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Modeling and Optimization for The Tensile Properties of 3D-Printed FRP using Artificial Neural Network and Artificial Bee Colony Algorithm

Modeling and Optimization for The Tensile Properties of 3D-Printed FRP using Artificial Neural Network and Artificial Bee Colony Algorithm
Autor(en): , , ,
Beitrag für IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022, veröffentlicht in , S. 1119-1128
DOI: 10.2749/nanjing.2022.1119
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Fiber-reinforced polymer (FRP) has multiple applications as a primary material or reinforcing material for the structural elements. Controlling the quality of the 3D printed FRP is critical to guar...
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Bibliografische Angaben

Autor(en): (Department of Structural Engineering, Tongji University, Shanghai 200092, China.)
(Department of Structural Engineering, Tongji University, Shanghai 200092, China.)
(School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, China.)
(Department of Bridge Engineering, Tongji University, Shanghai 200092, China.)
Medium: Tagungsbeitrag
Sprache(n): Englisch
Tagung: IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022
Veröffentlicht in:
Seite(n): 1119-1128 Anzahl der Seiten (im PDF): 10
Seite(n): 1119-1128
Anzahl der Seiten (im PDF): 10
DOI: 10.2749/nanjing.2022.1119
Abstrakt:

Fiber-reinforced polymer (FRP) has multiple applications as a primary material or reinforcing material for the structural elements. Controlling the quality of the 3D printed FRP is critical to guarantee a FRP material of high performance. In this research, machine learning (ML) model based on data collected from experimental studies was developed by artificial neural network (ANN) to control the quality of 3D printed FRP. ANN model predicts the ultimate tensile strength (UTS) of the FRP as function of 7 material and printing parameters. The UTS of the FRP was maximized via optimizing the printing and material parameters by using artificial bee colony (ABC) algorithm. ANN and ABC algorithms were coded by MATLAB. The results showed that the developed ANN model can predict with good accuracy the UTS of FRP. Moreover, it was found that the ABC optimization algorithm can design the input parameters such that a FRP with maximum UTS can be obtained.
Stichwörter:
3D-Drucken 3D-Druck
Copyright: © 2022 International Association for Bridge and Structural Engineering (IABSE)
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