org/10.1016/j.jmatprotec.2017.05.038
7	H Xu, D M Wu, Q X Zhu, Y J Zhang. Research of precision injection control system based on the on-line measurement of polymer melt density. Advanced Materials Research, 2012, 383: 5136–5141 https://doi.org/10.4028/www.scientific.net/AMR.383-390.5136
8	R X Gao, X Y Tang, G Gordon, D O Kazmer. Online product quality monitoring through in-process measurement. CIRP Annals, 2014, 63(1): 493–496 https://doi.org/10.1016/j.cirp.2014.03.041
9	J Y Chen, K J Yang, M S Huang. Online quality monitoring of molten resin in injection molding. International Journal of Heat and Mass Transfer, 2018, 122: 681–693 https://doi.org/10.1016/j.ijheatmasstransfer.2018.02.019
10	J Wang, C Hopmann, C Kahve, T Hohlweck, J Alms. Measurement of specific volume of polymers under simulated injection molding processes. Materials & Design, 2020, 196: 109136 https://doi.org/10.1016/j.matdes.2020.109136
11	P Zhao, J F Zhang, Z Y Dong, J Y Huang, H W Zhou, J Z Fu, L S Turng. Intelligent injection molding on sensing, optimization, and control. Advances in Polymer Technology, 2020, 2020: 7023616 https://doi.org/10.1155/2020/7023616
12	X W Zhou, Y Zhang, W J Yu, M Y Li, Y H Chen, H M Zhou. An imaging performance analysis method correlated with geometrical deviation for the injection molded high-precision aspheric negative plastic lens. Journal of Manufacturing Processes, 2020, 58: 1115–1125 https://doi.org/10.1016/j.jmapro.2020.09.017
13	C Abeykoon. A novel soft sensor for real-time monitoring of the die melt temperature profile in polymer extrusion. IEEE Transactions on Industrial Electronics, 2014, 61(12): 7113–7123 https://doi.org/10.1109/TIE.2014.2321345
14	F Suñol, D A Ochoa, J E Garcia. High-precision time-of-flight determination algorithm for ultrasonic flow measurement. IEEE Transactions on Instrumentation and Measurement, 2019, 68(8): 2724–2732 https://doi.org/10.1109/TIM.2018.2869263
15	G L Zhao, S Z Liu, C Zhang, L Jin, Q X Yang. Quantitative testing of residual deformation in plate with varying thickness based on nonlinear ultrasound. Materials & Design, 2022, 214: 110402 https://doi.org/10.1016/j.matdes.2022.110402
16	J Y Zheng, Y Zhang, D S Hou, Y K Qin, W C Guo, C Zhang, J F Shi. A review of nondestructive examination technology for polyethylene pipe in nuclear power plant. Frontiers of Mechanical Engineering, 2018, 13(4): 535–545 https://doi.org/10.1007/s11465-018-0515-9
17	M Kariminejad, D Tormey, S Huq, J Morrison, M McAfee. Ultrasound sensors for process monitoring in injection moulding. Sensors, 2021, 21(15): 5193 https://doi.org/10.3390/s21155193
18	T Ageyeva, S Horváth, J G Kovács. In-mold sensors for injection molding: on the way to Industry 4.0. Sensors, 2019, 19(16): 3551 https://doi.org/10.3390/s19163551
19	P Zhao, Y Zhao, H Kharbas, J F Zhang, T Wu, W M Yang, J Z Fu, L S Turng. In-situ ultrasonic characterization of microcellular injection molding. Journal of Materials Processing Technology, 2019, 270: 254–264 https://doi.org/10.1016/j.jmatprotec.2019.03.012
20	E C Brown, L Mulvaney-Johnson, P D Coates. Ultrasonic measurement of residual wall thickness during gas assisted injection molding. Polymer Engineering and Science, 2007, 47(11): 1730–1739 https://doi.org/10.1002/pen.20832
21	P Zhao, K P Ji, J F Zhang, Y H Chen, Z Y Dong, J G Zheng, J Z Fu. In-situ ultrasonic measurement of molten polymers during injection molding. Journal of Materials Processing Technology, 2021, 293: 117081 https://doi.org/10.1016/j.jmatprotec.2021.117081
22	L J Zhao, Y Lai, C Pei, C K Jen, K D Wu. Real-time diagnosing polymer processing in injection molding using ultrasound. Journal of Applied Polymer Science, 2012, 126(6): 2059–2066 https://doi.org/10.1002/app.36868
23	C L Shepard, B J Burghard, M A Friesel, B P Hildebrand, X Moua, A A Diaz, C W Enderlin. Measurements of density and viscosity of one- and two-phase fluids with torsional waveguides. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 1999, 46(3): 536–548 https://doi.org/10.1109/58.764840
24	N H Abu-Zahra. Measuring melt density in polymer extrusion processes using shear ultrasound waves. The International Journal of Advanced Manufacturing Technology, 2004, 24(9): 661–666 https://doi.org/10.1007/s00170-003-1743-6
25	J van Deventer, J Delsing. Thermostatic and dynamic performance of an ultrasonic density probe. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2001, 48(3): 675–682 https://doi.org/10.1109/58.920692
26	R Raišutis, R Kažys, L Mažeika. Application of the ultrasonic pulse-echo technique for quality control of the multi-layered plastic materials. NDT & E International, 2008, 41(4): 300–311 https://doi.org/10.1016/j.ndteint.2007.10.008
27	C Knapp, G Carter. The generalized correlation method for estimation of time delay. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1976, 24(4): 320–327 https://doi.org/10.1109/TASSP.1976.1162830
28	T M Yu, F C Jiang, J H Wang, Z Q Wang, Y P Chang, C H Guo. Acoustic insulation and absorption mechanism of metallic hollow spheres composites with different polymer matrix. Composite Structures, 2020, 248: 112566 https://doi.org/10.1016/j.compstruct.2020.112566
29	A T J Hayward. Compressibility equations for liquids: a comparative study. British Journal of Applied Physics, 1967, 18(7): 965–977 https://doi.org/10.1088/0508-3443/18/7/312
30	J Wang, C Hopmann, M Schmitz, T Hohlweck, J Wipperfürth. Modeling of PVT behavior of semi-crystalline polymer based on the two-domain Tait equation of state for injection molding. Materials & Design, 2019, 183: 108149 https://doi.org/10.1016/j.matdes.2019.108149
31	Z G Ma, W Wei, Y Q Zu, M Huang, P J Zhou, X Z Shi, C T Liu. A novel and simple method to improve thermal imbalance and sink mark of gate region in injection molding. International Communications in Heat and Mass Transfer, 2021, 127: 105498 https://doi.org/10.1016/j.icheatmasstransfer.2021.105498
32	W Michaeli, C Starke. Ultrasonic investigations of the thermoplastics injection moulding process. Polymer Testing, 2005, 24(2): 205–209 https://doi.org/10.1016/j.polymertesting.2004.08.009
33	M R Hoseini, M J Zuo, X D Wang. Denoising ultrasonic pulse-echo signal using two-dimensional analytic wavelet thresholding. Measurement, 2012, 45(3): 255–267 https://doi.org/10.1016/j.measurement.2011.12.007
34	S Smith. Digital Signal Processing: A Practical Guide for Engineers and Scientists. Amsterdam: Elsevier, 2013
35	P Zhao, N Xia, J F Zhang, J Xie, C Q Zhang, J Z Fu. Measurement of molecular orientation using longitudinal ultrasound and its first application in in-situ characterization. Polymer, 2020, 187: 122092 https://doi.org/10.1016/j.polymer.2019.122092

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