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  1. Abstract

    Magnetoactive elastomers (MAEs) are capable of large deformation, shape programming, and moderately large actuation forces when driven by an external magnetic field. These capabilities enable applications such as soft grippers, biomedical devices, and actuators. To facilitate complex shape deformation and enhanced range of motion, a unimorph can be designed with varying geometries, behave spatially varying multi-material properties, and be actuated with a non-uniform external magnetic field. To predict actuation performance under these complex conditions, an analytical model of a segmented MAE unimorph is developed based on beam theory with large deformation. The effect of the spatially-varying magnetic field is approximated using a segment-wise effective torque. The model accommodates spatially varying concentrations of magnetic particles and differentiates between the actuation mechanisms of hard and soft magnetic particles by accommodating different assumptions concerning the magnitude and direction of induced magnetization under a magnetic field. To validate the accuracy of the model predictions, four case studies are considered with various magnetic particles and matrix materials. Actuation performance is measured experimentally to validate the model for the case studies. The results show good agreement between experimental measurements and model predictions. A further parametric study is conducted to investigate the effects of the magnetic properties of particles and external magnetic fields on the free deflection. In addition, complex shape programming of the unimorph actuator is demonstrated by locally altering the geometric and material properties.

     
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  2. Purpose Recent work has demonstrated the possibility of selectively sintering polymer powders with radio frequency (RF) radiation as a means of rapid, volumetric additive manufacturing. Although RF radiation can be used as a volumetric energy source, non-uniform heating resulting from the sample geometry and electrode configuration can lead to adverse effects in RF-treated samples. This paper aims to address these heating uniformity issues by implementing a computational design strategy for doped polymer powder beds to improve the RF heating uniformity. Design/methodology/approach Two approaches for improving the RF heating uniformity are presented with the goal of developing an RF-assisted additive manufacturing process. Both techniques use COMSOL Multiphysics® to predict the temperature rise during simulated RF exposure for different geometries. The effectiveness of each approach is evaluated by calculating the uniformity index, which provides an objective metric for comparing the heating uniformity between simulations. The first method implements an iterative heuristic tuning strategy to functionally grade the electrical conductivity within the sample. The second method involves reorienting the electrodes during the heating stage such that the electric field is applied in two directions. Findings Both approaches are shown to improve the heating uniformity and predicted part geometry for several test cases when applied independently. However, the greatest improvement in heating uniformity is demonstrated by combining the approaches and using multiple electrode orientations while functionally grading the samples. Originality/value This work presents an innovative approach for overcoming RF heating uniformity issues to improve the resulting part geometry in an RF-assisted, volumetric additive manufacturing method. 
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  3. null (Ed.)
    Purpose Additive manufacturing (AM) of thermoplastic polymers for powder bed fusion processes typically requires each layer to be fused before the next can be deposited. The purpose of this paper is to present a volumetric AM method in the form of deeply penetrating radio frequency (RF) radiation to improve the speed of the process and the mechanical properties of the polymer parts. Design/methodology/approach The focus of this study was to demonstrate the volumetric fusion of composite mixtures containing polyamide (nylon) 12 and graphite powders using RF radiation as the sole energy source to establish the feasibility of a volumetric AM process for thermoplastic polymers. Impedance spectroscopy was used to measure the dielectric properties of the mixtures as a function of increasing graphite content and identify the percolation limit. The mixtures were then tested in a parallel plate electrode chamber connected to an RF generator to measure the heating effectiveness of different graphite concentrations. During the experiments, the surface temperature of the doped mixtures was monitored. Findings Nylon 12 mixtures containing between 10% and 60% graphite by weight were created, and the loss tangent reached a maximum of 35%. Selective RF heating was shown through the formation of fused composite parts within the powder beds. Originality/value The feasibility of a novel volumetric AM process for thermoplastic polymers was demonstrated in this study, in which RF radiation was used to achieve fusion in graphite-doped nylon powders. 
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  4. Abstract Supervised machine learning techniques have proven to be effective tools for engineering design exploration and optimization applications, in which they are especially useful for mapping promising or feasible regions of the design space. The design space mappings can be used to inform early-stage design exploration, provide reliability assessments, and aid convergence in multiobjective or multilevel problems that require collaborative design teams. However, the accuracy of the mappings can vary based on problem factors such as the number of design variables, presence of discrete variables, multimodality of the underlying response function, and amount of training data available. Additionally, there are several useful machine learning algorithms available, and each has its own set of algorithmic hyperparameters that significantly affect accuracy and computational expense. This work elucidates the use of machine learning for engineering design exploration and optimization problems by investigating the performance of popular classification algorithms on a variety of example engineering optimization problems. The results are synthesized into a set of observations to provide engineers with intuition for applying these techniques to their own problems in the future, as well as recommendations based on problem type to aid engineers in algorithm selection and utilization. 
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  5. Exploration of a design space is the first step in identifying sets of high-performing solutions to complex engineering problems. For this purpose, Bayesian network classifiers (BNCs) have been shown to be effective for mapping regions of interest in the design space, even when those regions of interest exhibit complex topologies. However, identifying sets of desirable solutions can be difficult with a BNC when attempting to map a space where high-performance designs are spread sparsely among a disproportionately large number of low-performance designs, resulting in an imbalanced classifier. In this paper, a method is presented that utilizes probabilities of class membership for known training points, combined with interpolation between those points, to generate synthetic high-performance points in a design space. By adding synthetic design points into the BNC training set, a designer can rebalance an imbalanced classifier and improve classification accuracy throughout the space. For demonstration, this approach is applied to an acoustics metamaterial design problem with a sparse design space characterized by a combination of discrete and continuous design variables. Paper No: DETC2018-85274 
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  6. Modern design problems present both opportunities and challenges, including multifunctionality, high dimensionality, highly nonlinear multimodal responses, and multiple levels or scales. These factors are particularly important in materials design problems and make it difficult for traditional optimization algorithms to search the space effectively, and designer intuition is often insufficient in problems of this complexity. Efficient machine learning algorithms can map complex design spaces to help designers quickly identify promising regions of the design space. In particular, Bayesian network classifiers (BNCs) have been demonstrated as effective tools for top-down design of complex multilevel problems. The most common instantiations of BNCs assume that all design variables are independent. This assumption reduces computational cost, but can limit accuracy especially in engineering problems with interacting factors. The ability to learn representative network structures from data could provide accurate maps of the design space with limited computational expense. Population-based stochastic optimization techniques such as genetic algorithms (GAs) are ideal for optimizing networks because they accommodate discrete, combinatorial, and multimodal problems. Our approach utilizes GAs to identify optimal networks based on limited training sets so that future test points can be classified as accurately and efficiently as possible. This method is first tested on a common machine learning data set, and then demonstrated on a sample design problem of a composite material subjected to a planar sound wave. 
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