Artificial Neural Networks for the Prediction of Electrochemical Etched Micro channel Dimensions

In this study, artificial neural network was used to model the micro channel size created with electrochemical etching method in a specific pattern. Special series 5754 aluminum surfaces were coated with employing mask. The pre-designed pattern was then marked to the masked surface with laser, then it was subjected to electrochemical etching process. In this way, micro-patterned channels are formed on the aluminum surface. Various experiments were carried out based on the electrochemical etching parameters, such as concentration (0.1-2.5 M), distance between the electrodes (5-15 cm), operating voltage (15-48 V) and time (6-30 min). And the depth and width of the channels were investigated. Studies conducted under various conditions were modeled with ANN and the synergistic effects of the input and output parameters were explored by the surface graphics obtained as a result of the modeling. This modeling study is a powerful tool in terms of providing a prediction of the channel dimensions of the micro channel fabricated by electrochemical etching for the future related studies. In addition to the modeling, some impressions and inferences obtained from the experiments were also yielded in the conclusion part.

Anahtar Kelimeler:

Electrochemical etching, Microchannel, Modelling, Artificial neural network

Artificial Neural Networks for the Prediction of Electrochemical Etched Micro channel Dimensions

Keywords:

Electrochemical etching, Microchannel, Modelling, Artificial neural network,

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Prakash S. and Kumar S., “Fabrication of microchannels: a review”, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2015, 229(8):1273-1288.
Zhao Q., Cui H., Wang Y., Du X., “Microfluidic platforms toward rational material fabrication for biomedical applications”, Small, 2020, 16(9):1903798.
Samal P., Blitterswijk C., Truckenmüller R., Giselbrecht S., “Grow with the flow: when morphogenesis meets microfluidics”, Advanced Materials, 2019, 31(17):1805764.
Yang Y., Xue Y., Zhang H., Chang H., “Flexible H2O2 microfluidic fuel cell using graphene/Prussian blue catalyst for high performance”, Chemical Engineering Journal, 2019, 369:813-817.
Zhang C., Tang Z., Zhang Z., Shi J., Chen J., Zhang M., “Impact of airside fouling on microchannel heat exchangers”, Applied Thermal Engineering, 2018, 128:42-50.
Liu Y., Zhou W., Lin Y., Chen L., Chu X., Zheng T., Lin J., “Novel copper foam with ordered hole arrays as catalyst support for methanol steam reforming microreactor”, Applied Energy, 2019, 246:24-37.
Ke Y., Zhou W., Chu X., Yuan D., Wan S., Yu W., Liu Y., “Porous copper fiber sintered felts with surface microchannels for methanol steam reforming microreactor for hydrogen production”, International Journal of Hydrogen Energy, 2019, 44(12):5755-5765.
Baydir E. and Aras Ö., “Methanol steam reforming in a microchannel reactor coated with spray pyrolysis method for durable Cu/ZnO nanocatalyst”, Journal of Analytical and Applied Pyrolysis, 2021, 158:105278.
Akman B. and Aras, O., “Usability, durability and regeneration of Ag/ZnO coated microreactor for photocatalytic degradation of methylene blue”, Journal of Molecular Structure, 2022, 1251:132003.
Baydir E. and Aras O. “Increasing biodiesel production yield in narrow channel tubular reactors”, Chemical Engineering and Processing-Process Intensification, 2022, 170:108719.
Schmidt V., Riel H., Senz S., Karg S., Riess W., Gösele U., “Realization of a Silicon Nanowire Vertical Surround-Gate Field-Effect Transistor”, Small, 2006, 2(1):85-88.
In H.J., Field C.R., Pehrsson P.E., “Periodically porous top electrodes on vertical nanowire arrays for highly sensitive gas detection”, Nanotechnology, 2011, 22(35):355501.
Peng K., Jie J., Zhang W., Lee S.T., “Silicon nanowires for rechargeable lithium-ion battery anodes”, Applied Physics Letters, 2008, 93(3):033105.
Zhang G., Finefrock S., Liang D., Yadav G.G., Yang H., Fang H., “Semiconductor nanostructure-based photovoltaic solar cells”, Nanoscale, 2011, 3(6):2430-2443.
Rao P.N. and Kunzru D., “Fabrication of microchannels on stainless steel by wet chemical etching”, Journal of micromechanics and microengineering, 2007, 17(12):N99.
Jung P.G., Jung I.D., Lee S.M., Ko J.S., “Fabrication of self-encapsulated nickel microchannels and nickel nanowalls by reactive ion etching”, Journal of Materials Processing Technology, 2008, 208(1-3):111-116.
Srikanth S., Mohan J.M., Dudala S., Dubey S.K., Javed A., Goel S., “Direct UV laser writing system to photolithographically fabricate optimal microfluidic geometries: Experimental investigations”, Materials Today: Proceedings, 2020, 28:799-803.
Xue B., Geng Y., Yan Y., Ma G., Wang D., He Y., “Rapid prototyping of microfluidic chip with burr-free PMMA microchannel fabricated by revolving tip-based micro-cutting”, Journal of Materials Processing Technology, 2020, 277:116468.
Rodriguez I., Spicar-Mihalic P., Kuyper C.L., Fiorini G.S., Chiu D.T., “Rapid prototyping of glass microchannels”, Analytica Chimica Acta, 2003, 496(1-2): 205-215.
Yang C.R., Fu P.C., Cheng C., Huang M.J., “An integrated micro electro mechanical system–based silicon wet etching process and nano carbon materials used for improving micro direct methanol fuel cells performance”, Materials Today Energy, 2021, 20:100696.
Węglowski M.S., “Electrolytic etching in welding metallography”, Institute of Welding Bulletin, 2013, 2:5-10.
Deng T., Zhu Z., Li X., Ma T., Wang Q., “Experimental study on electrochemical etching for titanium printed circuit heat exchanger channels”, Journal of Materials Processing Technology, 2020, 282:116669.
Sabahi N., Razfar M.R., Hajian M., “Experimental investigation of surfactant-mixed electrolyte into electrochemical discharge machining (ECDM) process”, Journal of Materials Processing Technology, 2017, 250:190-202.
Sarma U. and Joshi S.N., “Numerical modelling and simulation of microchannel fabrication on polycarbonate using Laser-Induced Plasma Assisted Ablation (LIPAA)”, Optik, 2020, 223:165379.
Abate K., “Photochemical etching of metals”, Metal Finishing, 1997, 95(1):39-44.
Örkçü H.H., Doğan M., Örkçü M.A., “Hybrid Applied Optimization Algorithm for Training Multi-Layer Neural Networks in the Data Classification”, Gazi University Journal of Science, 2015, 28(1):115-132.
Topal A., “Aggregate Classification by Using 3D Image Analysis Technique”, Gazi University Journal of Science, 2011, 24(4):773-780.
Kaya D., Türk M., Kaya T., “Examining the effect of dimension reduction on EEG signals by k-nearest neighbors algorithm”, El-Cezeri Journal of Science and Engineering, 2018, 5:591-595.
Subaşı M., Yılmaz O., Samet K., Safarian A., Karataş Ç., “Toz Enjeksiyon Kalıplamada 316 L Besleme Stokunun Çekme Yüzdesinin Yapay Sinir Ağları İle Tahmin Edilmesi”, El-Cezeri Journal of Science and Engineering, 2020, 7(3):1063-1073.
Öztemel E., “Yapay sinir ağlari”, PapatyaYayincilik, Istanbul, (2003).
Kukreja H., Bharath N., Siddesh C.S., Kuldeep S., “An introduction to artificial neural network”, Internatioal Journal of Advance Research Innovative Ideas in Education, 2016, 1:27-30.
Savasci D., Ornek A.H., Ervural S., Ceylan M., Konak M., Soylu H., “Classification of unhealthy and healthy neonates in neonatal intensive care units using medical thermography processing and artificial neural network”, In Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis, Academic Press, 1-29, (2019).