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Composite Bodies

Composite Bodies

A composite body is a body made up of multiple parts, connected to form a larger, unified object. Each part has its own weight and center of gravity, which must be considered to determine the center of gravity of the composite body. In cases where the density or specific weight is constant, the center of gravity coincides with the centroid.
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Unsymmetric Loading of Thin-Walled Members

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Related Experiment Video

Updated: Jun 13, 2026

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

Lightweight Structural Design of UAM Fuselage Using AI Predictive Modeling and Composite Big Data from Automated

Woo Hyuk Son^1,2, Ji Hoon Kim², Sung-Youl Bae¹

¹Aerospace Defense Research Group, Korea Institute of Ceramic Engineering & Technology, 101 Soho-ro, Jinju 52851, Republic of Korea.

Materials (Basel, Switzerland)

|June 12, 2026

Summary

This summary is machine-generated.

This study introduces an AI-driven method for designing lightweight, structurally sound airframes for urban air mobility (UAM). Composite materials significantly reduce weight by up to 50% while maintaining structural integrity for electric aircraft.

Keywords:

data-driven predictive model design process fiber reinforcement plastics (FRP)structural analysis urban air mobility (UAM)

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Last Updated: Jun 13, 2026

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Area of Science:

Materials Science
Aerospace Engineering
Artificial Intelligence

Background:

Rapid urbanization causes traffic congestion and air pollution, increasing the need for sustainable transport solutions like urban air mobility (UAM).
Lightweight yet structurally robust airframes are crucial for the commercial viability of electric propulsion-based UAM systems.
Current design processes often lack efficiency in optimizing composite material properties for advanced aerospace applications.

Purpose of the Study:

To develop an optimized lightweight airframe design process for UAM.
To integrate automated composite manufacturing with AI-based material property prediction.
To demonstrate the effectiveness of AI in predicting mechanical properties for novel composite materials.

Main Methods:

Finite-element analysis (FEA) was used to determine material properties (elastic modulus, weight reduction) of fiber-reinforced polymers (glass, basalt, carbon).
An automated manufacturing process generated a large dataset of fiber-reinforced plastics.
A deep learning regression model was developed using Altair AI Studio to predict mechanical properties.

Main Results:

FEA on a UAM fuselage model using predicted composite properties showed up to 50% weight reduction compared to aluminum with equivalent or superior stiffness.
Inverse reserve factor (IRF) analysis confirmed structural safety, with all configurations having IRF values below 1.
The AI-driven framework successfully predicted mechanical properties for untested material and process conditions.

Conclusions:

The proposed AI-driven framework offers a scalable, data-driven methodology for lightweight composite airframe design.
This approach is applicable to next-generation UAM and advanced air mobility structures.
The study validates the use of AI in accelerating the development of efficient and safe aerospace designs.