Studying the possibility of using machine learning algorithms to predict quality and processing parameters for Fused Deposition Modeling (FDM)

Abstract

Fused deposition modeling (FDM) is one of the most popular types of

additive manufacturing, which is

characterized by low costs and fast production, but this type contains many processing parameters (more than 20 parameters), Any change in the values of these parameters has a direct impact on the quality of production and mechanical properties, and previous studies were relied on the experimental method to show the effect of processing parameters on product quality. This article was implemented to show the possibility of using machine learning algorithms in predicting one of the product quality standards. It is the tensile strength, depending on the processing parameters, as well as the prediction of the type of material to be used for manufacturing based on certain values for the rest of the parameters.