Search arXiv⌕ Search

arXiv · 2610.32186

Freeform Programmable Liquid Crystal Elastomers via Monolithic Integration of Co-Optimization and 4D-Printing

Abstract

Liquid crystal elastomers (LCEs) are active materials capable of large, reversible thermally induced actuation, offering significant potential for applications such as artificial muscles and soft robotics. However, their broader deployment is hindered by the lack of a unified design-to-fabrication framework, the strong coupling among geometry, director fields, and manufacturing constraints, and the difficulty of reliably translating complex designs into physically realizable architectures. Here, we present an autonomous and monolithic integration of co-optimization and 4D printing that enables the end-to-end design-to-realization of versatile programmable LCE architectures with superior precision, complexity, and versatility. The proposed framework establishes a unified design strategy that co-optimizes the freeform multilayer geometry and fully continuous layer-specific director distributions, and subsequently translates the optimized fields into fabrication-ready DIW toolpaths through an automated manufacturing-aware pipeline. To bridge computation and realization, we develop an optimization-centered 4D printing approach with automated, fabrication-aware toolpath generation tailored for direct ink writing of LCEs. With the proposed design-to-fabrication paradigm, we experimentally realize architectures that exhibit complex, non-intuitive thermo-mechanical behaviors. Experiments achieve highly challenging prescribed functionalities beyond the reach of intuition-based approaches and show excellent agreement with numerical simulations across all samples. This work establishes a generalizable inverse design and physical realization of freeform programmable LCE systems, enabling function-oriented and optimization-driven intelligent LCE applications for next-generation soft robotic and responsive material engineering.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yibo Wang, Asad Farooq Ahmed, Weichen Li, Weiyun Xu, Xiaojia Shelly Zhang. 2026-10-23. Freeform Programmable Liquid Crystal Elastomers via Monolithic Integration of Co-Optimization and 4D-Printing. https://arxiv.org/abs/2610.32186

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Forecasting Global Equity Volatility: Econometric Anchoring and Cross-Market Attention

International volatility forecasting requires models to combine persistent local dynamics with information observed across different trading calendars. We propose PGA-Trans-HAR, an attention model that learns a bounded correction to a frozen heterogeneous autoregressive (HAR) forecast. A rolling predictive-connectedness graph guides spatial attention, while source masks accommodate market closures on the union calendar. We evaluate daily range-based volatility for eight equity indices from March 2013 to September 2026 at leads of 1, 5, and 22 union-calendar dates. Among seven primary forecasting models, PGA-Trans-HAR achieves the lowest equal-market average MSE at the 1- and 22-date leads and the lowest average QLIKE at all three leads. Its MSE reductions relative to HAR are 1.70% and 1.13% at the two endpoints. At the 1-date lead, the standard deviation of MSE across five fitted seeds is 0.00057, compared with 0.01260 for DCRNN-HAR and 0.03727 for GNN-HAR. The model is retained in 47 of 48 market-horizon MSE cells in the 90% Model Confidence Sets across two evaluation panels. Under historical-tail calibration, joint VaR/ES scores improve on HAR at the 1- and 5-date leads. A common intraday exposure rule yields annual CER gains of 7.89 to 9.28 basis points relative to HAR across the specified cost scenarios. These results support bounded correction of a local econometric forecast as a design for accurate and stable cross-market volatility prediction.

cs.CE↗

Temperature-Dependent Multiphysics Modeling of Additive Friction Stir Deposition Using Multi-Task Coupled Physics-Informed Neural Networks

Additive friction stir deposition (AFSD) involves coupled thermal and material-flow fields governed by frictional heating, plastic deformation, and tool-imposed boundary conditions. High-fidelity finite-volume methods (FVMs) can resolve these fields but are computationally expensive, while constant-property approximations can distort the predicted thermo-mechanical response. This work develops a steady-state multi-task coupled physics-informed neural network (MCoPINN) to predict three-dimensional temperature and material-flow fields while reconstructing temperature-dependent thermal conductivity, density, and specific heat from sparse property measurements. A theoretical analysis decomposes prediction error into property reconstruction and neural field-solver contributions and characterizes the discrepancy associated with constant-property models. A controlled one-dimensional nonlinear-conduction study evaluates property reconstruction under sparse measurements. The framework is then evaluated for AFSD against a temperature-dependent FVM benchmark and experimental thermocouple measurements. MCoPINN reproduces the reference thermal and material-flow fields and reduces average temperature root-mean-square error from 50.13 K for the constant-property CoPINN to 30.28 K across four traverse speeds. MCoPINN requires approximately 8.5 hours of training per condition, compared with 52 hours for the FVM benchmark. These results demonstrate computationally efficient, physics-coupled prediction with temperature-dependent material properties.

cs.CE↗

Marking strategies for adaptive mesh refinement: An efficiency-focused benchmark study for steady solid and fluid mechanics problems

Adaptive mesh refinement (AMR) is indispensable for efficient finite element analyses. However, its performance depends not only on the refinement itself but also on strategy to mark elements for refinement and the way it is tuned. This work compares classical marking methods (maximum, Dörfler bulk-chasing, quantile) with non-classical, statistically based approaches (z-score, Isolation Forest), all driven by the residual-based Kelly error estimator and tested on steady solid and fluid mechanics problems. The study finds quantile and z-score markings to be the most robust, Dörfler effective for large bulk parameters, and maximum marking sensitive to irregular fields. Isolation Forest can rival top classical methods with a high contamination level but may fail under aggressive settings. These results offer practical guidance for selecting marking strategies that balance refinement aggressiveness and computational cost in adaptive FEM workflows.

cs.CE↗