No abstract

One major impediment to wider adoption of additive manufacturing (AM) is the presence of larger-than-expected shape deviations between an actual print and the intended design. Since large shape deviations/deformations lead to costly scrap and rework, effective learning from previous prints is critical to improve build accuracy of new products for cost reduction. However, products to be built often differ from the past, posing a significant challenge to achieving learning efficacy. The fundamental issue is how to learn a predictive model from a small set of training shapes to predict the accuracy of a new object. Recently an emerging body of work has attempted to generate parametric models through statistical learning to predict and compensate for shape deviations in AM. However, generating such models for 3D freeform shapes currently requires extensive human intervention. This work takes a completely different path by establishing a nonparametric, random forest model through learning from a small training set. One novelty of this approach is to extract features from training shapes/products represented by triangular meshes, as opposed to point-cloud forms. This facilitates fast generation of predictive models for 3D freeform shapes with little human intervention in model specification. A real case study for a fused deposition modeling (FDM) process is conducted to validate model predictions. A practical compensation procedure based on the learned random forest model is also tested for a new part. The overall shape deviation is reduced by 44%, which shows a promising prospect for improving AM print accuracy.

In this paper, we propose an enhanced SEIRD (Susceptible-Exposed-Infectious-Recovered-Death) model with time varying case fatality and transmission rates for confirmed cases and deaths from COVID-19. We show that when case fatalities and transmission rates are represented by simple Sigmoid functions, historical cases and fatalities can be fit with a relative-root-mean-squared-error accuracy on the order of 2% for most American states over the period from initial cases to July 20 (2020). We find that the model is most accurate for states that so far had not shown signs of multiple waves of the disease (such as New York), and least accurate for states where transmission rates increased after initially declining (such as Hawaii). For such states, we propose an alternate multi-phase model. Both the base model and multi-phase model provide a way to explain historical reported cases and deaths with a small set of parameters, which in the future can enable analyses of uncertainty and variations in disease progression across regions.

We analyze the progression of COVID-19 in the United States over a nearly one-year period beginning March 1, 2020 with a novel metric motivated by queueing models, tracking partial-average day-of-event and cumulative probability distributions for events, where events are points in time when new cases and new deaths are reported. The partial average represents the average day of all events preceding a point of time, and is an indicator as to whether the pandemic is accelerating or decelerating in the context of the entire history of the pandemic. The measure supplements traditional metrics, and also enables direct comparisons of case and death histories on a common scale. We also compare methods for estimating actual infections and deaths to assess the timing and dynamics of the pandemic by location. Three example states are graphically compared as functions of date, as well as Hong Kong as an example that experienced a pronounced recent wave of the pandemic. In addition, statistics are compared for all 50 states. Over the period studied, average case day and average death day varied by two to five months among the 50 states, depending on data source, with the earliest averages in New York and surrounding states, as well as Louisiana. Supplementary Information The online version contains supplementary material available at 10.1007/s10729-022-09619-y.

With the development of smart power grids, smart electricity meters are widely applied to industrial production and the life. For interests of the power supply and consumption parties, electricity meters are required to have high quality, precision and consistency. As temperatures vary considerably in south and north of China and electricity meters work in complex temperature environments, studies on the effects of temperature on power metering are of significant importance. In this paper, an optimization method considering the effects of temperature is proposed for the consistency of electricity meters. The precision consistency of electricity meters is optimized in full temperature range through means such as electrothermal coupling simulation, orthogonal test, cost calculation, etc., and the design scheme of electricity meters with maximum cost performance can be delivered for their manufacturers.