Geometric fidelity of 3D printed products is critical for additive manufacturing (AM) to be a direct manufacturing technology. Shape deviations of AM built products can be attributed to multiple variation sources such as substrate geometry defect, disturbance in process variables, and material phase change. Three strategies have been reported to improve geometric quality in AM: (1) control process variables x based on the observed disturbance of process variables Ax, (2) control process variables x based on the observed product deviation Ay, and (3) control input product geometry y based on the observed product deviation Ay. This study adopts the third strategy which changes the computer-aided design (CAD) design by optimally compensating the product deviations. To accomplish the goal, a predictive model is desirable to forecast the quality of a wide class o f product shapes, particularly considering the vast library of AM built products with complex geometry. Built upon our previous optimal compensation study of cylindrical products, this work aims at a novel statistical predictive modeling and compensation approach to predict and improve the quality of both cylindrical and prismatic parts. Ex perimental investigation and validation of polyhedrons a indicates the promise of predict ing and compensating a wide class of products built through 3D printing technology.
Graphene is an emerging nanomaterial for a wide variety of novel applications. Controlled synthesis of high quality graphene sheets requires analytical understanding of graphene growth kinetics. Graphene growth via chemical vapour deposition starts with randomly nucleated islands that gradually develop into complex shapes, grow in size and eventually connect together to form a graphene sheet. Models proposed for this stochastic process do not, in general, permit assessment of uncertainty. We develop a stochastic framework for the growth process and propose Bayesian inferential models, which account for the data collection mechanism and allow for uncertainty analyses, for learning about the kinetics from experimental data. Furthermore, we link the growth kinetics with controllable experimental factors, thus providing a framework for statistical design and analysis of future experiments.
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