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Multi-Scale Low-Entropy Method for Optimizing the Processing Parameters during Automated Fiber Placement.

Zhenyu Han1, Shouzheng Sun2, Hongya Fu3

  • 1School of Mechatronics Engineering, Harbin Institute of Technology, No.92, Xidazhi Street, Harbin 150001, China. hanzy@hit.edu.cn.

Materials (Basel, Switzerland)
|September 5, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-scale, low-entropy method to optimize automated fiber placement (AFP) processing parameters. The approach enhances material properties and reduces defects by considering energy consumption and micro-scale characteristics.

Keywords:
automated fiber placementcarbon fiber-reinforced compositeslow-entropy methodmulti-scale analysisprocessing optimization

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Area of Science:

  • Materials Science and Engineering
  • Composite Materials Manufacturing
  • Computational Modeling and Simulation

Background:

  • Automated Fiber Placement (AFP) is a complex manufacturing process involving diverse energy forms and multi-scale effects.
  • Optimizing AFP processing parameters is crucial for enhancing the mechanical properties and efficiency of composite structures.
  • Existing methods often fail to comprehensively integrate multi-scale phenomena and energy considerations.

Purpose of the Study:

  • To propose and validate a novel multi-scale, low-entropy method for optimizing AFP processing parameters.
  • To synthetically consider multi-scale effects, energy consumption, energy utilization efficiency, and micro-system mechanical properties.
  • To improve matrix fluidity, interface adsorption, reduce void content, and enhance overall laminate mechanical performance.

Main Methods:

  • Utilized Finite Element Method (FEM) to obtain macro- and meso-scale mechanical properties of carbon fiber/epoxy prepregs.
  • Developed a multi-scale energy transfer model to link macroscopic results to microscopic systems.
  • Employed molecular dynamics (MD) to calculate microscopic characteristics (adsorption energy, diffusion coefficient entropy-enthalpy) under varying parameters.
  • Identified low-entropy regions based on the interrelation of entropy-enthalpy values, microscopic properties, and processing parameters.
  • Conducted nine experimental groups to validate simulation findings.

Main Results:

  • The multi-scale low-entropy optimization method effectively reduces void content in AFP-processed laminates.
  • Significant improvements in microscopic mechanical properties, including enhanced interface adsorbability and matrix fluidity, were observed.
  • Experimental validation confirmed the simulation results, demonstrating the method's efficacy.
  • The optimized parameters lead to lower energy consumption and higher energy quality.

Conclusions:

  • The proposed multi-scale low-entropy method offers a robust framework for optimizing AFP processing parameters.
  • This approach successfully integrates multi-scale effects and energy considerations for superior composite manufacturing outcomes.
  • The method demonstrably improves the quality and mechanical performance of composite laminates.