Material extrusion‐based additive manufacturing of sandwiched acrylonitrile butadiene styrene/glass fibers composites: Machine learning approach to model tensile stress

Abstract

Additive manufacturing (AM), also recognized as 3D printing, has gained significant attention in various industries for its potential to revolutionize production processes. One critical aspect of AM is ensuring the quality and performance of printed parts, particularly concerning mechanical properties like tensile stress. In the present work, the effect of process variables on 3D‐printed acrylonitrile butadiene styrene (ABS)/Glass fiber composite materials was explored. The machine learning approach, classification and regression trees (CART) algorithm, was used to predict tensile stress in ABS/Glass fiber composite materials based on predictor variables such as layer thickness, nozzle temperature, bed temperature, and infill density. The objective is to develop an accurate and interpretable model that captures the relationships between these variables and tensile stress. The model is evaluated using performance metrics such as R‐squared, mean absolute deviation (MAD), and root mean squared error (RMSE) on both training and test datasets. From results the highest tensile stress of 39 MPa was achieved at nozzle temperature of 250°C, bed temperature of 80°C and infill density at 60%. The CART model predicts the most influencing parameter as infill density followed by nozzle temperature and bed temperature.Highlights Novel material is manufactured by sandwiching glass fiber in ABS layers. FDM process for novel material is optimized based upon process parameters. SEM analysis reported good interlayer adhesion with few micro‐porosities. Modeling of tensile stress by classification and regression tree algorithm. CART model predicts significant impact of infill density on tensile stress.