Investigation on optimization of preparation process parameters of GO-CF/SMP composites prepared by VIHPS

• Published in: Journal of materials science Vol. 57; no. 7; pp. 4541 - 4555
• Main Authors: Ma, Yuqin, Wang, Jie, Ma, Juan, Guo, Haiyin, Li, Fei, Zhao, Yatao, Chen, Yi, Wang, Pei
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2022; Springer; Springer Nature B.V
• Subjects: Bearing capacity; Bend strength; Carbon fiber reinforced plastics; Carbon fiber reinforcement; Characterization and Evaluation of Materials; Chemical Routes to Materials; Chemistry and Materials Science; Classical Mechanics; Composite materials; Correlation analysis; Crystallography and Scattering Methods; Curing; Epoxy resins; Ethanol; Expandable structures; Extrusion; Graphene; Matching; Material properties; Materials Science; Mathematical models; Mechanical properties; Methods; Optimization; Performance prediction; Polymer Sciences; Polymers; Prediction models; Process parameters; Shape memory; Solid Mechanics; Support vector machines
• ISSN: 0022-2461; 1573-4803
• DOI: 10.1007/s10853-022-06932-3

Graphene oxide–carbon fiber hybrid reinforced shape memory polymer (GO-CF/SMP) is a composite with both excellent load-bearing capacity and shape memory properties, which is expected to be well suited in the space-expandable structure of aerospace, electronic communication, and other fields application. The preparation process and parameter control directly affect the macroscopic performance of composites. It is important to elucidate the mapping relationship between process parameters and material properties and obtain optimized parameter matching rules for the preparation of composite. In this article, a vacuum impregnation hot-pressing process system (VIHPS) is innovatively adopted to prepare GO-CF/SMP composite. Orthogonal experiment and support vector regression (SVR) analysis are used to determine the weight of the key process parameters in the preparation process on the material properties and construct the process parameter-material performance prediction model. Finally, the optimized process parameter matching law is obtained. When the curing temperature is 80 °C, the curing time is 150 min, the extrusion force is 0.9 MPa, and the vacuum degree is −0.09 MPa, the composite has the best mechanical properties and comprehensive shape memory performance. Its bending strength and comprehensive shape memory performance can reach 465.02 MPa and 96.76%, respectively. The process parameter-material performance prediction model based on SVR has a good degree of fit. The prediction accuracy of the bending strength performance and the comprehensive shape memory performance can reach 99.55% and 94.77%, respectively. The research in this paper provides new ideas for the preparation of GO-CF/SMP cross-scale materials and optimization of process parameters.