Strain Rate-Dependent Compressive Properties of Bulk Cylindrical 3D-Printed Samples from 316L Stainless Steel

Area of Science:

• Materials Science
• Mechanical Engineering
• Additive Manufacturing

Background:

• 3D printing, specifically laser powder bed fusion (LPBF), enables complex geometries but requires understanding material behavior.
• 316L stainless steel is a common material in additive manufacturing, yet its mechanical properties under varying conditions need thorough investigation.
• Strain rate sensitivity is a critical factor influencing the deformation response of metallic materials, especially under dynamic loading.

Purpose of the Study:

• To analyze the strain rate sensitivity of 3D-printed 316L stainless steel under compressive deformation.
• To investigate how printing orientation (0°, 45°, 90°) affects the strain rate sensitivity.
• To characterize the deformation behavior and mechanical properties of bulk samples produced by LPBF.

Main Methods:

• Samples of 316L stainless steel were fabricated using the laser powder bed fusion (LPBF) method with three distinct printing orientations.
• Experimental investigations were conducted using uni-axial quasi-static and dynamic loading conditions.
• Dynamic experiments utilized a split Hopkinson pressure bar (SHPB) apparatus, with deformation captured by high-speed visible-light and thermographic cameras, and analyzed using digital image correlation (DIC).

Main Results:

• A strong strain rate effect was observed on the mechanical response of the 3D-printed 316L stainless steel.
• The strain-rate sensitivity was found to be dependent on the printing orientation of the samples.
• Analysis of true stress-true strain diagrams and thermograms provided quantitative and qualitative insights into deformation processes.

Conclusions:

• The printing orientation significantly influences the strain rate sensitivity of 3D-printed 316L stainless steel.
• LPBF-processed 316L stainless steel exhibits a pronounced strain rate-dependent mechanical behavior.
• Understanding these dependencies is crucial for designing and predicting the performance of additively manufactured components.