Monte Carlo-Based Predictive Modeling of Geometric Defects in Cellular Truss Structures Manufacturing

This study investigates the mechanical consequences of geometric defects in additively man- ufactured metamaterials, with a focus on truss-based architecture. The superior mechanical performance of these architectures is due to their unique geometry and topology. Complemented by structural periodicity, these mechanical metamaterials outperform conventional materials by far, by supplying a high stiffness-to-weight ratio, high heat transfer efficacy, high toughness, and superior energy absorption characteristics. However, manufacturing-induced geometric defects perturb the periodicity of these structures and substantially degrade their mechanical perfor- mance. In this study, we focus on four commonly occurring manufacturing-induced geometric defects: strut waviness, missing or broken strut, wavy struts, and variable cross-section of struts, and examine their effect on the bulk behavior of the structure. To study the impact of defects, we picked ten representative topologies from the Stankovic dataset based on varying mechanical characteristics. We developed a Monte Carlo framework to stochastically distribute defects within these structural units, enabling systematic compari- son between as-designed (defect-free) and as-manufactured (defective) realizations. Mechanical property space for each topology is computed using numerical homogenization and finite ele- ment analysis and is validated experimentally via micro-compression testing. To support scalable studies, an automated data-generation pipeline is introduced that takes in nodal coordinates, connectivity data, and cross-section information to synthesize families of defective geometries, which are evaluated with our FEA pipeline to predict their non-linear response. Complementary specimens based on triply periodic minimal surfaces (TPMS) and stochastic lattices are also fabricated using selective laser sintering to catalogue the geometric defects specific to the process and surface-based lattices and summarize the empirical findings. Overall, the proposed methodology quantifies defect-driven variability in mechanical properties, identifies defect-sensitive motifs, and provides practical tools and datasets for reliability-aware design and process optimization of additively manufactured metamaterials.

Author dc:creator

• Muhammad Areeb (23291614)

• Design for Additive Manufacturing

Statement dc:rights

• In Copyright

DOI dc:identifier

https://doi.org/10.25417/uic.31451359.v1

OAI identifier oai:identifier

oai:figshare.com:article/31451359