Evaluating FDM Process Parameter Sensitive Mechanical Performance of Elastomers at Various Strain Rates of Loading

Area of Science:

• Materials Science
• Mechanical Engineering
• Additive Manufacturing

Background:

• Fused Deposition Modelling (FDM) is a widely used additive manufacturing technique.
• Optimizing FDM process parameters is crucial for achieving desired mechanical properties in fabricated parts.
• Thermoplastic Polyurethane (TPU) is a versatile material used in FDM for its unique properties.

Purpose of the Study:

• To investigate the influence of key FDM process parameters on the mechanical performance of TPU.
• To characterize the effects of infill percentage, layer height, and raster orientation on tensile and compressive strength.
• To establish a regression model predicting mechanical strength based on process parameters.

Main Methods:

• Mechanical testing of FDM-printed TPU samples under quasi-static and high strain rates (~2500 s⁻¹).
• Systematic variation of infill percentage, layer height, and raster orientation.
• Statistical analysis to identify significant parameters and interactions.
• Development of an artificial neural network (ANN) model for regression analysis.

Main Results:

• Layer height was the most significant factor for quasi-static tensile strength, with increased height decreasing strength by 36.5%.
• Infill percentage was critical at high strain rates, with 100% infill enhancing compressive strength by 12.4% compared to 80%.
• Significant interactions between input parameters were identified through statistical analysis.

Conclusions:

• Process parameter selection critically influences the mechanical performance of FDM-printed TPU parts.
• Layer height and infill percentage are key determinants of mechanical strength under different loading conditions.
• ANN modeling provides a viable approach for predicting and optimizing FDM part performance.