Interlocking modular assembly for scalable composite metamaterials with intrinsic self-sensing capability

Integrating load-bearing capacity, energy absorption, and real-time damage monitoring into a single composite system remains difficult. Embedded sensors can compromise mechanical integrity, complicate manufacturing, and limit the scale of high-performance composite structures. Here, we present an interlocking modular composite termed the assemblable modular mechanical metamaterial (AMMM). The design requires no external sensors and can be assembled beyond the build-volume limits of monolithic additive manufacturing. The AMMM was fabricated from graphene-nanoplatelet-reinforced polylactic acid by fused deposition modeling. Identical cells were joined without adhesives to form planar arrays larger than the printer build volume. Under quasi-static in-plane compression, the specimens underwent progressive layer-by-layer plastic collapse. The tested 1 × 1–4 × 4 arrays had plateau stresses of approximately 0.57–1.04 MPa. An energy-balance model based on the plastic-hinge rotation predicted the plateau stresses of the tested configurations with relative errors of approximately 10% or less. Deformation of the interlocking cells caused measurable resistance changes in the graphene-reinforced composite. The resistance response followed the main deformation and damage stages, with gauge factors of 15.6–25.4. Combining modular assembly with intrinsic piezoresistivity provides a route to smart protective composites for aerospace, automotive, and civil engineering applications.

Authors

Institutions

Publication Details

Journal
Engineering Structures
Published
2026-10-15
DOI
https://doi.org/10.1016/j.engstruct.2026.123963
Primary Topic
Cellular and Composite Structures
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Interlocking modular assembly for scalable composite metamaterials with intrinsic self-sensing capability

Xiang Xu, Qiansheng Tang, Zhe Liu, Lianchao Guo et al.
Engineering Structures
Cellular and Composite Structures
article

Interlocking modular assembly for scalable composite metamaterials with intrinsic self-sensing capability

Xiang Xu, Qiansheng Tang, Zhe Liu, Lianchao Guo, Jiale Chen, Yong Zhang, Pengfei Wang, Zhen Li, Guangding Wang, Xin Wang
article en

Abstract

Integrating load-bearing capacity, energy absorption, and real-time damage monitoring into a single composite system remains difficult. Embedded sensors can compromise mechanical integrity, complicate manufacturing, and limit the scale of high-performance composite structures. Here, we present an interlocking modular composite termed the assemblable modular mechanical metamaterial (AMMM). The design requires no external sensors and can be assembled beyond the build-volume limits of monolithic additive manufacturing. The AMMM was fabricated from graphene-nanoplatelet-reinforced polylactic acid by fused deposition modeling. Identical cells were joined without adhesives to form planar arrays larger than the printer build volume. Under quasi-static in-plane compression, the specimens underwent progressive layer-by-layer plastic collapse. The tested 1 × 1–4 × 4 arrays had plateau stresses of approximately 0.57–1.04 MPa. An energy-balance model based on the plastic-hinge rotation predicted the plateau stresses of the tested configurations with relative errors of approximately 10% or less. Deformation of the interlocking cells caused measurable resistance changes in the graphene-reinforced composite. The resistance response followed the main deformation and damage stages, with gauge factors of 15.6–25.4. Combining modular assembly with intrinsic piezoresistivity provides a route to smart protective composites for aerospace, automotive, and civil engineering applications.

Engineering StructuresVol. 370
Huaqiao University (CN), Anhui Agricultural University (CN), University of Shanghai for Science and Technology (CN)
National Natural Science Foundation of China, Anhui Provincial Department of Education
Industry, innovation and infrastructure
Openalex Percentile: Top 23%
Cellular and Composite Structures
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.