An official website of the United States government
This work presents a new method for efficiently designing loads and supports simultaneously with material distribution in density-based topology optimization. We use a higher-order or super-Gaussian function to parameterize the shapes, locations, and orientations of mechanical loads and supports. With a distance function as an input, the super-Gaussian function projects smooth geometric shapes which can be used to model various types of boundary conditions using minimal numbers of additional design variables. As examples, we use the proposed formulation to model both concentrated and distributed loads and supports. We also model movable non-design regions of predetermined solid shapes using the same distance functions and design variables as the variable boundary conditions. Computing the design sensitivities using the adjoint sensitivity analysis method, we implement the technique in a 2D topology optimization algorithm with linear elasticity and demonstrate the improvements that the super-Gaussian projection method makes to some common benchmark problems. By allowing the optimizer to move the loads and supports throughout the design domain, the method produces significant enhancements to structures such as compliant mechanisms where the locations of the input load and fixed supports have a large effect on the magnitude of the output displacements.
more »
« less
Compliant Mechanism Synthesis Using Nonlinear Elastic Topology Optimization With Variable Boundary Conditions
ABSTRACT In topology optimization of compliant mechanisms, the specific placement of boundary conditions strongly affects the resulting material distribution and performance of the design. At the same time, the most effective locations of the loads and supports are often difficult to find manually. This substantially limits topology optimization's effectiveness for many mechanism design problems. We remove this limitation by developing a method which automatically determines optimal positioning of a prescribed input displacement and a set of supports simultaneously with an optimal material layout. Using nonlinear elastic physics, we synthesize a variety of compliant mechanisms with large output displacements, snap‐through responses, and prescribed output paths, producing designs with significantly improved performance in every case tested. Compared to optimal designs generated using manually designed boundary conditions used in previous studies, the mechanisms presented in this paper see performance increases ranging from 47% to 380%. The results show that nonlinear mechanism responses may be particularly sensitive to boundary condition locations and that effective placements can be difficult to find without an automated method.
more »
« less
- Award ID(s):
- 2311078
- PAR ID:
- 10568818
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- International Journal for Numerical Methods in Engineering
- Volume:
- 126
- Issue:
- 1
- ISSN:
- 0029-5981
- Subject(s) / Keyword(s):
- bistability large displacements morphing wing design path generation variable loads variable supports
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The level set method has been widely applied in topology optimization of mechanical structures, primarily for linear materials, but its application to nonlinear hyperelastic materials, particularly for compliant mechanisms, remains largely unexplored. This paper addresses this gap by developing a comprehensive level set-based topology optimization framework specifically for designing compliant mechanisms using neo-Hookean hyperelastic materials. A key advantage of hyperelastic materials is their ability to undergo large, reversible deformations, making them well-suited for soft robotics and biomedical applications. However, existing nonlinear topology optimization studies using the level set method mainly focus on stiffness optimization and often rely on linear results as preliminary approximations. Our framework rigorously derives the shape sensitivity analysis using the adjoint method, including crucial higher-order displacement gradient terms often neglected in simplified approaches. By retaining these terms, we achieve more accurate boundary evolution during optimization, leading to improved convergence behavior and more effective structural designs. The proposed approach is first validated with a mean compliance problem as a benchmark, demonstrating its ability to generate optimized structural configurations while addressing the nonlinear behavior of hyperelastic materials. Subsequently, we extend the method to design a displacement inverter compliant mechanism that fully exploits the advantages of hyperelastic materials in achieving controlled large deformations. The resulting designs feature smooth boundaries and clear structural features that effectively leverage the material's nonlinear properties. This work provides a robust foundation for designing advanced compliant mechanisms with large deformation capabilities, extending the reach of topology optimization into new application domains where traditional linear approaches are insufficient. The developed methodology is expected to provide a timely solution to computational design for soft robotics, flexible mechanisms, and other emerging technologies that benefit from hyperelastic material properties.more » « less
-
Abstract When performing time-intensive optimization tasks, such as those in topology or shape optimization, researchers have turned to machine-learned inverse design (ID) methods—i.e., predicting the optimized geometry from input conditions—to replace or warm start traditional optimizers. Such methods are often optimized to reduce the mean squared error (MSE) or binary cross entropy between the output and a training dataset of optimized designs. While convenient, we show that this choice may be myopic. Specifically, we compare two methods of optimizing the hyperparameters of easily reproducible machine learning models including random forest, k-nearest neighbors, and deconvolutional neural network model for predicting the three optimal topology problems. We show that under both direct inverse design and when warm starting further topology optimization, using MSE metrics to tune hyperparameters produces less performance models than directly evaluating the objective function, though both produce designs that are almost one order of magnitude better than using the common uniform initialization. We also illustrate how warm starting impacts both the convergence time, the type of solutions obtained during optimization, and the final designs. Overall, our initial results portend that researchers may need to revisit common choices for evaluating ID methods that subtly tradeoff factors in how an ID method will actually be used. We hope our open-source dataset and evaluation environment will spur additional research in those directions.more » « less
-
Abstract This research presents a novel design of a four-bar mechanism featuring a variable stiffness link (VSL) as the output component, aimed at enabling diverse end-effector trajectories without modifying the link length or moment input. By employing both single-beam and multi-section beam configurations within a large deflection model, the study investigates the effect of varying link stiffness under constant load and geometric conditions on the mechanism’s trajectory outcomes. The proposed design was validated through both numerical modeling and experimental testing of a built prototype. The findings confirm the prototype’s alignment with theoretical predictions, highlighting the VSL’s key role in significantly enhancing the adaptability and application range of four-bar mechanisms. This advancement circumvents the traditional constraints of fixed-trajectory mechanisms, proposing a versatile, efficient, and cost-effective solution for complex motion applications in compliant mechanism design.more » « less
-
Geometry projection-based topology optimization has attracted a great deal of attention because it enables the design of structures consisting of a combination of geometric primitives and simplifies the integration with computer-aided design (CAD) systems. While the approach has undergone substantial development under the assumption of linear theory, it remains to be developed for non-linear hyperelastic problems. In this study, a geometrically non-linear explicit topology optimization approach is proposed in the framework of the geometry projection method. The energy transition strategy is adopted to mitigate excessive distortion in low-stiffness regions that might cause the equilibrium iterations to diverge. A neo-Hookean hyperelastic strain energy potential is used to model the material behavior. Design sensitivities of the functions passed to the gradient-based optimizer are detailed and verified. The proposed method is used to solve benchmark problems for which the output displacement in a compliant mechanism is maximized and the structural compliance is minimized.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/nme.7613
