Investigation on dynamic strength of 3D‐printed continuous ramie fiber reinforced biocomposites at various strain rates using machine learning methods. Issue 8 (21st June 2022)

• Record Type: Journal Article
• Authors: Cai, Ruijun; Lin, Hao; Cheng, Ping; Zhang, Zejun; Wang, Kui; Peng, Yong; Wu, Yuankai; Ahzi, Said
• Is Part Of: Polymer composites. Volume 43:Issue 8(2022)
• Page Start: 5235
• Page End: 5249
• Publication Date: 2022-06-21
• Subjects: 3D printing -- biocomposites -- continuous ramie fiber -- dynamic mechanical properties -- machine learning; Polymeric composites -- Periodicals; 620.192
• DOI: 10.1002/pc.26816
• Languages: English
• ISSNs: 0272-8397

Abstract

Abstract: 3D‐printed continuous natural fiber reinforced biocomposites have promising prospects due to their environmental friendliness and suitable mechanical properties. Understanding the dynamic mechanical properties of 3D‐printed biocomposites is essential to expand their application. In this study, the continuous ramie fiber reinforced biocomposites (CRFRC) with different layer thicknesses and hatch spacings were fabricated via 3D printing technique with microstructure characterized. In addition, the dynamic strengths of 3D‐printed CRFRC at four strain rates were investigated. The experimental results exhibited that the printing parameters presented nonlinear and interactive influences on the dynamic strength of CRFRC. Given this circumstance, machine learning methods were employed to link the dynamic strength of 3D‐printed CRFRC with different printing parameters. The experimental data were used to train, calibrate, and validate the machine learning models. The trained models were then utilized to predict the dynamic strength of CRFRC printed using different conditions. Behaviors under multiple strain rates were investigated over the whole parameter space. A good agreement was found between experimental results and predictions. Based on the prediction results, the relationships between parameters, microstructural characteristics and dynamic strength of printed CRFRC were quantitatively analyzed. Abstract : Investigation of the dynamic strength of 3D‐printed biocomposites using machine learning.