Surface integrity and high-cycle fatigue life of direct laser metal deposited AISI 431 alloys modified by plasticity ball burnishing
Source abstract
Abstract
• Plasticity ball burnishing (BB) was applied to AISI 431 alloys manufactured by LMD process. • LMDed surface roughness and hardness enhanced by BB treatment. • BB enabled increased gain refinement and dislocation density. • High cycle fatigue resistance of LMDed specimen improved by BB treatment. • Fatigue cracking initiated from deeper in subsurface of BB treated specimen. Laser metal deposition (LMD) as an additive manufacturing (AM) is widely used to repair and extend wear and fatigue life of the critical components. This paper has investigated the application of ball burnishing (BB) to improve surface integrity and high-cycle fatigue resistance of LMDed AISI 431 alloys. Results showed that the BB treated samples exhibited significant surface finish improvement by lowering roughness by 91 %. Microhardness increased from 490 to 530 HV 0.1 , an increase by 10 % with a modified depth of 400 µm from the top surface. XRD results showed a peak shift and increase in FWHM by up to 17 %. This had been corroborated by EBSD exhibiting a 20 % increase in dislocation density and 24 % increase in localised misorientation within microstructure. As a result, the overall high cycle fatigue strength of the burnished sample increased by 50 %, and the cracks initiated from sub-surface level defects at a depth of 350 μm below the top surface, delaying the crack propagation and fracture failure. The findings clearly highlight that the burnishing treatment can be a plausible approach in improving the dynamic fatigue resistance and the overall service life of LMDed AISI 431 steel alloys components in engineering applications.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Surface Treatment and Residual Stress · High Entropy Alloys Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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