journal article·2023-01

Process design of variable fiber content in layers of needle-punched preforms

Yunchao Qi, Guodong Fang, Zhengong Zhou, Songhe Meng, Jun Liang

Journal of Materials Science· Volume 58· Issue 2
Publisher page

Abstract

The needle-punched composite preforms have great potential to be used in the components with aerospace industry. The variable fiber content in layers of needle-punched preforms can affect the structure and properties of the formed composites, where the fiber content distributions can be designed in the needle-punched direction by the needle punching process and compaction process. A needle-punched fiber analysis method is established to determine the fiber content introduced in the thickness direction during the needle punching process. An artificial neural network (ANN) is constructed between the remaining fiber content in layers and needle punching process parameters. Based on the ANN, the needle punching process is designed in order to satisfy the specified requirements of fiber content distributed in the needle-punched preform. The maximum error between the designed fiber content and the target value is 1.18%. An improved compaction theoretical model is also applied to obtain the variation of fiber content in layers. The fiber content can be accurately obtained by controlling the pressure of needle plate and the pressure can also be set to satisfy the thickness requirements of needle-punched preforms. The compaction theoretical model is further validated by compressive experiment of the needle-punched preforms. The relative error between experimental value and theoretical value is 3.49%. This work can effectively guide the design of variable fiber content in layers of needle-punched preform.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Electronic Packaging and Soldering Technologies · 3D IC and TSV technologies · Mechanical Behavior of Composites

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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