Affiliation:
1. College of Mechanical and Vehicle Engineering Taiyuan University of Technology Taiyuan China
2. Advanced Forming and Intelligent Equipment Research Institute Taiyuan University of Technology Taiyuan China
3. College of Information and Computer Taiyuan University of Technology Taiyuan China
4. National Key Laboratory of Transit Physics Nanjing University of Science and Technology Nanjing China
Abstract
AbstractThe extrusion procedure used to manufacture Short Fiber Reinforced Composites (SFRCs) results in significant anisotropy in the components due to the skin‐core structure. While there are existing accurate stiffness prediction methods available, there is still a need in engineering for an efficient and cost‐effective prediction method. This work proposes an efficient, low‐cost and effective stiffness prediction method for skin‐core structure using the orientation averaging method and series–parallel model. Initially, the sample is analyzed using industrial CT and metallographic microscopy to ascertain the fiber characteristics and skin‐core dimensions. Furthermore, it is presumed that the skin and core are distinct materials and their stiffness is determined using the orientation averaging method. Finally, the stiffness of the sample is determined by employing a series–parallel model for analyzing stress–strain transmission, which involved merging the skin and core components. Experimental and finite element results confirm the method's accuracy, boasting a calculation time of 31.36 s and a maximum stiffness error below 10%. This method is expected to be applied to automotive, construction and other engineering fields to meet the demand for fast, cost‐efficient and effective stiffness prediction of SFRCs.Highlights
A stiffness prediction method is proposed, which is no modeling required.
Combined with Hashin‐Tsai, orientation average method and series–parallel model.
The stiffness of skin‐core structure is predicted by this method within 31.36 s.
This method can be extended to more complex fiber orientation examples.
Funder
National Natural Science Foundation of China