A Phenotype-Constrained Multi-objective Algorithm for Programmable Metamaterial Insole Design

Chenglin Xing; Peng Zeng; Xuezheng Yue

doi:10.62051/ijcsit.v8n5.05

Authors

Chenglin Xing School of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai, China
Peng Zeng School of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, China
Xuezheng Yue School of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai, China

DOI:

https://doi.org/10.62051/ijcsit.v8n5.05

Keywords:

Plantar Pressure, Foot orthosis, Programmable Metamaterial, TPMS, Lattice Structure, Multi-objective optimization, Personalized insole, Computational Design

Abstract

Personalized insoles are commonly designed from foot geometry or local pressure magnitudes, yet these approaches do not directly translate plantar loading phenotypes into region-specific mechanical functions. Programmable metamaterials based on triply periodic minimal surface (TPMS) architectures provide a tunable design space for stiffness, compliance, load capacity, and energy absorption, but a transparent mapping algorithm is needed to connect plantar pressure phenotypes with suitable regional structures. Here we introduce MetaInsole Mapper, a phenotype-constrained multi-objective algorithm for assigning TPMS structures to heel, midfoot, and forefoot regions. The framework encodes three quiet-standing plantar pressure phenotypes as regional demand vectors spanning cushioning, support, stability, and compliance. A TPMS mechanics database containing 24 candidate structures is transformed into normalized mechanical feature vectors, and candidate assignments are optimized using a demand-weighted score with printability and adjacent-region stiffness-transition penalties. MetaInsole Mapper generated phenotype-specific regional designs for heel-load exposure, high-variance postural-control, and balanced-control phenotypes. Compared with random assignment, uniform TPMS assignment, and a pressure-only rule, the proposed method achieved the lowest demand-matching error (0.5605) and the highest functional score (1.2440), while maintaining full regional specificity. Sensitivity analysis showed stable assignments under +/-5% and +/-10% demand perturbations and high overall stability under +/-20% perturbation (0.9333). These findings support MetaInsole Mapper as a reproducible computational bridge between plantar pressure phenotyping and programmable metamaterial insole design. The present study establishes an algorithmic design workflow; future work should validate the assigned structures through fabrication, bench testing, and human wear studies.

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