Applied thermal engineering2022，Vol.21217.DOI:10.1016/j.applthermaleng.2022.118533

Thermal response analysis and parameter prediction of additively manufactured polymers

Moslemi, Navid Abdi, Behzad Gohari, Soheil Sudin, Izman Atashpaz-Gargari, E. Redzuan, Norizah Ayob, Amran Burvill, Colin Su, Meini Arya, Farid
Applied thermal engineering2022，Vol.21217.DOI:10.1016/j.applthermaleng.2022.118533
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  • NSTL
  • Elsevier

Thermal response analysis and parameter prediction of additively manufactured polymers

Moslemi, Navid 1Abdi, Behzad 1Gohari, Soheil 2Sudin, Izman 1Atashpaz-Gargari, E. 3Redzuan, Norizah 1Ayob, Amran 1Burvill, Colin 2Su, Meini 1Arya, Farid4
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作者信息

  • 1. Univ Manchester
  • 2. Univ Melbourne
  • 3. National Univ
  • 4. Buckinghamshire New Univ
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Abstract

Fused Deposition Modelling (FDM), is an additive manufacturing technology where polymers are extruded using appropriate processing parameters to achieve suitable bonding while ensuring that overheating does not occur. Among processing parameters, polymer inlet temperature, nozzle size, extrusion speed, and air cooling speed are significantly effect on the extrusion process at the distance between the build plate and the nozzle tip (standoff region). This study aims to evaluate the influences of the processing parameters on the thermal behavior and phase change zone of Polyamide 12 (PA12) and Acrylonitrile Butadiene Styrene (ABS) polymers at standoff region. A nonlinear three-dimensional (3D) finite element (FE) model was developed by implementing an apparent heat capacity model using the Heat Transfer Module in COMSOL (R) Multiphysics software. FE results in the standoff region were validated by experimental tests, concerning various nozzle sizes and extrusion speed. The validated numerical results demonstrated that there is a complex correlation between processing parameters and thermal behaviors such as phase change and temperature distribution in the standoff region. The FE results were then employed in training an artificial neural network (ANN). A well-established compromise between the trained ANN and the FE results demonstrates that the trained ANN can be employed in the prediction of further thermal and glass transition behavior using subsequent processing parameters.

Key words

Finite element analysis/Artificial Neural Network/Polymers/Additive manufacturing/3D printing/HEAT-TRANSFER/FILAMENT TEMPERATURE/LIQUEFIER DYNAMICS/OPTIMIZATION/EXTRUSION

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出版年

2022
Applied thermal engineering

Applied thermal engineering

EISCI
ISSN：1359-4311
被引量6
参考文献量81
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