Abstract:

This study systematically investigated the influence of ten fused deposition modeling process parameters on the tensile properties of ABS parts using a Latin hypercube experimental design. The examined parameters include layer height， wall thickness， top/bottom thickness， top/bottom line direction， infill density， infill line direction， printing speed， extrusion temperature， bed temperature， and workspace temperature. Three machine learning approaches， artificial neural network （ANN）， random forest （RF）， and gradient boosting （GB）， were compared for their predictive accuracy of tensile strength and elongation at break. Results demonstrated that GB outperformed other methods， achieving superior correlation coefficients （R=0.975 9~0.981 2） and the lowest mean square errors （MSE=1~10）. RF showed intermediate performance （R=0.913 6~0.924 0， MSE=3~20）， while ANN yielded the lowest accuracy （R=0.883 5~0.892 4， MSE=5~24）. Feature importance analysis revealed infill density as the most influential parameter， contributing approximately 80 % to GB predictions compared to 40 % in RF. Other significant factors included wall thickness， infill line direction， and layer height. These findings provide valuable insights for optimizing FDM process parameters and selecting appropriate machine learning techniques for mechanical property prediction.

Key words: fused deposition modeling, artificial neural network, random forest, gradient boosting, tensile performance