Application of artificial neural network optimization for resilient ceramic parts fabricated by direct ink writing. Issue 1 (30th September 2019)

• Record Type: Journal Article
• Authors: Fan, Yunhao; Feng, Bo; Yang, Liangliang; Zhang, Yue
• Other Names: Berto Arnaldo Moreno guestEditor.
• Is Part Of: International journal of applied ceramic technology. Volume 17:Issue 1(2020)
• Page Start: 217
• Page End: 226
• Publication Date: 2019-09-30
• Subjects: additive manufacturing -- artificial neural network -- finite element analysis -- sealing; Ceramics -- Periodicals; Ceramics -- Technological innovations -- Periodicals; 620.1405
• DOI: 10.1111/ijac.13394
• Languages: English
• ISSNs: 1546-542X

Abstract

Abstract: Geometries of ceramic parts for high‐temperature sealing have great influence on their compression‐resilience behaviors. In this work, an accurate and large‐scale artificial neural network (ANN) was established to match the relationship between structural parameters and mechanical properties of ZrO2 parts fabricated by 3D printing. Four geometry parameters of the designed ZrO2 parts were imported as input and apparent Young's modulus and maximum deformation simulated by finite element method (FEM) were imported as output. FEM calculation provided 400 groups of data for the training of ANN, which greatly improved the predicted accuracy of the network. The predicted results show the mechanical performance of the parts with a range of modulus from 9.24 × 10 −3 GPa to 100.35 × 10 −3 GPa and a range of maximum deformation from 2.32% to 5.80% can be forecasted with error less than 8%. Based on the optimized structural parameters, the designed ZrO2 parts were fabricated by Direct Ink Writing (DIW) technique. The experimental compression‐rebound property is comparable to that of ANN prediction. It demonstrates that the combined method of ANN and FEM is a preferable way to optimize the structure and guide the fabrication of complex ceramic parts by 3D printing method. Abstract : Geometries of resilient ceramic parts were firstly designed. Abundant data calculated by FEM successfully trained an accurate ANN which conveyed nonlinear relationships between structure and mechanical properties. Finally, ZrO2 parts as designed were fabricated via direct ink writing method and compressive tests of the parts verified the prediction of ANN.