Prediction of mechanical properties for acrylonitrile-butadiene-styrene parts manufactured by fused deposition modelling using artificial neural network and genetic algorithm

ABSTRACT

This paper aims to model and predict the mechanical properties of acrylonitrile-butadiene-styrene parts manufactured by fused deposition modelling. An L27 Taguchi array was used to associate the peculiar parameters (nozzle diameter, layer height, fill density, printing velocity, raster orientation and infill pattern) to perform experiments. A three-point bending test was executed on all samples in accordance with the ASTM D7264 standard, aimed to obtain Flexural strength and the 0.2% offset yield strength. An artificial neural network multi-parameter regression model was formed and then the model of the input-output relations developed by this network was optimised by genetic algorithm. This modelling and optimisation suggest a direct relation between choosing process parameters correctly and enhancing performance fused deposition modelling. This method provides optimal solutions for getting allotted output values. Infill pattern plays a critical role in providing strength. Infill patterns can be recommended for desired output within constraints of input factors.

KEYWORDS:

• 3D-Printing
• FDM
• flexural strength
• artificial neural network
• genetic algorithm

Disclosure statement

The authors declare that there are no financial interests/personal relationships which may be considered as potential competing interests.