Welcome to Scholar Publishing Group

International Journal of Art Innovation and Development, 2022, 3(1); doi: 10.38007/IJAID.2022.030103.

Computer-assisted Tang Songcai Crafts Surface Nano Processing Technology


Bin Chen

Corresponding Author:
Bin Chen

Fuzhou University of International Studies and Trade, Fuzhou, Fujian, China


Tang Sancai is the first ceramic variety using cobalt blue color as a decorated system in China's ceramic system. It laid the foundation for this development and prosperity. With the development of social development, the application of computer technology is very wide. Tang Singer's crafts use computer production, can stimulate craftsmen' creative thinking, help design, reduce production costs, improve product quality, and obtain competitive advantages. The GRNN model is used to simulate the Si2N2O-Sialon ceramic superplastic extrusion process, and the strength and effect of extrusion deformation under different temperature and pressure and tension distribution during extrusion process are obtained. Studies have shown that the elongation of Si2N2O-Si3N4 ceramics at 1550 °C can reach 65%, and nano-Si2N2O-Sialon ceramics have better superplasticity.


Computer Assist, Artificial Neural Network, Tang Sangcai, Nano Si3N4

Cite This Paper

Bin Chen. Computer-assisted Tang Songcai Crafts Surface Nano Processing Technology. International Journal of Art Innovation and Development (2022), Vol. 3, Issue 1: 28-44. https://doi.org/10.38007/IJAID.2022.030103.


[1] Anitha R, Raja D. Development of computer‐aided approach for brain tumor detection using random forest classifier. International Journal of Imaging Systems & Technology. (2018) 28(1): 48-53. https://doi.org/10.1002/ima.22255

[2] Acharya V, Kumar P. Identification and red blood cell automated counting from blood smear images using computer-aided system. Medical & Biological Engineering & Computing. (2018) 56(16): 483-489. https://doi.org/10.1007/s11517-017-1708-9

[3] Bukhari N I, Julianto T, Pereira R. Computer-aided prediction of cefotaxime sodium stability in aqueous solution at different pH from sparse data. Latin American Journal of Pharmacy. (2018) 37(3): 571-578.

[4] Daemei A B, Safari H. Factors affecting creativity in the architectural education process based on computer-aided design. Frontiers of Architectural Research. (2018) 7(1): 100-106. https://doi.org/10.1016/j.foar.2017.09.001

[5] Alanis, Alma Y. Electricity Prices Forecasting using Artificial Neural Networks. IEEE Latin America Transactions. (2018) 16(1): 105-111. https://doi.org/10.1109/TLA.2018.8291461

[6] Ravichandran V, Li C, Banagozar A. Artificial neural networks based on memristive devices. Science China Information Sciences. (2018) 61(6): 1-14. https://doi.org/10.1007/s11432-018-9425-1

[7] Dc A, Rm B, Lz A. Characterizing the chemical composition of Tang Sancai wares from five Tang dynasty kiln sites. Ceramics International. (2020) 46(4): 4778-4785. https://doi.org/10.1016/j.ceramint.2019.10.210

[8] Widerski A, Arkadiusz Jówiak, Jachimowski R. Operational quality measures of vehicles applied for the transport services evaluation using artificial neural networks. Eksploatacja i Niezawodnosc - Maintenance and Reliability. (2018) 20(2): 292-299. https://doi.org/10.17531/ein.2018.2.16

[9] Zhang L, Zhou J, Yang J. Study of the Synergistic Effect of Hydrogen Bonding and Nano-Si3N4 in XNBR Matrix. Journal of Inorganic and Organometallic Polymers and Materials. (2021) 31(7): 2859-2867. https://doi.org/10.1007/s10904-021-01972-9

[10] D P S J K P, Ruby J. Computer Aided Therapeutic of Alzheimer's Disease Eulogizing Pattern Classification and Deep Learning Protruded on Tree-based Learning Method. Advances in Intelligent Systems and Computing. (2018) 564(1): 103-113. https://doi.org/10.1007/978-981-10-6875-1_11

[11] Suto J, Oniga S. Efficiency investigation of artificial neural networks in human activity recognition. Journal of Ambient Intelligence & Humanized Computing. (2018) 9(4): 1-12. https://doi.org/10.1007/s12652-017-0513-5

[12] Alomari M H, Adeeb J, Younis O. Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural Networks. International Journal of Electrical and Computer Engineering. (2018) 8(1): 497-504. https://doi.org/10.11591/ijece.v8i1.pp497-504

[13] Mundada V, Suresh K. Optimization of Milling Operations Using Artificial Neural Networks (ANN) and Simulated Annealing Algorithm (SAA). Materials today: proceedings. (2018) 5(2): 4971-4985. https://doi.org/10.1016/j.matpr.2017.12.075

[14] Czischek S, Gaerttner M, Gasenzer T. Quenches near Ising quantum criticality as a challenge for artificial neural networks. Physical review. B, Condensed Matter and Materals Physics. (2018) 98(2): 024311.1-024311.10. https://doi.org/10.1103/PhysRevB.98.024311

[15] Rahmanadi L, Sulistiyono H. Hybrid technique between design of experiments and artificial neural networks for rainfall-runoff model calibration method. International Journal of Civil Engineering and Technology. (2018) 9(1): 11-21.

[16] Goay C H, Goh P, Ahmad N S. Eye-height/width prediction using artificial neural networks from S-Parameters with vector fitting. Journal of Engineering Science and Technology. (2018) 13(3): 625-639.

[17] Saha G, Chakraborty K, Das P. Voltage Stability Prediction on Power Networks using Artificial Neural Networks. Indonesian Journal of Electrical Engineering and Computer Science. (2018) 10(1): 1-9. https://doi.org/10.11591/ijeecs.v10.i1.pp1-9

[18] Miona, V, Andrejevi. Implementation of Recurrent Artificial Neural Networks for Nonlinear Dynamic Modeling in Biomedical Applications. The International Journal of Artificial Organs. (2018) 36(11): 833-842. https://doi.org/10.5301/ijao.5000255

[19] Charfi S, Ansari M E. Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images. Multimedia Tools and Applications. (2018) 77(3): 4047-4064. https://doi.org/10.1007/s11042-017-4555-7

[20] Qin Y, Lu W, Qi Q. Towards an ontology-supported case-based reasoning approach for computer-aided tolerance specification. Knowledge-Based Systems. (2018) 141(2)129-147. https://doi.org/10.1016/j.knosys.2017.11.013

[21] Nguyen P T, Huynh V, Vo K D. An Optimal Deep Learning based Computer-aided Diagnosis System for Diabetic Retinopathy. Computers, Materials and Continua. (2021) 66(3): 2815-2830. https://doi.org/10.32604/cmc.2021.012315

[22] Aldegheishem A. Computer-Aided-Design and Manufacturing of Full Mouth Restoration of a Male Patient with Gastroesophageal Reflux Disease: A Case Report. Bioscience Biotechnology Research Communications. (2021) 14(1): 100-104. https://doi.org/10.21786/bbrc/14.1/13

[23] Saeed F, Rashid A, Saleem W. Implications of Computer-Aided Learning in Elt for Second Language Learners and Teachers During Covid-19. Humanities & Social Sciences Reviews. (2021) 9(3): 1528-1541. https://doi.org/10.18510/hssr.2021.93154

[24] Ma K N, Chen H, Ye H Q. Advances in computer aided design and computer aided manufacturing of removable partial denture. Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology. (2021) 56(5): 485-490.

[25] Li H, Zhang H, Zhao Y. Design of Computer-aided Teaching Network Management System for College Physical Education. Computer-Aided Design and Applications. (2021) 18(S4): 152-162. https://doi.org/10.14733/cadaps.2021.S4.152-162

[26] Wojnarowska W, Kwolek M, Miechowicz S. Selection of a workpiece clamping system for computer-aided subtractive manufacturing of geometrically complex medical models. Open Engineering. (2021) 11(1): 239-248. https://doi.org/10.1515/eng-2021-0026

[27] Wu H. Multimedia Interaction-Based Computer-Aided Translation Technology in Applied English Teaching. Mobile Information Systems. (2021) 2021(5): 1-10. https://doi.org/10.1155/2021/5578476

[28] Dc A, Rm B, Jc B. Circulation of Tang Sancai wares and lead materials in the two capital cities of the Tang empire. Ceramics International. (2021) 47(7): 10147-10152. https://doi.org/10.1016/j.ceramint.2020.12.163