Browsing by Author "Detwiler, Duane"

Now showing 1 - 8 of 8
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    Cluster-Based Optimization of Cellular Materials and Structures for Crashworthiness
    (ASME, 2018-09) Liu, Kai; Detwiler, Duane; Tovar, Andres; Mechanical and Energy Engineering, School of Engineering and Technology
    The objective of this work is to establish a cluster-based optimization method for the optimal design of cellular materials and structures for crashworthiness, which involves the use of nonlinear, dynamic finite element models. The proposed method uses a cluster-based structural optimization approach consisting of four steps: conceptual design generation, clustering, metamodel-based global optimization, and cellular material design. The conceptual design is generated using structural optimization methods. K-means clustering is applied to the conceptual design to reduce the dimensional of the design space as well as define the internal architectures of the multimaterial structure. With reduced dimension space, global optimization aims to improve the crashworthiness of the structure can be performed efficiently. The cellular material design incorporates two homogenization methods, namely, energy-based homogenization for linear and nonlinear elastic material models and mean-field homogenization for (fully) nonlinear material models. The proposed methodology is demonstrated using three designs for crashworthiness that include linear, geometrically nonlinear, and nonlinear models.
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    Design for Crashworthiness of Categorical Multimaterial Structures Using Cluster Analysis and Bayesian Optimization
    (ASME, 2019-12) Liu, Kai; Wu, Tong; Detwiler, Duane; Panchal, Jitesh; Tovar, Andres; Mechanical and Energy Engineering, School of Engineering and Technology
    This work introduces a cluster-based structural optimization (CBSO) method for the design of categorical multimaterial structures subjected to crushing, dynamic loading. The proposed method consists of three steps: conceptual design generation, design clustering, and Bayesian optimization. In the first step, a conceptual design is generated using the hybrid cellular automaton (HCA) algorithm. In the second step, threshold-based cluster analysis yields a lower-dimensional design. Here, a cluster validity index for structural optimization is introduced in order to qualitatively evaluate the clustered design. In the third step, the optimal design is obtained through Bayesian optimization, minimizing a constrained expected improvement function. This function allows to impose soft constraints by properly redefining the expected improvement based on the maximum constraint violation. The Bayesian optimization algorithm implemented in this work has the ability to search over (i) a real design space for sizing optimization, (ii) a categorical design space for material selection, or (iii) a mixed design space for concurrent sizing optimization and material selection. With the proposed method, materials are optimally selected based on multiple attributes and multiple objectives without the need for material ranking. The effectiveness of this approach is demonstrated with the design for crashworthiness of multimaterial plates and thin-walled structures.
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    Design of Progressively Folding Thin-Walled Tubular Components Using Compliant Mechanism Synthesis
    (ScienceDirect, 2015-10) Bandi, Punit; Detwiler, Duane; Schmiedeler, James P.; Tovar, Andres; Department of Mechanical Engineering, School of Engineering
    This work introduces a design method for the progressive collapse of thin-walled tubular components under axial and oblique impacts. The proposed design method follows the principles of topometry optimization for compliant mechanism design in which the output port location and direction determine the folding (collapse) mode. In this work, the output ports are located near the impact end with a direction that is perpendicular to the component's longitudinal axis. The topometry optimization is achieved with the use of hybrid cellular automata for thin-wall structures. The result is a complex enforced buckle zone design that acts as a triggering mechanism to (a) initiate a specific collapse mode from the impact end, (b) stabilize the collapse process, and (c) reduce the peak force. The enforced buckle zone in the end portion of the tube also helps to avoid or delay the onset of global bending during an oblique impact with load angles higher than a critical value, which otherwise adversely affects the structure's capacity for load-carrying and energy absorption. The proposed design method has the potential to dramatically improve thin-walled component crashworthiness.
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    Machine Learning and Metamodel-Based Design Optimization of Nonlinear Multimaterial Structures
    (ASME, 2016-08) Liu, Kai; Detwiler, Duane; Tovar, Andres; Department of Mechanical Engineering, School of Engineering and Technology
    This study presents an efficient multimaterial design optimization algorithm that is suitable for nonlinear structures. The proposed algorithm consists of three steps: conceptual design generation, design characterization by machine learning, and metamodel-based multi-objective optimization. The conceptual design can be generated from extracting finite element analysis information or by using structure optimization. The conceptual design is then characterized by using machine learning techniques to dramatically reduce the dimension of the design space. Finally, metamodels are derived using Efficient Global Optimization (EGO) followed by multi-objective design optimization to find the optimal material distribution. The proposed methodology is demonstrated using examples from multiple physics and compared with traditional multimaterial topology optimization method.
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    Metamodel-Based Global Optimization of Vehicle Structures for Crashworthiness Supported by Clustering Methods
    (Springer, 2018) Liu, Kai; Detwiler, Duane; Tovar, Andres; Mechanical Engineering, School of Engineering and Technology
    This work introduces a metamodel-based global optimization method for crashworthiness with the ability to synthesize continuum structures with an optimal distribution of material phases or gauges. The proposed optimization method makes use of fully nonlinear, dynamic crash simulations and consists of three main elements: (1) the generation of a conceptual design from the structures crash response, (2) the optimal clustering of the conceptual design to define the location of the material phases or gauges, (3) the metamodel-based global optimization, which aims to find the optimal settings for each cluster. The conceptual design can be generated from extracting finite element analysis information or by using structural optimization. The conceptual design is then clustered using clustering analysis to reduce the dimension of the design space. The global optimization problem aims to find the optimal material distribution on the reduced design space using metamodels. The metamodels are built using sampling and cross-validation, and sequentially updated using an expected improvement function until convergence. The proposed methodology is demonstrated using examples from multi-objective crashworthiness design examples.
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    Optimal Design of Cellular Material Systems for Crashworthiness
    (SAE, 2016-04) Liu, Kai; Xu, ZongYing; Detwiler, Duane; Tovar, Andres; Mechanical and Energy Engineering, School of Engineering and Technology
    This work proposes a new method to design crashworthiness structures that made of functionally graded cellular (porous) material. The proposed method consists of three stages: The first stage is to generate a conceptual design using a topology optimization algorithm so that a variable density is distributed within the structure minimizing its compliance. The second stage is to cluster the variable density using a machine-learning algorithm to reduce the dimension of the design space. The third stage is to maximize structural crashworthiness indicators (e.g., internal energy absorption) and minimize mass using a metamodel-based multi-objective genetic algorithm. The final structure is synthesized by optimally selecting cellular material phases from a predefined material library. In this work, the Hashin-Shtrikman bounds are derived for the two-phase cellular material, and the structure performances are compared to the optimized structures derived by our proposed framework. In comparison to traditional structures that made of a single cellular phase, the results demonstrate the improved performance when multiple cellular phases are used.
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    Optimal Design of Nonlinear Multimaterial Structures for Crashworthiness Using Cluster Analysis
    (ASME, 2017-08) Liu, Kai; Detwiler, Duane; Tovar, Andres; Mechanical Engineering, School of Engineering and Technology
    This study presents an efficient multimaterial design optimization algorithm that is suitable for nonlinear structures. The proposed algorithm consists of three steps: conceptual design generation, clustering, and metamodel-based global optimization. The conceptual design is generated using a structural optimization algorithm for linear models or a heuristic design algorithm for nonlinear models. Then, the conceptual design is clustered into a predefined number of clusters (materials) using a machine learning algorithm. Finally, the global optimization problem aims to find the optimal material parameters of the clustered design using metamodels. The metamodels are built using sampling and cross-validation and sequentially updated using an expected improvement function until convergence. The proposed methodology is demonstrated using examples from multiple physics and compared with traditional multimaterial topology optimization (MTOP) method. The proposed approach is applied to a nonlinear, multi-objective design problems for crashworthiness.
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    Thin-Walled Compliant Mechanism Component Design Assisted by Machine Learning and Multiple Surrogates
    (SAE, 2015-04) Liu, Kai; Tovar, Andres; Nutwell, Emily; Detwiler, Duane; Mechanical and Energy Engineering, School of Engineering and Technology
    This work introduces a new design algorithm to optimize progressively folding thin-walled structures and in order to improve automotive crashworthiness. The proposed design algorithm is composed of three stages: conceptual thickness distribution, design parameterization, and multi-objective design optimization. The conceptual thickness distribution stage generates an innovative design using a novel one-iteration compliant mechanism approach that triggers progressive folding even on irregular structures under oblique impact. The design parameterization stage optimally segments the conceptual design into a reduced number of clusters using a machine learning K-means algorithm. Finally, the multi-objective design optimization stage finds non-dominated designs of maximum specific energy absorption and minimum peak crushing force. The proposed optimization problem is addressed by a multi-objective genetic algorithm on sequentially updated surrogate models, which are optimally selected from a set of 24 surrogates. The effectiveness of the design algorithm is demonstrated on an S-rail thin-walled structure. The best compromised Pareto design increases specific energy absorption and decreases peak crushing force in the order of 8% and 12%, respectively.