Comparative simulations of stress modulated properties of SiC and Si during the lapping process
Source abstract
Abstract
This study conducted a comparative investigation of the stress-dependent mechanical behavior of SiC and Si to develop efficient, damage-free SiC machining processes through combined experimental and computational approaches. Lapping experiments demonstrate a strong correlation between post-processing residual stress and indentation hardness, with distinct stress and hardness distribution patterns for SiC and Si under varying lapping loads. First-principles calculations reveal that compressive stress enhances plastic deformability by facilitating bond angle variations, while tensile stress promotes brittle fracture through bond length changes. Young's modulus and bulk modulus calculations show that compressive stress significantly affects SiC mechanical properties, whereas both tensile and compressive stresses considerably impact Si performance. Thermodynamic analysis indicates that compressive stress increases chemical reactivity in both materials, while tensile stress reduces it. Molecular dynamics (MD) simulations reveal that tensile stress decreases nanoindentation hardness, increases dislocation density during lapping, and promotes hexagonal diamond phase formation. Conversely, compressive stress increases hardness, reduces dislocation density, and inhibits hexagonal phase transformation. Comparative analysis shows that SiC exhibits significantly higher dislocation density and hexagonal diamond transformation than Si under identical lapping conditions. Furthermore, stress exerts opposite effects on amorphous phase transformation volume during material removal in SiC and Si. These findings provide new insights into the influence of stress on SiC processing.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Surface Polishing Techniques · Advanced machining processes and optimization · Advanced ceramic materials synthesis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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