Self-recovery of worn surface of TiNi shape memory alloy
Source abstract
Abstract
Abstract In this study, the thermally induced deformation recovery of TiNi alloy worn surface under dry sliding condition was examined. Surface deformations were simulated under various normal loads and sliding frequencies by a ball-on-disk tribometer system at room temperature. Surface profiles of wear scars were obtained before and after heating in air at 80 °C for 10 min, and the experimental results showed that partial recovery of the worn surface was observed. The partial deformation recovery is relative to recovery of the martensitic transformation-induced slip-dislocations and thermally-induced martensite reorientation variants to austenite. The recovery ratio, which is defined as the deformation recovery in the depth direction, was influenced by normal loads and reciprocating frequencies. As the normal load increased from 2 N to 6 N, the deformation recovery ratio of TiNi alloy decreased from 21.4% to 6.4%. With further increasing to 8 N, the recovery ratio was declined to 4.8%. These observations were explained and discussed with respect to the corresponding wear mechanisms and contact stress distribution during sliding wear tests. For different frequencies, the deformation recovery ratio tended to decrease as the reciprocating frequency increased. In addition, the deformation recovery of worn surface was also simulated by indentation in conjunction with a mechanical polishing process. The results showed that spherical protrusion morphology was observed, and its height (18 μm) was around 50% of the depth of initial indent. It confirmed that the deformation recovery existed under wear conditions, and opened up potential engineering applications of textures.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Shape Memory Alloy Transformations · Metal and Thin Film Mechanics · Titanium Alloys Microstructure and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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