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Yield Criteria for Ductile Materials under Plane Stress01:25

Yield Criteria for Ductile Materials under Plane Stress

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In designing structural elements and machine parts using ductile materials, it is crucial to ensure that these components withstand applied stresses without yielding. Yielding is initially determined through a tensile test, which evaluates the material's response to uniaxial stress. However, tensile stress is insufficient when components face biaxial or plane stress conditions This condition requires advanced criteria to predict failure.
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Fatigue

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Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
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Design Consideration

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Designing a structure involves a series of considerations, primarily the material's ultimate strength, calculated through tests that measure changes under increased force until the material reaches its breaking point or limit. The ultimate load, where the material breaks, is divided by its original cross-sectional area, resulting in the ultimate normal stress or strength. The ultimate shearing stress is another significant factor taken into account.
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Thin-Walled Hollow Shafts01:15

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In analyzing a thin-walled hollow shaft subjected to torsional loading, a segment with width dx is isolated for examination. Despite its equilibrium state, this segment faces torsional shearing forces at its ends. These forces are quantitatively described by the product of the longitudinal shearing stress on the segment's minor surface and the area of this surface, leading to the concept of shear flow. This shear flow is consistent throughout the structure, indicating a uniform distribution of...
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Unsymmetric Loading of Thin-Walled Members: Problem Solving

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The shear center of a channel section with uniform thickness, height, and width, is determined by computing the shear force in the member and calculating the moments of inertia of the sections.
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Crashworthiness Prediction of Perforated Foam-Filled CFRP Rectangular Tubes Crash Box Using Machine Learning.

Harri Junaedi1, Khaled Akkad2, Tabrej Khan1

  • 1Department of Engineering Management, College of Engineering, Prince Sultan University, Riyadh 12435, Saudi Arabia.

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Summary
This summary is machine-generated.

Polyurethane foam (PUF)-filled carbon fiber-reinforced polymer (CFRP) tubes significantly improve crashworthiness, nearly tripling energy absorption. Machine learning models, particularly decision tree regressors, accurately predict performance, optimizing CFRP crash box design.

Keywords:
crushing testenergy absorptionhybrid structuresregression algorithmthin-walled structure

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Area of Science:

  • Materials Science and Engineering
  • Mechanical Engineering
  • Computational Mechanics

Background:

  • Carbon fiber-reinforced polymer (CFRP) tubes offer high specific strength and energy absorption, making them suitable for automotive crash boxes.
  • Optimizing crashworthiness requires understanding the impact of design parameters like perforations and internal filling.
  • Traditional experimental testing for crashworthiness is time-consuming and costly.

Purpose of the Study:

  • To investigate the axial crashworthiness of rectangular CFRP tubes with varying hole configurations and polyurethane foam (PUF) filling.
  • To evaluate the influence of hole diameter, number, and placement, as well as PUF filling, on crash performance.
  • To assess the feasibility of using machine learning (ML) for predicting CFRP crash box performance to reduce experimental efforts.

Main Methods:

  • Quasi-static axial compression tests were performed on designed CFRP tubes.
  • Crashworthiness indicators, including initial peak force (P_ip), mean crushing force (P_m), and energy absorption (EA), were recorded.
  • Multiple ML algorithms (DTR, LR, RR, LAR, ENs, MLP) were employed to predict crashworthiness indicators using experimental data.

Main Results:

  • PUF-filled tubes exhibited significantly enhanced crashworthiness, with P_m and EA increasing nearly threefold compared to unfilled tubes.
  • In unfilled tubes, holes had variable effects based on diameter and placement; in PUF-filled tubes, holes reduced performance.
  • The Decision Tree Regressor (DTR) model demonstrated the highest prediction accuracy, with RMSE of 1251 and MAPE of 11.37%.

Conclusions:

  • Polyurethane foam filling is crucial for enhancing the crashworthiness of CFRP tubes.
  • Perforation design significantly impacts the crash performance of both filled and unfilled CFRP tubes.
  • Machine learning models, particularly DTR, offer a viable and efficient approach for optimizing CFRP crash box designs.