Improved prediction of mechanical properties using patterned peening with direct energy measurement in 304 stainless steel
Source abstract
Abstract
Surface hardening has been applied to improve mechanical characteristics in metals for decades. Most peening processes rely on many randomized and overlapping impacts to achieve surface saturation or a prescribed Almen intensity, but more recent reports have highlighted the potential to achieve a favorable strength and ductility balance with limited surface coverage. We investigate the contribution of position- and energy-controlled surface mechanical attrition treatment (PECSMAT) to the mechanical performance of 304 stainless steel with variables of cross-sectional thickness and cumulative impact energy. PECSMAT allows each impact’s energy and location to be prescribed, thereby enabling precise surface deformation and a well-defined relationship between process and properties. This work examines these relationships in samples with thicknesses of 0.6, 1.2, and 2.3 mm with varying impact spacing and energy. The impact characteristics and tensile properties of these samples indicate clear and consistent trends in strength and ductility. Under the tested conditions, impact energy intensity (mJ/mm 3 ) was found to be a better predictor of these properties than thickness-normalized surface coverage (%/mm), but the accuracy of either should be confirmed for each material type, cross-sectional thickness, total energy input, etc. In general, strength is proportional in samples processed with similar impact energy intensity despite differences in thickness and the way that energy is applied, which is not captured in surface coverage alone. This highlights the importance of process control and verification to tune the properties of surface-hardened components as well as the unique utility of PECSMAT to achieve both.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Surface Treatment and Residual Stress · Erosion and Abrasive Machining · Metal and Thin Film Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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