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Abstract Smart materials have demonstrated great potential for applications requiring flexibility, shape morphing, and reconfigurability. Among these, magneto-active elastomers (MAEs) are especially promising, achieving relatively large actuation deflections and forces under noncontact magnetic fields with rapid response times. Optimizing the structure and material properties of such advanced materials typically requires a systematic design strategy. Most approaches employ forward design methods, which are time-intensive as they evaluate multiple design variables and the resultant performances to find optimal solutions. In this work, an inverse design strategy is proposed for an MAE unimorph to efficiently identify the feasible design space that meets predefined performance requirements, such as actuated displacement, force thresholds, and actuation energy targets. Using a supervised Bayesian network classifier (BNC) adapted to training data and user-defined performance thresholds, this approach identifies a feasible design space capable of achieving customized performance goals. The BNC model is further enhanced by incorporating the inherent manufacturing variability of additive manufacturing (AM) processes, specifically thickness variation in reactive extrusion additive manufacturing (REAM). The design strategy is demonstrated with an MAE unimorph, using training data from an analytical model based on classic beam theory with free deflection and blocked force as performance metrics. The BNC mapping of the feasible design space demonstrates a resultant lower bending stiffness for displacement targets and a higher bending stiffness for the actuation force threshold. The feasible design space is further reduced to satisfy both performance targets and manufacturing reliability. This approach serves as a powerful tool for identifying high-performing designs that account for manufacturing variability for advanced material designs.
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Design and optimization of magneto-active elastomer (MAE) unimorphs using genetic algorithms
Abstract Magneto-active elastomers (MAEs), a type of smart material, deform in response to external magnetic fields owing to their flexible, untethered, and reconfigurable capabilities. These capabilities are useful for biomedical devices, robotic systems, and actuators for which control over shape morphing, extent of deformation, and force output during actuation are desirable. However, programming the MAE to realize targeted functionalities such as complex deformations (e.g., biomimetic motions), or manipulating force (e.g., grippers) is challenging due to inherent trade-offs between deformation and force generation, where greater flexibility enables larger displacements but typically reduces force, necessitating a formal design and optimization approach. This work introduces a systematic design and optimization approach to program MAEs to achieve complex shape morphing and predictable force-displacement responses. The proposed approach enables optimization for target objectives by integrating previously developed analytical models for predicting the MAE actuation performance into evolutionary algorithms, including genetic algorithms (GAs) and the multi-objective non-dominated sorting (NSGA-II). The optimization output includes the geometry and magnetic properties of the MAE to achieve target deformations or forces as closely as possible when actuated by contactless magnetic fields. Specifically, the approach is used to minimize deviations between the actuated MAE and a predefined target shape and to examine the performance trade-offs between free deflection and blocked force. Two case studies are conducted to match the actuated MAE with predefined target shapes inspired by a gripper and a snake. Both studies demonstrate strong qualitative and quantitative agreements, with shape errors of 3 × 10−5cm and 8.9 × 10−4cm, respectively. The trade-offs between free deflection and blocked force are analyzed and illustrated using a Pareto front plot, which highlights the set of potential solutions. This research offers a promising avenue for revolutionizing traditional static structures by incorporating smart materials capable of altering their shapes with customized and predictable displacement and actuation force profiles.
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- Award ID(s):
- 2401218
- PAR ID:
- 10710628
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Smart Materials and Structures
- Volume:
- 34
- Issue:
- 10
- ISSN:
- 0964-1726
- Page Range / eLocation ID:
- 105026
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Magnetoactive elastomers (MAEs) are capable of large deformation, shape programming, and moderately large actuation forces when driven by an external magnetic field. These capabilities enable applications such as soft grippers, biomedical devices, and actuators. To facilitate complex shape deformation and enhanced range of motion, a unimorph can be designed with varying geometries, behave spatially varying multi-material properties, and be actuated with a non-uniform external magnetic field. To predict actuation performance under these complex conditions, an analytical model of a segmented MAE unimorph is developed based on beam theory with large deformation. The effect of the spatially-varying magnetic field is approximated using a segment-wise effective torque. The model accommodates spatially varying concentrations of magnetic particles and differentiates between the actuation mechanisms of hard and soft magnetic particles by accommodating different assumptions concerning the magnitude and direction of induced magnetization under a magnetic field. To validate the accuracy of the model predictions, four case studies are considered with various magnetic particles and matrix materials. Actuation performance is measured experimentally to validate the model for the case studies. The results show good agreement between experimental measurements and model predictions. A further parametric study is conducted to investigate the effects of the magnetic properties of particles and external magnetic fields on the free deflection. In addition, complex shape programming of the unimorph actuator is demonstrated by locally altering the geometric and material properties.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1088/1361-665X/ae0f67
