Origami Actuator Design and Networking Through Crease Topology Optimization

2015 ◽  
Vol 137 (9) ◽  
Author(s):  
Kazuko Fuchi ◽  
Philip R. Buskohl ◽  
Giorgio Bazzan ◽  
Michael F. Durstock ◽  
Gregory W. Reich ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Design Optimization ◽  
Design Method ◽  
Network Connectivity ◽  
Mechanical Analysis ◽  
Optimization Techniques ◽  
Truss Structures ◽  
Optimization Approach ◽  
Design Strategies ◽  
Multiple Actuators

Origami structures morph between 2D and 3D conformations along predetermined fold lines that efficiently program the form of the structure and show potential for many engineering applications. However, the enormity of the design space and the complex relationship between origami-based geometries and engineering metrics place a severe limitation on design strategies based on intuition. The presented work proposes a systematic design method using topology optimization to distribute foldline properties within a reference crease pattern, adding or removing folds through optimization, for a mechanism design. Optimization techniques and mechanical analysis are co-utilized to identify an action origami building block and determine the optimal network connectivity between multiple actuators. Foldable structures are modeled as pin-joint truss structures with additional constraints on fold, or dihedral, angles. A continuous tuning of foldline stiffness leads to a rigid-to-compliant transformation of the local foldline property, the combination of which results in origami crease design optimization. The performance of a designed origami mechanism is evaluated in 3D by applying prescribed forces and finding displacements at set locations. A constraint on the number of foldlines is used to tune design complexity, highlighting the value-add of an optimization approach. Together, these results underscore that the optimization of function, in addition to shape, is a promising approach to origami design and motivates the further development of function-based origami design tools.

Topology Optimization for Design of Origami-Based Active Mechanisms

2014 ◽  
Author(s):  
Kazuko Fuchi ◽  
Philip R. Buskohl ◽  
James J. Joo ◽  
Gregory W. Reich ◽  
Richard A. Vaia
Keyword(s):  
Topology Optimization ◽  
Mechanism Design ◽  
Design Method ◽  
Truss Structures ◽  
Systematic Design ◽  
Design Strategies ◽  
Foldable Structures ◽  
And Performance ◽  
2D And 3D ◽  
Additional Constraints

Origami structures morph between 2D and 3D conformations along predetermined fold lines that efficiently program the form of the structure and show potential for many engineering applications. However, the enormity of the design space and the complex relationship between origami-based geometries and engineering metrics place a severe limitation on design strategies based on intuition. The presented work proposes a systematic design method using topology optimization to distribute foldline properties within a reference crease pattern, adding or removing folds through optimization, for a mechanism design. Following the work of Schenk and Guest, foldable structures are modeled as pin-joint truss structures with additional constraints on fold, or dihedral, angles. The performance of a designed origami mechanism is evaluated in 3D by applying prescribed forces and finding displacements at set locations. The integration of the concept of origami in mechanism design thus allows for the description of designs in 2D and performance in 3D. Numerical examples indicate that origami mechanisms with desired deformations can be obtained using the proposed method. A constraint on the number of foldlines is used to simplify a design.

Experiment Driven Local Optimization (EDLO) With an Application to Additive Manufacturing

2017 ◽  
Author(s):  
J. M. Hamel
Keyword(s):  
Additive Manufacturing ◽  
Objective Function ◽  
Design Optimization ◽  
Optimization Problems ◽  
Numerical Test ◽  
Local Optimization ◽  
Optimization Techniques ◽  
Decision Makers ◽  
Optimization Approach ◽  
Physical Experiments

The rise of the use of additive manufacturing processes by engineering and research enterprises has greatly increased opportunities for decision makers to quickly evaluate complex geometries and components throughout the design process. This capability makes it possible to explore design trade-off for which computational or analytical models are not readily available, or are impractical to obtain given available time and resources. However, this often means that decision makers are forced to rely primarily on physical experiments for design data, and this greatly limits opportunities for the application of design optimization techniques. To meet this challenge, a new so-called “online” surrogate based optimization approach, called Experiment Driven Local Optimization (EDLO), has been developed. This approach is focused specifically on using approximation techniques and real-time online surrogate training to solve design optimization problems where the system objective function must be evaluated using physical experiments. As a result, this approach is ideally suited to design problems focused on components that are fabricated using additive manufacturing. This approach is capable of mitigating the effects of experimental uncertainty (or noise) in a design objective function, does not require close coordination between the objective function evaluations and the optimizer, and requires as few physical experiments as possible. These capabilities have been demonstrated through the use of several numerical test problems and also through a 3D printed compliant mechanism design problem. The results produced by the EDLO approach are encouraging and show that this new technique has the potential to move decision makers working with physical experiments and additive manufacturing away from engineering intuition and towards the greater potential of design optimization techniques.

scholarly journals Truss geometry and topology optimization with global stability constraints

2020 ◽  
Vol 62 (4) ◽  
pp. 1721-1737
Author(s):  
Alemseged Gebrehiwot Weldeyesus ◽  
Jacek Gondzio ◽  
Linwei He ◽  
Matthew Gilbert ◽  
Paul Shepherd ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Global Stability ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Truss Structures ◽  
Cross Sectional ◽  
And Topology ◽  
Highly Nonlinear ◽  
Primal Dual ◽  
The Cross

Abstract In this paper, we introduce geometry optimization into an existing topology optimization workflow for truss structures with global stability constraints, assuming a linear buckling analysis. The design variables are the cross-sectional areas of the bars and the coordinates of the joints. This makes the optimization problem formulations highly nonlinear and yields nonconvex semidefinite programming problems, for which there are limited available numerical solvers compared with other classes of optimization problems. We present problem instances of truss geometry and topology optimization with global stability constraints solved using a standard primal-dual interior point implementation. During the solution process, both the cross-sectional areas of the bars and the coordinates of the joints are concurrently optimized. Additionally, we apply adaptive optimization techniques to allow the joints to navigate larger move limits and to improve the quality of the optimal designs.

Design optimization of large-scale structures with sequential linear programming

2002 ◽  
Vol 216 (8) ◽  
pp. 799-811 ◽  
Author(s):  
L Lamberti ◽  
C Pappalettere
Keyword(s):  
Linear Programming ◽  
Design Optimization ◽  
Large Scale ◽  
Optimization Technique ◽  
Computational Cost ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Truss Structures ◽  
Large Set ◽  
Sequential Linear Programming

Design optimization of complex structures entails tasks that oppose the usual constraints on time and computational resources. However, using optimization techniques is very useful because it allows engineers to obtain a large set of designs at low computational cost. Among the different optimization methods, sequential linear programming (SLP) is very popular because of its simplicity and because linear solvers (e.g. Simplex) are easily available. In spite of the inherent theoretical simplicity, well-coded SLP algorithms may outperform more sophisticated optimization methods. This paper describes the experience obtained in the design optimization of large-scale truss structures and beams with SLP-based algorithms. Sizing and configuration problems of structures under multiple loading conditions with up to 1000 design variables and 3500 constraints are considered. The relative performance and merits of some SLP-based algorithms are compared and the efficiency of an advanced SLP-based algorithm called ILEAML (improved linearization error amplitude move limits) is tested. ILEAML is also compared to the sequential quadratic programming (SQP) method, which is considered by theoreticians as probably the best theoretically founded optimization technique.

scholarly journals A Sequential Approach for Aerodynamic Shape Optimization with Topology Optimization of Airfoils

2021 ◽  
Vol 26 (2) ◽  
pp. 34
Author(s):  
Isaac Gibert Martínez ◽  
Frederico Afonso ◽  
Simão Rodrigues ◽  
Fernando Lau
Keyword(s):  
Topology Optimization ◽  
Shape Optimization ◽  
Volume Fraction ◽  
Optimization Techniques ◽  
Free Form ◽  
Aerodynamic Shape Optimization ◽  
Sequential Approach ◽  
Airfoil Surface ◽  
Aerodynamic Shape ◽  
Design Variables

The objective of this work is to study the coupling of two efficient optimization techniques, Aerodynamic Shape Optimization (ASO) and Topology Optimization (TO), in 2D airfoils. To achieve such goal two open-source codes, SU2 and Calculix, are employed for ASO and TO, respectively, using the Sequential Least SQuares Programming (SLSQP) and the Bi-directional Evolutionary Structural Optimization (BESO) algorithms; the latter is well-known for allowing the addition of material in the TO which constitutes, as far as our knowledge, a novelty for this kind of application. These codes are linked by means of a script capable of reading the geometry and pressure distribution obtained from the ASO and defining the boundary conditions to be applied in the TO. The Free-Form Deformation technique is chosen for the definition of the design variables to be used in the ASO, while the densities of the inner elements are defined as design variables of the TO. As a test case, a widely used benchmark transonic airfoil, the RAE2822, is chosen here with an internal geometric constraint to simulate the wing-box of a transonic wing. First, the two optimization procedures are tested separately to gain insight and then are run in a sequential way for two test cases with available experimental data: (i) Mach 0.729 at α=2.31°; and (ii) Mach 0.730 at α=2.79°. In the ASO problem, the lift is fixed and the drag is minimized; while in the TO problem, compliance minimization is set as the objective for a prescribed volume fraction. Improvements in both aerodynamic and structural performance are found, as expected: the ASO reduced the total pressure on the airfoil surface in order to minimize drag, which resulted in lower stress values experienced by the structure.

scholarly journals Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm

2021 ◽  
Vol 4 (3) ◽  
pp. 50
Author(s):  
Preeti Warrier ◽  
Pritesh Shah
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Power Converters ◽  
Buck Converter ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Computational Time ◽  
Optimization Approach ◽  
Response Characteristics ◽  
Fractional Order Controller

The control of power converters is difficult due to their non-linear nature and, hence, the quest for smart and efficient controllers is continuous and ongoing. Fractional-order controllers have demonstrated superior performance in power electronic systems in recent years. However, it is a challenge to attain optimal parameters of the fractional-order controller for such types of systems. This article describes the optimal design of a fractional order PID (FOPID) controller for a buck converter using the cohort intelligence (CI) optimization approach. The CI is an artificial intelligence-based socio-inspired meta-heuristic algorithm, which has been inspired by the behavior of a group of candidates called a cohort. The FOPID controller parameters are designed for the minimization of various performance indices, with more emphasis on the integral squared error (ISE) performance index. The FOPID controller shows faster transient and dynamic response characteristics in comparison to the conventional PID controller. Comparison of the proposed method with different optimization techniques like the GA, PSO, ABC, and SA shows good results in lesser computational time. Hence the CI method can be effectively used for the optimal tuning of FOPID controllers, as it gives comparable results to other optimization algorithms at a much faster rate. Such controllers can be optimized for multiple objectives and used in the control of various power converters giving rise to more efficient systems catering to the Industry 4.0 standards.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

scholarly journals A Shape Optimization Method for Part Design Derived from the Buildability Restrictions of the Directed Energy Deposition Additive Manufacturing Process

Designs ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 19
Author(s):  
Andreas K. Lianos ◽  
Harry Bikas ◽  
Panagiotis Stavropoulos
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Energy Deposition ◽  
Design Method ◽  
Current Trend ◽  
Internal Stresses ◽  
Material Distribution ◽  
Load Case ◽  
Design Elements ◽  
Industrial Sectors

The design methodologies and part shape algorithms for additive manufacturing (AM) are rapidly growing fields, proven to be of critical importance for the uptake of additive manufacturing of parts with enhanced performance in all major industrial sectors. The current trend for part design is a computationally driven approach where the parts are algorithmically morphed to meet the functional requirements with optimized performance in terms of material distribution. However, the manufacturability restrictions of AM processes are not considered at the primary design phases but at a later post-morphed stage of the part’s design. This paper proposes an AM design method to ensure: (1) optimized material distribution based on the load case and (2) the part’s manufacturability. The buildability restrictions from the direct energy deposition (DED) AM technology were used as input to the AM shaping algorithm to grant high AM manufacturability. The first step of this work was to define the term of AM manufacturability, its effect on AM production, and to propose a framework to estimate the quantified value of AM manufacturability for the given part design. Moreover, an AM design method is proposed, based on the developed internal stresses of the build volume for the load case. Stress tensors are used for the determination of the build orientation and as input for the part morphing. A top-down mesoscale geometric optimization is used to realize the AM part design. The DED Design for Additive Manufacturing (DfAM) rules are used to delimitate the morphing of the part, representing at the same time the freeform mindset of the AM technology. The morphed shape of the part is optimized in terms of topology and AM manufacturability. The topology optimization and AM manufacturability indicator (TMI) is introduced to screen the percentage of design elements that serve topology optimization and the ones that serve AM manufacturability. In the end, a case study for proof of concept is realized.

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