Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Mechanical meta-materials are engineered periodic solids exhibiting unique mechanical properties arising from geometry rather than chemical composition. Although extensive computational and experimental studies exist, analytical models capable of predicting their effective mechanical response remain limited. This paper presents a purely theoretical investigation of mechanical meta-materials using Effective Medium Theory (EMT) to establish closed-form expressions and theoretical bounds for stiffness, strength, and damping. A generalized homogenization framework is formulated using energy equivalence principles and mean-field theory. The results yield analytical upper and lower limits analogous to classical Voigt–Reuss and Hashin–Shtrikman bounds but extended to include geometric effects via a topology correction factor. The derived relations show that the mechanical performance of meta-materials can be tuned continuously through geometric parameters such as ligament orientation and cell connectivity. The study provides an analytical foundation for meta-material design and optimization, independent of numerical or experimental validation.
"Analytical Study of Mechanical Meta-materials Using Effective Medium Theory: Derivation of Theoretical Bounds on Stiffness, Strength, and Damping.", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 10, page no.b439-b444, October-2025, Available :http://www.ijrti.org/papers/IJRTI2510152.pdf
Downloads:
00091
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator