Effect of Layup and Specimen Size on Spring‐Back and Strength in Al/ CF / PEEK Hybrid V‐Parts
Source abstract
Abstract
ABSTRACT Spring‐back is a significant forming defect in high‐performance Al/CF/PEEK hybrid structures (made of aluminum alloy and CF/PEEK). The strength enhancement of hybrid components is also urgently needed. This study establishes a thermo‐mechanical coupling model and finite element model for Al/CF/PEEK hybrid V‐shaped components, taking into account the crystallization shrinkage deformation of CF/PEEK as well as the anisotropy of its thermal expansion coefficient and thermal conductivity during the forming–cooling–crystallization–spring‐back process. The effects of various layup structures (multi‐layer CF/PEEK plies and multi‐layer aluminum alloy plies) and specimen length on the strength, stress–strain distribution during forming, and spring‐back of hybrid V‐shaped components were studied. Model validation was achieved through profile/temperature/spring‐back/relative crystallinity comparisons between simulations and experiments. The study reveals that shorter specimen dimensions prevent the formation of end bending zone D on the component, effectively reducing both stress and shear strain levels, thereby facilitating easier forming processes. The spring‐back increases with the number of Al sheets, decreases with the number of CF/PEEK prepreg layers, and reduces with decreasing specimen size. The bending strength increases with the number of both Al sheets and prepreg layers. While multiple Al sheets are more effective in enhancing the bending strength of hybrid V‐shaped components, they adversely affect spring‐back reduction. Increasing CF/PEEK layers can significantly improve the interlaminar strength of the V‐parts.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Mechanical Behavior of Composites · Structural Behavior of Reinforced Concrete · Metal Forming Simulation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Big Data and Intelligent Engineering Guizhou University of Commerce Guiyang Guizhou China
College of Mechanical and Electrical Engineering Central South University Changsha Hunan China
Light Alloy Research Institute Central South University Changsha Hunan China
Funding
National Natural Science Foundation of China
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