The objective of this research is to study the effect of graphene platelet (GPL) loading on the machinability of epoxy-based GPL composites. To this end, micro-milling experiments are conducted on composites with varying GPL content and their results are contrasted against that of plain epoxy. The material microstructure is characterized using transmission electron microscopy and scanning electron microscopy methods. Chip morphology, cutting force, machined surface morphology, and tool wear, are employed as the machinability measures for comparative purposes. At lower loadings of GPL (0.1% and 0.2% by weight), the deformation of the polymer phase plays a major role; whereas, at a higher loading of 0.3% by weight, the GPL agglomerates and interface-dominated failure dictates the machining response. The minimum chip thickness value of the composites decreases with an increase in GPL loading. Overall, the 0.2% GPL composite has the highest cutting force and the lowest tool wear.
Bayesian Optimization (BO) has shown significant success in tackling expensive low-dimensional black-box optimization problems. Many optimization problems of interest are high-dimensional, and scaling BO to such settings remains an important challenge. In this paper, we consider generalized additive models in which low-dimensional functions with overlapping subsets of variables are composed to model a high-dimensional target function. Our goal is to lower the computational resources required and facilitate faster model learning by reducing the model complexity while retaining the sample-efficiency of existing methods. Specifically, we constrain the underlying dependency graphs to tree structures in order to facilitate both the structure learning and optimization of the acquisition function. For the former, we propose a hybrid graph learning algorithm based on Gibbs sampling and mutation. In addition, we propose a novel zooming-based algorithm that permits generalized additive models to be employed more efficiently in the case of continuous domains. We demonstrate and discuss the efficacy of our approach via a range of experiments on synthetic functions and real-world datasets.
The objective of this research is to study the effect of graphene platelet (GPL) loading on the machinability of epoxy-based GPL composites. To this end, micro-milling experiments are conducted on composites with varying GPL content and their results are contrasted against that of plain epoxy. The material microstructure is characterized using transmission electron microscopy and scanning electron microscopy methods. Chip morphology, cutting force, machined surface morphology, and tool wear, are employed as the machinability measures for comparative purposes. At lower loadings of GPL (0.1% and 0.2% by weight) the deformation of the polymer phase plays a major role, whereas at a higher loading of 0.3% by weight, the GPL agglomerates and interface-dominated failure dictates the machining response. The minimum chip thickness value of the composites decreases with an increase in GPL loading. Overall, the 0.2% GPL composite has the highest cutting force and the lowest tool wear.
Bayesian Optimization (BO) has shown significant success in tackling expensive low-dimensional black-box optimization problems. Many optimization problems of interest are high-dimensional, and scaling BO to such settings remains an important challenge. In this paper, we consider generalized additive models in which low-dimensional functions with overlapping subsets of variables are composed to model a high-dimensional target function. Our goal is to lower the computational resources required and facilitate faster model learning by reducing the model complexity while retaining the sample-efficiency of existing methods. Specifically, we constrain the underlying dependency graphs to tree structures in order to facilitate both the structure learning and optimization of the acquisition function. For the former, we propose a hybrid graph learning algorithm based on Gibbs sampling and mutation. In addition, we propose a novel zooming-based algorithm that permits generalized additive models to be employed more efficiently in the case of continuous domains. We demonstrate and discuss the efficacy of our approach via a range of experiments on synthetic functions and real-world datasets. IntroductionBayesian Optimization (BO) is a widespread method for sequential global optimization (Snoek, Larochelle, and Adams 2012), and is suited to scenarios in which the target function f is unknown and expensive to evaluate. BO was traditionally used in model selection (Močkus 1975) and hyperparameter tuning (Snoek, Larochelle, and Adams 2012; Swersky, Snoek, and Adams 2013). Recently, BO has also found success in black-box adversarial attack (Ru et al. 2020), robotics (Jaquier et al. 2020), finance (Gonzalvez et al. 2019), pharmaceutical product development (Sano et al. 2019), natural language processing (Yogatama, Kong, and Smith 2015), and more. Two critical ingredients of BO include a model that captures prior beliefs about the objective function, and an acquisition function that can be optimized efficiently.BO has been most successful in low dimensions (i.e. 10 or less) (Wang et al. 2013;Nayebi, Munteanu, and Poloczek 2019), whereas many applications require optimization in higher-dimensional spaces; this remains a critical problem in
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