Speaker
Description
Abstract:
Extending 3D printing shape transformation undergoes the incorporation of smart materials and programmability over external stimulus time response. The time-dependent response is challenged due to the complex interaction and nonlinear behavior of process parameters and design influences of materials. However, modeling the computation and modeling dynamic behavior and structures of 4D printing integrate the machine learning approaches in deformation prediction. The techniques evolve the time-dependent evolution of kinematic structures through finite element analysis (FEA) and thermal stimuli. During the 450 K polylactic acid (PLA) finite element analysis simulation, its surface deformation reaches 5.4E-7 m during 8.35 N/m² stress. In parallel, the temperature distribution shape evolution performance extends to 5.4E-7 m at 200 s, and the Eigen frequency reads 2.7642E-7 + 2.6012E-10i, which pursues 7.87 N/m². Similarly, the self-transforming strain change across the principal axis is predicted using linear regression (R² = 0.92) to compare actual and predicted displacement. Thereby, the proposed method enables accuracy and design optimization during computational time; reduction is efficient and reliable, whereas a 4D printing framework is applicable in adaptive soft robot manufacturing.
Keywords: 4D printing, time-dependent, Machine learning, simulation, thermal stimulus
| Topic Area | Design Optimization, Additive Manufacturing Technologies & Applications |
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