Prediction of Tensile Strength of Abs Material Manufactured by Fused Deposition Modeling Using Machine Learning

Additive Manufacturing (AM) processes, such as Fused Deposition Modelling (FDM), are increasingly usedto fabricate functional parts using Acrylonitrile Butadiene Styrene (ABS) material in which the tensile strengthof 3D printed materials, such as those fabricated using Acrylonitrile Butadiene Styrene (ABS), is a criticalmechanical property that determines their suitability for various applications. However, predicting the tensilestrength of these parts remains challenging due to the complex interplay of various printing parameters. Thisstudy proposes a machine learning approach to predict the tensile strength of ABS parts based on their definedprinting parameters such as Layer height, Infill density, printing speed, nozzle temperature and Bedtemperature.In order to conduct the experiment, Taguchi's Design of the Experiment is employed to create an L25orthogonal array sample dataset. This dataset encompasses a range of combinations of the printing parameters,allowing for a comprehensive analysis of their effect on Tensile strength. The machine learning algorithms arethen applied to this dataset, and their performance is compared to identify the most accurate model-fit.Using a Universal testing machine the tensile strength value of each specimen is known. From theseexperiment values, A machine learning model was trained and validated, Various machine learningalgorithms, like linear regression, random forest regressor, are employed to model and analyze the complexrelationships between the printing parameters and Tensile strength. The best machine learning model isselected based on the least error.