Journal
APPLIED THERMAL ENGINEERING
Volume 212, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.applthermaleng.2022.118533
Keywords
Finite element analysis; Artificial Neural Network; Polymers; Additive manufacturing; 3D printing
Funding
- UTM [Q. J130000.21A2.05E30]
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In this study, a three-dimensional finite element model with an apparent heat capacity model was developed to evaluate the influence of processing parameters on the thermal behavior of polymers. The experimental validation showed a complex correlation between processing parameters and thermal behaviors such as temperature distribution and phase change. Furthermore, an artificial neural network trained using the model results demonstrated the capability to predict thermal and glass transition behavior with subsequent processing parameters.
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.
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