Brittle or Ductile? Effects of Print Orientation and Raster Angle on Polylactic Acid (PLA) Fused Filament Fabrication (FFF) Tensile Samples

The automotive industry leverages Fused Filament Fabrication (FFF) -based Additive Manufacturing (AM) to reduce lead time and costs for prototypes, rapid tooling, and low-volume customized designs. This paper examines the impact of print orientation and raster angle on the tensile properties of Polylactic Acid (PLA), selected for its ease of use and accessibility. Dog bone samples were designed to the ASTM D638 tensile testing standard and printed solid with a 0.2 mm layer height, two outer walls, and varying raster-fill angles, with layers alternating by 90°. Testing was conducted on the MTS Criterion Model 43, 50 kN system. Varying print orientation along the X and Y axes (double angle builds) produced a Young's modulus (YM) range of 0.7519, reflecting a 34.42% increase between the witnessed minimum and maximum values. These builds exhibited more brittle behavior than most single angle builds, except for X10 Y10 Z0 at a 45° raster (the lowest recorded YM) and X0 Y15 Z0 at a 30° raster (the highest recorded YM). The same build orientation (X0 Y15 Z0) with varying raster angles between 0°-90° resulted in a yield stress range between 2.53 and 3.31 kN/mm2. These findings show that strategic build parameter configuration enables PLA to exhibit both flexible and rigid characteristics. Therefore, when creating AM solutions, Design for Additive Manufacturing (DfAM) and material selection are important, but slicing parameters also play a crucial role in determining a part's final mechanical properties. These findings lay the groundwork for applying this experiment methodology to other thermoplastics commonly used in the automotive industry, such as ABS and Nylons. This research aims to provide detailed experimental data, highlighting extreme values and uncertainties in the tensile strength of FFF samples. Understanding how build orientation influences part strength can help engineers optimize performance and predict failure modes.