Tailoring interfacial properties of 3D-printed continuous natural fiber reinforced polypropylene composites through parameter optimization using machine learning methods. (August 2022)

• Record Type: Journal Article
• Authors: Cai, Ruijun; Wen, Wei; Wang, Kui; Peng, Yong; Ahzi, Said; Chinesta, Francisco
• Is Part Of: Materials today communications. Volume 32(2022)
• Publication Date: 2022-08
• Subjects: Additive manufacturing -- Interfacial properties -- Continuous natural fiber -- Machine learning -- Polypropylene-based composites; Materials science -- Periodicals; 620.11
• DOI: 10.1016/j.mtcomm.2022.103985
• Languages: English
• ISSNs: 2352-4928

Abstract

Abstract: 3D-printed continuous ramie fiber reinforced polypropylene composites (CRFRPP) are expected to ensure good mechanical properties while meeting the requirements of environmental friendliness and sustainability. To promote the wide industrial application of CRFRPP, this work investigated the effects of printing parameters (extrusion flow rate, printing temperature, layer thickness and printing speed) on the interfacial properties of CRFRPP. The interlayer and intralayer interfacial properties of CRFRPP with different printing parameters were studied using the design of experiment approach. Machine learning methods and response surface methodology prediction were also carried out based on the experimental results to bridge the printing parameters and interfacial properties. According to the prediction results, the printing parameters were optimized to improve the production efficiency while ensuring the desired interfacial performance. At last, the bending tests were conducted to investigate how the difference in interfacial properties can be translated to the mechanical performance. The results found that printed specimens with weak interfacial strength suffered interlaminar delamination failure when subjected to bending loads, greatly weakening the mechanical properties of the composites. Graphical Abstract: ga1 Highlights: ● Continuous natural fiber reinforced composites were prepared via 3D printing. ● Interlayer and intralayer properties of 3D-printed composites were investigated. ● Machine learning methods were used to bridge processing parameters and properties. ● Parameters optimization helped to balance production efficiency and properties.