Evaluation of ultrasonic cleaning process

User Rating:  / 0
PoorBest 

Authors:


V.Morkun, orcid.org/0000-0003-1506-9759, Kryvyi Rih National University, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.Kravchenko, orcid.org/0000-0003-0667-2695, Kryvyi Rih National University, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


повний текст / full article



Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2021, (3): 117 - 123

https://doi.org/10.33271/nvngu/2021-3/117



Abstract:



Purpose.
To establish relationship between intensity of ultrasonic cleaning and an ultrasonic response fixed at a set point. To determine the influence of liquid contamination on this signal. To specify parameters of an ultrasonic signal to evaluate the course of cleaning a preset section of a body. To define limiting values indicating efficiency of ceasing the cleaning process.


Methodology.
The research is based on simulation of ultrasonic wave propagation in the heterogeneous environment and subsequent analysis of the signals by using software.


Findings.
Dependences are established to form evaluation of the course of the cleaning process by parameters of the ultrasonic response. It is found that both the bodys state and that of the liquid affect the ultrasonic response obtained by the sensor at the set point during ultrasonic cleaning. With high contamination (>30%) the state of the liquid becomes a critical factor for forming the signal of the ultrasonic response. With that, there is an abrupt reduction of threshold signal arrival time and increase in the main amplitude, which is to be one of indicators of ceasing the cleaning process in case of its inefficiency. With lower contamination, suspended contaminant particles only correct the signal to some extent without distorting it. There is a relationship between reduction of contamination, the arrival time of threshold signal, on the one hand, and the value of the main amplitude and the 2nd and 3rd-order nonlinearity coefficients, on the other hand. The arrival of the threshold value of the signal is the major parameter determining intensity of cleaning, i.e. reduction of contaminant thickness, which is determined by the value of the main amplitude of the signal and the 2nd and 3rd-order nonlinearity coefficients.


Originality.
For the first time, methods for evaluating the course of the ultrasonic cleaning process by analyzing ultrasonic responses at a specified point have been developed.


Practical value.
To consider spatial distribution of the ultrasonic cleaning process, its control is to be based on evaluating the bodys contamination in several preset points. To implement this, the character of dependences between changes in contamination of the body section and parameters of the fixed ultrasonic response is determined. Impacts are considered of suspended contaminant particles in the liquid on the signal analyzed. The observed relationship will provide the basis for building the spatially distributed control over ultrasonic cleaning considering the cleaned bodys state.



Keywords:
ultrasonic cleaning, liquid contamination, nonlinear factors, simulation

References.


1. Xu, H., Tu, J., Niu, F., & Yang, P. (2016). Cavitation dose in an ultrasonic cleaner and its dependence on experimental parameters. Applied Acoustics, 101, 179184. https://doi.org/10.1016/j.apacoust.2015.08.020.

2. Saalbach, K.-A., Twiefel, J., & Wallaschek, J. (2019). Self-sensing cavitation detection in ultrasound-induced acoustic cavitation. Ultrasonics, 94, 401-410. https://doi.org/10.1016/j.ultras.2018.06.016.

3. Yamashita, T., & Ando, K. (2019). Low-intensity ultrasound induced cavitation and streaming in oxygen-supersaturated water: Role of cavitation bubbles as physical cleaning agents. Ultrasonics Sonochemistry, 52(1), 268-279. https://doi.org/10.1016/j.ultsonch.2018.11.025.

4. Duran, F., & Teke, M. (2018). Design and implementation of an intelligent ultrasonic cleaning device. Intelligent Automation and Soft Computing, 1-10. https://doi.org/10.31209/2018.11006161.

5. Tangsopha, W., & Thongsri, J. (2020). A Novel Ultrasonic Cleaning Tank Developed by Harmonic Response Analysis and Computational Fluid Dynamics. Metals, 10(3), 335. https://doi.org/10.3390/met10030335.

6. Nigmetzyanov, R.I., Kazantsev, V.F., Prikhodko, V.M., Sundukov,S.K., & Fatyukhin, D.S. (2019). Improvement in Ultrasound Liquid Machining by Activating Cavitational Clusters. Russian Engineering Research, 8, 699-702. https://doi.org/10.3103/S1068798X19080112.

7. Zhang, X., Fu, Z.-Q., Li, S.-Y., Zou, T., & Wang, B. (2017). Atime/space separation based 3D fuzzy modeling approach for nonlinear spatially distributed systems. International Journal of Automation and Computing, (15), 52-65. https://doi.org/10.1007/s11633-017-1080-0.

8. Morkun, V., & Kravchenko, O. (2020). Adaptive control over ultrasonic cleaning of mining equipment. E3S Web of Conferences, (2020), 01005. https://doi.org/10.1051/e3sconf/202020101005.

9. Rajani, C., Klami, A., Salmi, A., Rauhala, T., Hggstrm, E., & Myllymki, P. (2018). Detecting industrial fouling by monotonicity during ultrasonic cleaning. Aalborg: IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), 1-6. https://doi.org/10.1109/MLSP.2018.8517080.

10. Simeone, A., Woolley, E., Escrig, J., & Watson, N.J. (2020). Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression. Sensors 2020, 20, 3642. https://doi.org/10.3390/s20133642.

11. Papa, I., Lopresto, V., & Langella, A. (2021). Ultrasonic inspection of composites materials: Application to detect. International Journal of Lightweight Materials and Manufacture, 4(1), 37-42. https://doi.org/10.1016/j.ijlmm.2020.04.002.

12. Majhi, S., Mukherjee, A., George, N., Karaganov, V., & Uy, B. (2021). Corrosion Monitoring in Steel Bars using Laser Ultrasonic Guided Waves and Advanced Signal Processing. Mechanical Systems and Signal Processing, 149, 107176. https://doi.org/10.1016/j.ymssp.2020.107176.

13. Liao, Z., Zhang, X., Liu, T., Jia, J., & Tu, S.T. (2020). Characteristics of high-temperature equipment monitoring using dry-coupled ultrasonic waveguide transducers. Ultrasonics, 108, 106236. https://doi.org/10.1016/j.ultras.2020.106236.

14. Porkuian, O., Morkun, V., & Morkun, N. (2020). Measurement of the ferromagnetic component content in the ore suspension solid phase, Ultrasonics, 105, 106103. https://doi.org/10.1016/j.ultras.2020.106103.

15. Yang, Z.-F., Tian, Y., Zhou, H.-Q., Xu, Y., Zhang W.-B., & Li,J.-M. (2016). Nonlinear Ultrasonic Response of TATB-Based Polymer. 19th World Conference on Non-Destructive Testing 2016, 1-8.

16. Yang, Z., Tian, Y., Li, W., Zhou, H., Zhang, W., & Li, J. (2017). Experimental Investigation of the Acoustic Nonlinear Behavior in Granular Polymer Bonded Explosives with Progressive Fatigue Damage. Materials, 10, 660. https://doi.org/10.3390/ma10060660.

17. Morkun, V., Morkun, N., & Pikilniak, A. (2019). The Propagation of Ultrasonic Waves in Gas-containing SuspensionsCambridge. Scholars Publishing. ISBN: 1-5275-1814-0.

 

Visitors

7350769
Today
This Month
All days
44
40272
7350769

Guest Book

If you have questions, comments or suggestions, you can write them in our "Guest Book"

Registration data

ISSN (print) 2071-2227,
ISSN (online) 2223-2362.
Journal was registered by Ministry of Justice of Ukraine.
Registration number КВ No.17742-6592PR dated April 27, 2011.

Contacts

D.Yavornytskyi ave.,19, pavilion 3, room 24-а, Dnipro, 49005
Tel.: +38 (056) 746 32 79.
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
You are here: Home Monographs EngCat Archive 2021 Content №3 2021 Evaluation of ultrasonic cleaning process