This paper describes the secondary research on feature extraction and selection for decoding the brain electroencephalograph (EEG) signals in designing a prosthetic arm, a Brain Computer Interface (BCI) system. It considers EEG pattern recognition using Principal Component Analysis (PCA) for Feature Extraction. The data used for this research is the EEG signal that is recorded during the imagination of vowels /a/, /e/, /i/, /o/, /u/ by 20 subjects. Since brain signals are very noisy in nature, a robust PCA is also used to extract the best solution to find principal patterns of the data. The final goal of our research is to train the system based on the information in the sample EEG data and make it ready to classify the pattern correctly.

As the use of short dental implants becomes increasingly popular, the effects of the crown-to-implant (C/I) ratio on stress and strain distributions remain controversial. Previous studies in literature disagree on results of interest and level of necessary technical detail. Purpose. The present study sought to evaluate the strain distribution and assess its functional implications in a single implant-supported crown with various C/I ratios placed in the maxillary molar region. Materials and Methods. A high-fidelity, nonlinear finite-element model was developed to simulate multiple clinical scenarios by laterally loading a set of single implants with various implant lengths and crown heights. Strain distribution and maximum equivalent strain were analyzed to evaluate the effects and significance of the crown height, implant length and C/I ratio. The consistency of predicted functional responses to resulting strain at the implant interface were analyzed by interface surface area. Results. Results were evaluated according to the mechanostat hypothesis to predict functional response to strain. Overloading and effects of strain concentrations were more prevalent with increasing C/I ratios. Overloading was predicted for all configurations to varying degrees, and increased with decreasing implant lengths. Fracture in trabecular bone was predicted for at least one C/I ratio and all implant lengths of 10 mm or less. Conclusions. Higher C/I ratios and lower implant lengths increase the biomechanical risks of overloading and fracture. Increasing C/I ratios augment the functional effects of other implant design factors, particularly implant interface features. Greater C/I ratios may be achieved with implant designs that induce less significant strain concentrations.

The main goal of this paper is to achieve asymptotically stable walking for a bipedal robot over an Aperiodic Gait pattern. The five-link planar bipedal robot has one degree of under-actuation, four actuators and point feet. Trajectory tracking and multi-orbital stability were performed using Generalized Proportional Integral (GPI) controller. The stability of the multi-periodical walking was tested through the computation of Poincaré return maps. This analysis was performed by numerical simulation with a gait pattern reconfiguration. The results of the simulation show that the GPI control achieved a performance robust enough to overcome the gait pattern reconfiguration without changes in the control law with an event-based action. The robot analyzed corresponds to a prototype under development at the Control Laboratory of the Universidad Nacional de Colombia, Bogotá.

An Active Disturbance Rejection Control based on Generalized Proportional Integral observer (ADRC with GPI observer) was developed to control the gait of a bipedal robot with five degrees of freedom. The bipedal robot used is a passive point feet which produces an underactuated dynamic walking. A virtual holonomic constraint is imposed to generate online smooth trajectories which were used as references of the control system. The proposed control strategy is tested through numerical simulation on a task of forward walking with the robot exposed to external disturbances. The performance of ADRC with GPI observer strategy is compared with a feedback linearization with proportional-derivative control. A stability test consisting on analyzing the existence of limit cycles using the Poincare’s method revealed that asymptotically stable walking was achieved. The proposed control strategy effectively rejects the external disturbances and keeps the robot in a stable dynamic walking.

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.

Biologically inspired designs have become evident and proved to be innovative and efficacious throughout the history. This paper introduces a bio-inspired design of protective structures that is lightweight and provides outstanding crashworthiness indicators. In the proposed approach, the protective function of the vehicle structure is matched to the protective capabilities of natural structures such as the fruit peel (e.g., pomelo), abdominal armors (e.g., mantis shrimp), bones (e.g., ribcage and woodpecker skull), as well as other natural protective structures with analogous protective functions include skin and cartilage as well as hooves, antlers, and horns, which are tough, resilient, lightweight, and functionally adapted to withstand repetitive high-energy impact loads. This paper illustrates a methodology to integrate designs inspired by nature, Topology optimization, and conventional modeling tools. Two designs are explained to support this methodology: Helmet design inspired by human bone cellular structure (trabecular structure) and vehicle body inspired by a water droplet, ribcage, and human bone. In the helmet design, a finite part of is optimized using topology optimization to generate the porous structure. In the vehicle body design, a water droplet framework, the bio-inspired simulation-based design algorithm used in this work generates innovative layouts. At the vehicle scale, the generated spaceframe has a structure similar to the one of a long bone. In essence, the aerodynamic water droplet shape is protected by the specialized ribcage. At the component scale, each spaceframe tubular component is filled with a functionally graded cellular structure. This internal cellular structure reminds the one of a bone. The spaceframe is attainable with few parts of greater complexity. Such complex, lightweight, multiscale structural layout can be manufactured using 3D printing technologies..

This work introduces a new Advanced Layered Composite (ALC) design that redirects impact load through the action of a lattice of 3D printed micro-compliant mechanisms. The first layer directly comes in contact with the impacting body and its function is to prevent an intrusion of the impacting body and uniformly distribute the impact forces over a large area. This layer can be made from fiber woven composites imbibed in the polymer matrix or from metals. The third layer is to serve a purpose of establishing contact between the protective structure and body to be protected. It can be a cushioning material or a hard metal depending on the application. The second layer is a compliant buffer zone (CBZ) which is sandwiched between two other layers is responsible for the dampening of most of the impact energy. The compliant buffer zone, comprised by the lattice of micro-compliant mechanism, is designed using topology optimization to dynamically respond by distributing localized impact in the normal direction into a distributed load in the radial direction (perpendicular to the normal direction). The compliant buffer zone depicts a large radial deformation in the middle but not on the surface, which only moves in the normal direction. The effect is a significant reduction of the interfacial shear stress with two adjacent layered phases. A low interfacial shear stress translates into a reduced delamination. The ALC’s response to the impact is tested by using dynamic finite element analysis. The proposed ALC design is intended to be used for the design of protective devices such as helmets and crashworthy components in vehicle structures.

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.

During the injection molding cycle, molten material is injected at high pressure inside the mold andcooled down to form a solid part. This creates thermomechanical stresses that are alleviated by the cor-rect design of a cooling system. In conventional molds, the cooling system consists of straight-line coolingchannels, which can be manufactured using machining processes; however, they are thermally inefficientand unable to cool the injected part uniformly. The emergence of metal-based additive manufacturingtechniques such as direct metal laser sintering (DMLS) allows the fabrication of molds with conformalcooling channels. Conformal cooling molds cool down the part faster and more uniformly; however, theyface limitations. First, their fabrication cost is 10 to 20 times higher than the one of a conventional mold.Second, the DMLS process, which is the most popular fabrication method of conformal cooling molds,produces internal thermal stresses that distort the mold. The development of structural optimizationmethods such as multiscale topology optimization offers the potential to create novel and complex cel-lular structures that alleviate these current limitations. The objective of this research is to establish amultiscale topology optimization method for the optimal design of non-periodic cellular structures sub-jected to thermomechanical loads. The result is a hierarchically complex design that is thermally efficient,mechanically stable, and suitable for additive manufacturing. The proposed method seeks to minimizethe mold mass at the macroscale, while satisfying the thermomechanical constraints at the mesoscale.The thermomechanical properties of the mesoscale cellular unit cells are estimated using homogenizationtheory. A gradient-based optimization algorithm is used for which macroscale and mesoscale sensitivitycoefficients are derived. The design and evaluation of a porous injection mold is presented to demonstratethe proposed optimization method.

One of the primary challenges faced in Additive Manufacturing (AM) is reducing the overall cost and printing time. A critical factor in cost and time reduction is post-processing of 3D printed (3DP) parts, which includes removing support structures. Support is needed to prevent the collapse of the part or certain areas under its own weight during the 3D printing process. Currently, the design of self-supported 3DP parts follows experimental trials. A trial and error process is needed to produce high quality parts by Fused Depositing Modeling (FDM). An example for a chamfer angle, is the common use of 45 degree angle in the AM process. Surfaces that are more flat show defects than inclined surfaces, and therefore a numerical model is needed. The model can predict the problematic areas at a print, reducing the experimental prints and providing a higher number of usable parts. Physical-based models have not been established due to the generally unknown properties of the material during the AM process. With simulations it is possible to simulate the part at different temperatures with a variety of other parameters that have influence on the behavior of the model. In this research, analytic calculations and physical tests are carried out to determine the material properties of the thermoplastic polymer Acrylonitrile - Butadiene - Styrene (ABS) for FDM at the time of extrusion. This means that the ABS is going to be extruded at 200C to 245C and is a viscus material during part construction. Using the results from the physical and analytical models, i.e., Timoshenko’s modified beam theory for micro structures, a numerical material model is established to simulate the filament deformation once it is deposited onto the part. Experiments were also used to find the threshold for different geometric specifications, which could then be applied to the numerical model to improve the accuracy of the simulation. The result of the nonlinear finite element analysis is compared to experiments to show the correlation between the prediction of deflection in simulation and the actual deflection measured in physical experiments. A case study was conducted using an application that optimizes topology of complex geometries. After modeling and simulating the optimized part, areas of defect and errors were determined in the simulation, then verified and and measured with actual 3D prints

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This work introduces a multimaterial density-based topology optimization method suitable for nonlinear structural problems. The proposed method consists of three stages: continuous density distribution, clustering, and metamodel-based optimization. The initial continuous density distribution is generated following a synthesis strategy without penalization, e.g., the hybrid cellular automaton (HCA) method. In the clustering stage, unsupervised machine learning (e.g., K-means clustering) is used to optimally classify the continuous density distribution into a finite number of clusters based on their similarity. Finally, a metamodel (e.g., Kriging interpolation) is generated and iteratively updated following a global optimization algorithm (e.g., genetic algorithms) to ultimately converge to an optimal material distribution. The proposed methodology is demonstrated with the design of multimaterial stiff (minimum compliance) structures, compliant mechanisms, and a thin-walled S-rail structure for crashworthiness. Copyright © 2015 by ASME Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

This work presents a framework for optimizing additive manufacturing of plastic injection molds. The proposed system consists of three modules, namely process and material modeling, multi-scale topology optimization, and experimental testing, calibration and validation. Advanced numerical simulation is implemented for a typical die with conformal cooling channels to predict cycle time, part quality and tooling life. A multi-scale thermo-mechanical topology optimization algorithm is being developed to minimize the die weight and enhance its thermal performance. The technique is implemented for simple shapes for validation before it is applied to dies with conformal cooling in future work. Finally, material modeling using simulation as well as design of experiments is underway for obtaining the material properties and their variations.

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.

With support from the Walmart U.S. Manufacturing Innovation Fund, researchers at the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) are embarking on a project to reduce cost and increase performance of U.S. manufactured injection tooling (dies and molds) through experimentally supported structural design optimization methods and metal additive manufacturing. The Walmart U.S. Manufacturing Innovation Fund is a partnership between Walmart, the Walmart Foundation, and the United States Conference of Mayors (USCM). The Fund is focused on the development of U.S. manufacturing, with a specific goal of making it both easier and more competitive to make household goods in the United States.

Thin-walled tubular components are commonly used as deformable crushing elements in vehicle structures due to their high specific energy absorption when they progressively fold during a crash event. However, oblique loading and geometric imperfections promote global collapse characterized by Euler-type bending that compromises their energy absorption capabilities. In order to avoid this undesirable condition, we have proposed the use of compliant mechanism synthesis methods to transfer impact energy from input to selected output ports forcing a desired collapse mode. The resulting conceptual design is characterized by an innovative continuous thickness distribution along the tube, which increases its energy absorption but faces several manufacturing difficulties. The objective of this research is to determine the optimal number of clusters and cluster thicknesses that maximize the tube’s energy absorption capabilities. In order to identify the optimal number of clusters, we propose the implementation of a k-means clustering technique that minimizes the similarity between cluster thicknesses. Once the optimal number of cluster is defined, we propose the use of a sequential Kriging-based multi-objective optimization method to maximize the tubes’s specific energy absorption and minimize peace crushing force. The result is the Pareto-optimal set of manufacturable thin-walled structures with an innovative thickness distribution. These solutions, as well as the final synthesized design, are compared with a linearly graded structure which is currently used in automotive applications. The proposed clustering and multiobjective optimization method is applicable any conceptual design of thin-walled components characterized by a continuous thickness distribution.

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This work presents the automation of high-accuracy CNC tool trajectory planning from CAD to G-code generation through optimal NURBs surface approximation. The proposed optimization method finds the minimum number of NURBS control points for a given admissible theoretical cord error between the desired and manufactured surfaces. The result is a compact part program that is less sensitive to data starvation than circular and spline interpolations with potential better surface finish. The proposed approach is demonstrated with the tool path generation of an involute gear profile.

This paper presents the multidisciplinary design optimization (MDO) and fabrication of four robotic football players: quarterback, center, and two receivers. Each robot has a footprint of up to 16 square inches and is up to 24 inches high. The game of American football is played in an enclosed arena similar to a basketball court and each robot is remotely controlled. The design, fabrication, and operation of the robots involves Indiana University-Purdue University Indianapolis (IUPUI) undergraduates majoring in STEM disciplines, including mechanical, electrical, and computer engineering. The students are exposed to numerous engineering design challenges, such as shock absorbent structure design, fast and dexterous robot maneuvering, development of robust and reliable control hardware and software, and ball transfer between robots in a highly unpredictable game environment. To address these challenges, we adopted a collaborative optimization (CO) approach. CO is a multi-level MDO method that incorporates system-level and subsystem-level optimization. Five disciplines emerged in the course of this project, namely: structures, mechanisms, electronics, software, and manufacturing. CO’s advantage over other MDO methods is that it allows disciplinary autonomy while achieving interdisciplinary compatibility. The effectiveness of this experience is demonstrated with the multidisciplinary design, fabrication, and operation of the IUPUI-Butler robotic football team in a game environment.

One of the most promising methods to assist amputated or paralyzed patients in the control of prosthetic devices is the use of a brain computer interface (BCI). The use of a BCI allows the communication between the brain and the prosthetic device through signal processing protocols. However, due to the noisy nature of the brain signal, available signal processing protocols are unable to correctly interpret the brain commands and cannot be used beyond the laboratory setting. To address this challenge, in this work we present a novel automatic brain signal recognition protocol based on vowel articulation mode. This approach identifies the mental state of imagery of open-mid and closed vowels without the imagination of the movement of the oral cavity, for its application in prosthetic device control. The method consists on using brain signals of the language area (21 electrodes) with the specific task of thinking the respective vowel. In the prosecution stage, the power spectral density (PSD) was calculated for each one of the brain signals, carrying out the classification process with a Support Vector Machine (SVM). A measurement of precision was achieved in the recognition of the vowels according to the articulation way between 84% and 94%. The proposed method is promissory for the use of amputated or paraplegic patients.

The burgeoning growth of tall buildings around the world requires novel design methodologies to resolve design challenges imposed by the enormous volume of material and energy employed during their construction and operation. To meet this upcoming social demand on tall buildings, practical and efficient design method is proposed for optimal outrigger placement using topology optimization. Outriggers, one of the key structural components in a tall and narrow building, are the horizontal structures which connect the building core (spine) and the exterior surface in order to improve the building’s shear stiffness. In the proposed method, the high-fidelity simulation model of a tall building is constructed with multiple finite element types for the core and the reinforcing truss system. The floor-wise outriggers are parameterized using the Simple Isotropic Material with Penalization (SIMP) and defined as the design variables. The outrigger placement problem is solved using topology optimization. The continuation method is used for material penalization parameter in order to obtain “0–1” design. The versatility of the proposed design methodology is proven using the realistic FEM model of a three-dimensional 201 m tall building.

Biological structures and materials are continually adapting to changes in their physical environment. In bones, for example, it has been widely accepted that mineral tissue is resorbed in regions exposed to low mechanical stimulus, whereas new bone is deposited where the stimulus is high. This process of functional adaptation is thought to enable bone to perform its mechanical functions with a minimum of mass. Many theoretical models for bone remodeling use the concept of an error signal as part of a strategy to simulate bone structural adaptation. These models imply the existence of an equilibrium state (or zero error condition) where the bone structure is adapted to the environment and no net remodeling is required. Simulations of bone functional adaptation can be applied to improving our understanding of age related bone loss and to the design of traditional and tissue-engineered devices. In the last three decades, several mathematical models have been proposed to explain and predict bone functional adaptation to changes in its mechanical environment. The first practical computational models were developed under the assumption of isotropy of the trabecular structure in the continuum level. Despite the similarities in density distribution with in-vivo bone, no convergent solution was possible to obtain with these models. Recent models have been developed to consider the anisotropic nature of the trabecular bone in the continuum level making use of optimization principles; despite of some mechanical aspects reflected by these idealized microstructures, they just represent a mathematical abstraction of the trabecular structure. The objective of this investigation is to develop a an algorithm that incorporates tissue-level mechanisms of bone functional adaptation compatible with both phenomenological and optimization approaches. The resulting trabecular structure will also be incorporated into a continuum level model. In this way, the anisotropic nature of the trabecular bone will be determined by direct simulation and not by using a mathematical approximation. This technique makes use of the cellular automaton (CA) paradigm and concepts of structural optimization. The algorithm developed in this work makes use of the finite element method (FEM) to perform structural analysis. The distribution of material across the design domain is parameterized into a continuous variable density as a function of a mechanical stimulus. The design domain is composed of a lattice of cells or cellular automata. Sensor cells or ostecytes are ideally localized along the design domain. The osteocytes act as sensors of variations in the mechanical stimulus over time within a certain radius of action. They activate local processes of formation and resorption of bone tissue. Parameters of the proposed algorithm include mechanotransduction of variations in mechanical stimulus into remodeling signal and intracellular communication. This algorithm has been applied to a variety of two- and three-dimensional structural models. These models exhibit self trabeculation for all parameters applied. Changes in the parameters resulted in changes in both density and the architecture of the structure.

Competitive marketplaces have driven the need for simulation-based design optimization to produce efficient and cost-effective designs. However, such design practices typically do not take into account model uncertainties or manufacturing tolerances. Such designs may lie on failure-driven constraints and are characterized by a high probability of failure. Reliability-based design optimization (RBDO) methods have been developed to obtain designs that optimize a merit function while ensuring a target reliability level is satisfied. Unfortunately, these methods are notorious for the high computational expense they require to converge. In this research variable-fidelity methods are used to reduce the cost of RBDO. Variable-fidelity methods use a set of models with varying degrees of fidelity and computational expense to aid in reducing the cost of optimization. The variable-fidelity RBDO methodology developed in this investigation is demonstrated on two test cases: a nonlinear analytic problem and a high-lift airfoil design problem. For each of these problems the proposed method shows considerable savings for performing RBDO as compared with standard approaches.

Topology optimization allows designers to obtaining lightweight structures considering the binary distribution of a solid material. Further material savings and increased performance may be achieved if the material and the structure topologies are concurrently optimized. The use of homogenization methods promotes the introduction of material-scale parameters in the problem’s formulation. While some research has been focused on material parameters and periodic topology optimization, this work deals with non-periodic material topologies. Since no preconceived material and structure geometries are considered, the multiscale approach is capable of driving the design to innovative and potentially better configurations at both length scales. The proposed methodology is applied to minimum compliance problems and compliant mechanism synthesis. The multiscale results are compared with the traditional structural-level designs in the context of Pareto solutions, demonstrating benefits of ultra-lightweight configurations

Biologically inspired designs have become evident and proved to be innovative and efficacious throughout the history. This paper introduces a bio-inspired design of protective structures that is lightweight and provides outstanding crashworthiness indicators. In the proposed approach, the protective function of the vehicle structure is matched to the protective capabilities of natural structures such as the fruit peel (e.g., pomelo), abdominal armors (e.g., mantis shrimp), bones (e.g., ribcage and woodpecker skull), as well as other natural protective structures with analogous protective functions include skin and cartilage as well as hooves, antlers, and horns, which are tough, resilient, lightweight, and functionally adapted to withstand repetitive high-energy impact loads. This paper illustrates a methodology to integrate designs inspired by nature, Topology optimization, and conventional modeling tools. Two designs are explained to support this methodology: Helmet design inspired by human bone cellular structure (trabecular structure) and vehicle body inspired by a water droplet, ribcage, and human bone. In the helmet design, a finite part of is optimized using topology optimization to generate the porous structure. In the vehicle body design, a water droplet framework, the bio-inspired simulation-based design algorithm used in this work generates innovative layouts. At the vehicle scale, the generated spaceframe has a structure similar to the one of a long bone. In essence, the aerodynamic water droplet shape is protected by the specialized ribcage. At the component scale, each spaceframe tubular component is filled with a functionally graded cellular structure. This internal cellular structure reminds the one of a bone. The spaceframe is attainable with few parts of greater complexity. Such complex, lightweight, multiscale structural layout can be manufactured using 3D printing technologies.

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Topology optimization allows designers to obtaining lightweight structures considering the binary distribution of a solid material. Further material savings and increased performance may be achieved if the material and the structure topologies are concurrently optimized. The use of homogenization methods promotes the introduction of material-scale parameters in the problem’s formulation. While some research has been focused on material parameters and periodic topology optimization, this work deals with non-periodic material topologies. Since no preconceived material and structure geometries are considered, the multiscale approach is capable of driving the design to innovative and potentially better configurations at both length scales. The proposed methodology is applied to minimum compliance problems and compliant mechanism synthesis. The multiscale results are compared with the traditional structural-level designs in the context of Pareto solutions, demonstrating benefits of ultra-lightweight configurations.

This investigation introduces a methodology to design dynamically crushed thin-walled tubular structures for crashworthiness applications. Due to their low cost, high-energy absorption efficiency, and capacity to withstand long strokes, thin-walled tubular structures are extensively used in the automotive industry. Tubular structures subjected to impact loading may undergo three modes of deformation: progressive crushing/buckling, dynamic plastic buckling, and global bending or Euler-type buckling. Of these, progressive buckling is the most desirable mode of collapse because it leads to a desirable deformation characteristic, low peak reaction force, and higher energy absorption efficiency. Progressive buckling is generally observed under pure axial loading; however, during an actual crash event, tubular structures are often subjected to oblique impact loads in which Euler-type buckling is the dominating mode of deformation. This undesired behavior severely reduces the energy absorption capability of the tubular structure. The design methodology presented in this paper relies on the ability of a compliant mechanism to transfer displacement and/or force from an input to desired output port locations. The suitable output port locations are utilized to enforce desired buckle zones, mitigating the natural Euler-type buckling effect. The problem addressed in this investigation is to find the thickness distribution of a thin-walled structure and the output port locations that maximizes the energy absorption while maintaining the peak reaction force at a prescribed limit. The underlying design for thickness distribution follows a uniform mutual potential energy density under a dynamic impact event. Nonlinear explicit finite element code LS-DYNA is used to simulate tubular structures under crash loading. Biologically inspired hybrid cellular automaton (HCA) method is used to drive the design process. Results are demonstrated on long straight and S-rail tubes subject to oblique loading, achieving progressive crushing in most cases.

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This paper presents an overview of a proposed tall building design platform that has the aim of determining the optimal external shape and structural system for tall buildings subject to aerodynamic loads. The platform is intended to bridge the gap between the traditionally manual conceptual design stage and the more automated detailed design stage in an attempt to define a new generation of innovative tall buildings. Shape optimization will be used to sculpt the optimal external profile of the building, topology optimization will be utilized to determine the optimal configuration of the structural system, and reliability-based optimization algorithms will be used in the detailed design stages. This paper presents the results of several preliminary studies concerning the topology optimization stage of the platform, performed on simplified building structures, to highlight the concept and benefits of this approach.

Structural design for crashworthiness is a challenging area of research due to large plastic deformations and complex interactions among diverse components of the vehicle. A notable idea in topology optimization is the hybrid cellular automaton (HCA) method capable of topology synthesis for crashworthiness design. The HCA algorithm was inspired by the structural adaptation of bones to their ever changing mechanical environment. This methodology has been shown to be an effective topology synthesis tool.The objective of this investigation is to examine the convergence and algorithm factors analysis of topology optimization for crashworthiness based on hybrid cellular automata paradigm. The orthogonal test is also proposed to study the effects of the algorithm factors on the dependent variables of the structure with new optimized topology. To demonstrate the convergence properties influenced by factors of the HCA algorithm in dynamic problems, the HCA framework is developed to a methodology for crashworthiness, which combines transient, non-linear finite-element analysis and local control rules acting on cells, and some simple cantilevered beam examples are utilized.

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This work presents a novel method for designing crashworthy structures with controlled energy absorption based on the use of compliant mechanisms. This method helps in introducing flexibility at desired locations within the structure, which in turn reduces the peak force at the expense of a reasonable increase in intrusion. For this purpose, the given design domain is divided into two subdomains: flexible (FSD) and stiff (SSD) subdomains. The design in the flexible subdomain is governed by the compliant mechanism synthesis approach for which output ports are defined at the interface between the two subdomains. These output ports aid in defining potential load paths and help the user make better use of a given design space. The design in the stiff subdomain is governed by the principle of a fully-stressed design for which material is distributed to achieve uniform energy distribution within the design space. Together, FSD and SSD provide for a combination of flexibility and stiffness in the structure, which is desirable for most crash applications. Copyright © 2012 by ASME Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

Each year, bone metabolic diseases affect millions of people of all ages, genders, and races. Common diseases such as osteopenia and osteoporosis result from the disruption of the bone remodeling process and can place an individual at a serious fracture risk. Bone remodeling is the complex process by which old bone is replaced with new tissue. This process occurs continuously in the body and is carried out by bone cells that are regulated by numerous metabolic and mechanical factors. The remodeling process provides for various functions such as adaptation to mechanical loading, damage repair, and mineral homeostasis. An improved understanding of this process is necessary to identify patients at risk of bone disease and to assess appropriate treatment protocols.High-fidelity computer models are needed to understand the complex interaction of all parameters involved in bone remodeling. The primary focus of this investigation is to present a new computational framework that utilizes mathematical rules to mechanistically model the cellular mechanisms involved in the bone remodeling process. The computational framework used in this research combines accepted biological principles, cellular-level rules in a cellular automaton framework, and finite-element analysis. This computational model is referred to as hybrid cellular automaton (HCA) model. The simulations obtained with the HCA model allow to predict time-dependent morphology variations at the tissue level as a result of biological changes at the cellular level.

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The aim of this work is to develop a multivariable robust control strategy for a gait-assisting exoskeleton. The exoskeleton is designed to support a patient with lower-limb disability providing lower extremity motion and stability. The proposed controlled strategy is referred to as proportional-integral (PI) vectorial with pole location. This strategy efficiently minimizes the error between the system response and a reference trajectory. This strategy also rejects the disturbances caused by the motion of the upper body. A commutation approach is used for different gate phase control. The performance of the controlled system shows potential application on real-life environments with moderate disturbances.

This paper presents a piezoelectric energy harvesting skin (EHS) design using topology optimization. EHS was motivated to embody a power-generating skin structure by attaching thin piezoelectric patches onto a vibrating skin for the purpose of self-sustainable health monitoring with wireless sensors. In this paper the hybrid cellular automata (HCA) algorithm is involved to optimize piezoelectric material distribution on a harmonically vibrating skin structure. Valid computational (finite element) models for vibrating structure are constructed, and the optimal piezoelectric material distribution is found on a surface of the structure. The piezoelectric material is modeled with penalization, and the optimal density and poling direction is found per each piezoelectric finite element using HCA algorithm. HCA algorithm demonstrated its ability to find the optimal design for piezoelectric material to yield maximum power output.

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This paper examines the continuation method and the conditions under which a gradient-based optimization algorithm converges to a global optimum in topology optimization. The focus of this investigation is the compliance problem. Filtering is used to impose a constraint on the length scale and regularize the optimization problem. This investigation considers the influence of two filtering methods (sensitivity and density) and several weighting functions (e.g., box, hat, and Gaussian). Binary and consistent designs are obtained by filter reduction based on surface recognition. This methodology allows for a fair assessment of the optimum's globality. Several gradient-based optimization algorithms are used in this investigation, including the method of moving asymptotes (MMA), optimality criterion (OC), sequential quadratic programming (SQP), and control-based optimization (CBO) proposed by the authors. The results show that the use of the continuation method matches the analytical solution and the global optimum found by exhaustive search. They also demonstrate convergence with respect to the reduction of the step continuation parameter. These results support the conclusion that the continuation method is an efficient strategy to reach a global optimum.

The primary objective of this investigation is the optimum design of lightweight foam material systems for controlled energy absorption under blast impact. The ultimate goal of these systems is to increase the safety and integrity of occupants and critical components in structural systems such as automotive vehicles, buildings, ships, and aircrafts. Although outstanding results have been achieved with the use of foams in blast protective systems, current design practices rely on trial and error as there is an absence of a systematic design method. While the governing equations are known for a variety of physical phenomena in appropriate length scales, there are no suitable methodologies to accomplish the aforementioned objectives. A promising approach to systematically design the material's microstructure is the use of structural optimization methods. This investigation presents an appropriate design methodology to optimally design foam material systems for blast mitigation. The objective function is expressed in terms of acceleration. Macroscopic effective material properties are used to drive the nonlinear analysis of the elasto-plastic material under time-dependent loading conditions. Gradient-based optimization methods are used to obtain the final density distribution of the foam material system. The application of this approach is shown through a two-dimensional optimization problem.

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Aero-optic aberrations originating from the nearby flowfield of aircraft can seriously limit the ability to focus on-board laser systems onto farfield targets. These aero-optic aberrations can be mitigated by using gences to control the flow around the outgoing beam aperture. The objective of this investigation is to attempt to determine the best shape for these fences using computational fluid dynamics in combination with optimization techniques. Future work will experimentally and computationally build on the solutions presented here.

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A lot of research work has been done in the field of crashworthiness design of structural components and systems. Most of the work in this area considers single load case resulting in designs suitable for only that one loading condition. However, in real life the structure can be impacted in many ways and hence a design should behave reasonably well under multiple loading conditions. One way of accounting this uncertainty in loading conditions is to incorporate the notion of reliability in the design process. Another, computationally less expensive, way is to design the structure for some finite number of representative loading conditions. A number of researchers have incorporated multiple loading consitions in the design method by superimposing over the results for various load cases. A weighted average of certain structural properties like compliance, stress or strain energy are used for the purpose of superimposing over multiple load cases. In most of the work constant (not changin with design cycle) weighting factors are used which are obtained either by design requirements or the probability of occurence of corresponding load cases. In case where few of the load cases dominate over the others, it is observed that the final designs are dictated by the dominating load cases unless the weighting factors for these load cases are deliberately chosen to be comparatively smaller. However a design must behave well for all the considered loading conditions. For crash problems, force displacement behavior is one of the most widely used criteria to measure the performance of a design. In this work, three different strategies are introduced to allocate the associated weighting factors dynamically (changing with design cycle) during the design iterations such that an acceptable force displacement behavior is achieved for all the loading conditions. A well developed, Hybrid Cellular Automation (HCA) based design methodology is used for efficient material distribution within the design domain. A crash problem with three loading conditions is used to demonstrate the implementation and effectiveness of the proposed strategies.

All reported results in literature indicate that even simple topology optimization problems, such as the compliance problem, have many local optimum points. Besides numerical instabilities associated to the mesh size in the finite element model, different algorithms and initial designs lead to different topologies. This phenomenon is referred to as non-uniqueness. This paper studies the conditions under which linear and nonlinear compliance problems have a unique solution, providing practical insights on the conditions that lead to non-uniqueness. To this end, three different optimization algorithms are used: OC-SIMP, SQP, and a new control-based optimization (CBO) method. The latter one has been developed to solve for nonlinear compliance problems, but it shows its best performance when solving for problems involving uniform distribution, e.g., uniform strain energy density distribution (USEDD). The solution obtained with the USEDD criterion are approximate solutions to the ones with minimum compliance; however, they can be easily obtained with the CBO method without sensitivity analysis. Furthermore, the results of this investigation show the approximate solutions closely match the exact solutions of the compliance problem.

A major challenge in the design of structural components subject to dynamic loading is the optimum design of the optimum transient response of the structure. The reduction in size of lightweight, fuel-efficient vehicles has promoted the use of new engineering materials in sophisticated designs of structural components that may act as energy absorbers in a collision. That makes the use of material optimization for crashworthiness attractive and increasing in popularity among automotive companies. This investigation incorporates optimization approach with modeling of viscoelastic structures to minimize the maximum nodal acceleration of the structure as well as minimize the maximum nodal displacement. Uni-dimensional models demonstrate the complexity of the analytical solution. For the theoretical approximation, the dynamic response of structural members is obtained employing Kelvin-Voigt (solid) viscoelastic models. The design variables correspond to the stiffness and damping coefficient of the material. The proposed optimum material system is the base to design components for more complex applications such as crashworthiness and blast mitigation, and to be employed for topology optimization.

Structural optimization efforts for blast mitigation seek to counteract the damaging effects of an impulsive threat on critical components of vehicles and to protect the lives of the crew and occupants. The objective of this investigation is to develop a novel optimization tool that simultaneously accounts for both energy dissipating properties of a shaped hull and the assembly constraints of such a component to the vehicle system. The resulting hull design is shown to reduce the blast loading imparted on the vehicle structure. Component attachment locations are shown to influence the major deformation modes of the target and the final hull design.

A first order structural optimization problem is examined to evaluate the effects of structural geometry on blast energy transfer in a fully coupled fluid structure interaction problem. The fidelity of the fluid structure interaction simulation is shown to yield significant insights into the blast mitigation problem not captured in similar empirically based blast models. An emphasis is placed on the accuracy of simulating such fluid structure interactions and its implications on designing continuum level structures. Higher order design methodologies and algorithms are discussed for the application of such fully coupled simulations on vehicle level optimization problems.

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This paper presents a framework to incorporate the notion of reliability into crashworthy designs of automotive vehicle components. Optimal design for crashworthiness is a challenging task in itself as it involves time dependent complex interactions among bodies along with material and geometric nonlinearities. Maximum energy absorption in the structure is widely used as a design criteria in crashworthiness designs as long as penetration levels are kept under allowable limits. These designs behave well and are safe as long as loading conditions and material properties are deterministic however failure can happen due to excessive penetration under uncertain conditions. Hence a semi coupled reliability based crashworthiness design methodology is proposed in which independent reliability assessments are done on the designs at various intermediate levels during an iterative design cycle of a crashworthy structure. Reliability index approach (RIA) is used to incorporate reliability constraint in the design problem where reliability index is evaluated using first order reliability method (FORM). The most probable point of failure (MPP) is determined with respect to the maximum allowable deflection of the structure when loaded. The reliability constraint is satisfied by changing global mass of the structure as the maximum deflection of the structure is dependent on the mass of the structure. Finally a non gradient based method called hybrid cellular automata (HCA) is used to distribute material within the design domain to achieve maximum energy absorption in the structure. The application of the proposed method will be shown with the help of a practical example.

Formula SAE is a very competitive event in which collegiate engineering teams design, build, and race open-wheeled vehicles. With teams representing the best engineering programs from around the world, small decreases in weight on every component can mean large overall decreased weight for the entire vehicle, leading to faster lap times. The goal of this work is to redesign a wheel upright for a Formula SAE racing vehicle. Being that the vehicle uprights are considered un-sprung weight, any weight savings achieved on their design is worth twice as much as weight savings achieved on sprung vehicle weight. The structural design optimization problem is expressed in terms of two conflicting objectives: minimize the compliance and minimize the weight of the component. The optimization process is performed through a topology optimization approach. Given the loading conditions, tractions, and null elements for the manufacturing of the wheel upright, a new topology is generated. Altair Engineering's OptiStruct software package is used to perform the optimization process. The optimized structure exhibits a 0.31 lb (15%) reduction in mass with no gain in peak stress and practically the same compliance (or stiffness) with respect to the current design. The technique shown in this work can be implemented for other components to decrease the un-sprung weight of the vehicle.

Design for structural topology optimization is a method of distributing material within a design domain of prescribed dimensions. This domain is discretized into a large number of elements in which the optimization algorithm removes, adds, or maintains the amount of material. The resulting structure maximizes a prescribed mechanical performance while satisfying functional and geometric constraints. Among different topology optimization algorithms, the hybrid cellular automaton (HCA) method has proven to be efficient and robust in problems involving large, plastic deformations. The HCA method has been used to design energy absorbing structures subject to crash impact. The goal of this investigation is to extend the use of the HCA algorithm to the design of an advanced composite armor (ACA) system subject to a blast load. The ACA model utilized consists of two phases: ceramic and metallic. In this work, the proposed algorithm drives the optimal distribution of a metallic phase within the design domain. When the blast pressure wave hits the targeted structure, the fluids kinetic energy is transformed into strain energy (SE) inside the solid medium. Maximum attenuation is reached when SE is maximized. Along with an optimum use of material, this condition is satisfied when SE is uniformly distributed in the design domain. This work makes use of the CONWEP model developed by the Army Research Laboratory. The resulting structure shows the potential of the HCA method when designing ACAs.

This work presents an application of the Hybrid Cellular Automation (HCA) approach to the shape optimization of aluminum thin walled structures for blast mitigation. The design objective is to minimize the kinetic energy transferred from the blast event to the target. Fluid structure interaction between blast products and the target structure is considered as the driving factor in the blast mitigation problem. The method proposed couples an Arbitrary Lagrange Eulerian Fluid Structure Interaction FEA model with a reformulated HCA methodology to generate shape function concepts with improved pressure response attributes. This novel methodology is applied to the design of a target plate subject to a simulated surface detonation and shallow buried detonation. The proposed approach is shown to generate intriate shape designs difficult to obtain by function decomposition methods and is computationally effictient in terms of the total function evaluations required to achieve algorithm convergence. Solutions obtained by this method are shown to produce a considerable reduction in peak pressure and impulse imparted to the target over the original design. The optimized shape is dependant upon loading condition: whether the target is loaded by a surface of buried detonation.

A prescribed force-displacement (FD) response of plastically deformable structures subject to impact loading is an essential requisite in crashworthiness design. The FD behavior determines the reaction force, displacement and the internal energy that the structure should withstand. However, attempts to include this requirement in structural topology/ topometry optimization problems remain scarce. Due to the complexity of a crash event and the consequent time required to numerically analyze the dynamic response of the structure, classical methods (i.e., gradient-based and direct) are not developed enough to solve this undertaking. This investigation presents an approach under the framework of the hybrid cellular automaton (HCA) method to solve the above challenge. In this method, a set field variables is driven to their target states by changing a convenient set of design variables (e.g., thickness/density). These rules operate locally in cells within a lattice that only know local conditions. The field variable associated with the cells are driven to a setpoint to get the desired structure. Previous investigations on crashworthiness have primarily focused on distributing a given amount of material within a three-dimensional continuum lattice. More recently, optimizing the thickness distribution for sheet metal structure according to an energy absorption criterion and displacement constraints has been proposed. With the use of HCA rules, manufacturability constraints (e.g., rolling/extrusion) have also been implemented. Even though plastically deformable structures seem appropriate for crashworthiness, they do not succeed when their behavior is compared to an ideal FD curve. The objective of this research is to incorporate a desired FD response as the primary objective function in a topometry optimization problem. To this end, the HCA algorithm is used to design for structures with controlled energy absorption with specified buckling zones. The peak reaction force and the maximum displacement are also constrained to meet the desired safety level according to passenger safety regulations. Design for prescribed FD response by minimizing the error between the actual response and seeded points in the desired FD curve is implemented. This methodology is applied to a shock-absorbing structure for passengers in a crashing automotive. These results are compared to previous designs showing the benefits of the method introduced in this investigation. Copyright © 2010 by the American Institute of Aeronautics and Astronautics, Inc.

Axially crushed thin-walled tubular structures are extensively used as energy absorbers in various automotive and aerospace applications because of their high energy absorption efficiency and long strokes. Various experimental and numerical studies in the past have revealed that a thin-walled square tube can under go mainly three modes of deformation, progressive crushing/buckling, dynamic plastic buckling and global bending depending up on the loading conditions and tube geometry among many factors. Out of these three, progressive crushing/buckling is the most desired mode of collapse for efficient energy absorption. Moreover, crushing starting from one end progressing systematically towards the other end is preferred because of availability of more space/material for plastic deformation without jamming resulting in increased peak forces. A thin square tube can show four possible modes of collapse during progressive crushing depending up on the ratio of width to thickness. The mean crush load for extensional mode of collapse is higher than the other three modes resulting in higher energy absorption during the overall crushing event. The crush behavior of thin square columns in case of oblique impact is highly dependent on the angle of impact. In the situations when impact angle is higher than a critical value, the mean crush load may drop by 40% than the axial crushing load because of global bending. In this paper, a novel approach based on compliant mechanism design which is extensively employed in topology optimization of MEMS is used to design the thin-walled square tubes. The ability of a compliant mechanism to transfer or transform displacement, force or energy from an input load location to the desired output locations is utilized to enforce the desired buckle zones in the axial member. A biologically inspired, gradient free hybrid cellular automata (HCA) method is used to synthesize compliant mechanisms with elemental thickness distribution governed by an enrgy like functional, mutual potential energy (MPE). Nonlinear explicit finite element code LS-DYNA is used to simulate quasi-static axial crushing of the thin square columns in this paper. Numerical results show that progressive crushing in a desired mode of collapse can be enforced in axial and oblique loading conditions using the proposed methodology.

The bone remodeling process provides for various functions such as mineral homeostasis, damage repair, and adaptation to mechanical loading. At present, a clear link between the mechanical stimulation of bones and the biochemical response is not fully understood. Computational simulations can provide a means to test hypotheses and gain insight into processes that are difficult to examine experimentally. The objective of this work is to predict the effect of damage and strain as the stimulus for regulating the cellular signaling activity of remodeling. In this study, potential signaling pathways that mediate this cellular activity were incorporated in a hybrid cellular automaton (HCA) algorithm. Biological rules were implemented in this model to control recruitment, differentiation, and activation of osteoclasts. Prominent processes for describing recruitment and inhibition of the bone cells, as reported from experimental studies, are utilized. This work focuses on the resorption of a damaged site on a trabecular strut.

One of the most intriguing aspects of bone is its ability to grow, repair damage, adapt to mechanical loads, and maintain mineral homeostasis [1]. It is generally accepted that bone adaptation occurs in response to the mechanical demands of our daily activities; moreover, strain and microdamage have been implicated as potential stimuli that regulate bone remodeling [2]. Computational models have been used to simulate remodeling in an attempt to better understand the metabolic activities which possess the key information of how this process is carried out [3]. At present, the connection between the cellular activity of remodeling and the applied mechanical stimuli is not fully understood. Only a few mathematical models have been formulated to characterize the remolding process in terms of the cellular mechanisms that occur [4,5].

Structural design for crashworthiness is a challenging area of research due to large plastic deformations and complex interactions among diverse components of the vehicle. Previous research in this field primarily focused on energy absorbing structures that utilise a desired amount of material. These structures have been shown to absorb a large amount of the kinetic energy generated during the crash event; however, the large plastic strains experienced can lead to material failure and loss of structural integrity. This research introduces a strain-based, dynamical multi-domain topology optimisation algorithm for crashworthy structures undergoing large deformations. This technique makes use of the hybrid cellular automaton framework, which combines transient, non-linear finite-element analysis and local control rules acting on cells. The set of all cells defines the design domain. In the proposed algorithm, the design domain is dynamically divided into two sub-domains for different objectives, i.e., high-strain sub-domain (HSSD) and low-strain sub-domain (LSSD). The distribution of these sub-domains is determined by a plastic strain limit value. During the design process, the material is distributed within the LSSD to distribute internal energy uniformly. In the HSSD, the material is distributed to satisfy a failure criterion given by a maximum strain value. Results show that the new formulation and algorithm are suitable for practical applications. The case study presented demonstrates the potential significance of this work for a wide range of engineering design problems.

Design for structural topology optimization is a method of distributing material within a design domain of prescribed dimensions. This domain is discretized into a large number of elements in which the optimization algorithm removes, adds, or maintains the amount of material. The resulting structure maximizes a prescribed mechanical performance while satisfying functional and geometric constraints. Among different topology optimization algorithms, the hybrid cellular automaton (HCA) method has proven to be efficient and robust in problems involving large, plastic deformations. The HCA method has been used to design energy absorbing structures subject to crash impact. The goal of this investigation is to extend the use of the HCA algorithm to the design of an advanced composite armor (ACA) system subject to a blast load. The ACA model utilized consists of two phases: ceramic and metallic. In this work, the proposed algorithm drives the optimal distribution of a metallic phase within the design domain. When the blast pressure wave hits the targeted structure, the fluids kinetic energy is transformed into strain energy (SE) inside the solid medium. Maximum attenuation is reached when SE is maximized. Along with an optimum use of material, this condition is satisfied when SE is uniformly distributed in the design domain. This work makes use of the CONWEP model developed by the Army Research Laboratory. The resulting structure shows the potential of the HCA method when designing ACAs.