Effect Investigation of Process Parameters on 3D Printed Composites Tensile Performance Boosted by Attention...

Area of Science:

• Additive Manufacturing
• Materials Science
• Artificial Intelligence

Background:

• Fused Deposition Modeling (FDM) is a key additive manufacturing method for polymers and composites.
• Predicting mechanical properties of FDM parts is challenging due to multiple influencing printing parameters.
• Carbon fiber reinforced polylactic acid (PLA-CF) composites are increasingly used in FDM applications.

Purpose of the Study:

• To develop an advanced deep learning model for predicting the tensile performance of FDM-printed PLA-CF composites.
• To investigate the impact of key printing parameters on the mechanical properties of these composites.
• To establish a reliable framework for optimizing FDM process parameters for enhanced material performance.

Main Methods:

• An attention-enhanced multi-modal convolutional neural network (ATT-MM-CNN) was designed and implemented.
• A multi-modal dataset was created using 256 combinations of four printing parameters (layer thickness, nozzle temperature, material flow rate, printing speed) and tensile test data.
• The model integrated multi-modal features and an attention mechanism to capture complex parameter-performance relationships.

Main Results:

• The ATT-MM-CNN achieved high performance across all evaluation metrics (accuracy, precision, recall, F1-score), exceeding 0.95.
• Prediction accuracy was significantly improved by at least 17.3% compared to baseline models.
• The model effectively learned the nonlinear relationships between printing parameters and tensile properties.

Conclusions:

• The ATT-MM-CNN offers an effective and reliable approach for predicting the tensile performance of FDM-printed composite materials.
• The developed framework facilitates process-parameter optimization for FDM-based additive manufacturing.
• This study highlights the potential of AI in enhancing the predictability and control of composite material properties in additive manufacturing.