Grow Local Manufacturing along US/Mexico Border Region for an Integrated Supply Chain in the Post COVID-19 Era

The current coronavirus disease pandemic, plus the strong movement of manufacturing reshoring, provide a unique opportunity for many US regions to grow local manufacturers. This technical note attempts to review the current situation and trend in US manufacturing. We then discuss challenges and necessary steps, such as asset mapping, required to grow local suppliers. Suggestions are then made to support growing local suppliers along the US/Mexico border region

A Framework of Dynamic Data Driven Digital Twin for Complex Engineering Products: the Example of Aircraft Engine Health Management

Digital twin is a vital enabling technology for smart manufacturing in the era of Industry 4.0. Digital twin effectively replicates its physical asset enabling easy visualization, smart decision-making and cognitive capability in the system. In this paper, a framework of dynamic data driven digital twin for complex engineering products was proposed. To illustrate the proposed framework, an example of health management on aircraft engines was studied. This framework models the digital twin by extracting information from the various sensors and Industry Internet of Things (IIoT) monitoring the remaining useful life (RUL) of an engine in both cyber and physical domains. Then, with sensor measurements selected from linear degradation models, a long short-term memory (LSTM) neural network is proposed to dynamically update the digital twin, which can estimate the most up-to-date RUL of the physical aircraft engine. Through comparison with other machine learning algorithms, including similarity based linear regression and feed forward neural network, on RUL modelling, this LSTM based dynamical data driven digital twin provides a promising tool to accurately replicate the health status of aircraft engines. This digital twin based RUL technique can also be extended for health management and remote operation of manufacturing systems

Zinc ferrite based gas sensors: A review

Flammable, explosive and toxic gases, such as hydrogen, hydrogen sulfide and volatile organic compounds vapor, are major threats to the ecological environment safety and human health. Among the available technologies, gas sensing is a vital component, and has been widely studied in literature for early detection and warning. As a metal oxide semiconductor, zinc ferrite (ZnFe2O4) represents a kind of promising gas sensing material with a spinel structure, which also shows a fine gas sensing performance to reducing gases. Due to its great potentials and widespread applications, this article is intended to provide a review on the latest development in zinc ferrite based gas sensors. We first discuss the general gas sensing mechanism of ZnFe2O4 sensor. This is followed by a review of the recent progress about zinc ferrite based gas sensors from several aspects: different micro-morphology, element doping and heterostructure materials. In the end, we propose that combining ZnFe2O4 which provides unique microstructure (such as the multi-layer porous shells hollow structure), with the semiconductors such as graphene, which provide excellent physical properties. It is expected that the mentioned composites contribute to improving selectivity, long-term stability, and other sensing performance of sensors at room or low temperature

Study of the graphene energy absorbing layer and the viscosity of sodium alginate in Laser-Induced- Forward-Transfer (LIFT) bioprinting

Laser induced forward transfer (LIFT) bioprinting has been viewed as a new and actively developed three-dimensional bioprinting technology due to its high accuracy and good cell viability. The printing quality is highly dependent on the jet formation and its stability in the LIFT bioprinting process. The objective of this study is to investigate the effect of a graphene Energy Absorbing Layer (EAL) and alginate hydrogel (SA) (w.t. 1% and 2%) viscosity on jet generation in the LIFT bioprinting process. Since SA exhibits a shear-thinning behavior, it is a non-Newtonian fluid. The effect of EAL thickness and SA’s viscosity were addressed for various experimental conditions. After the laser irradiated on the quartz substrate, small holes caused by laser interaction appeared in the interaction area on the EAL. The EAL substrate with multiple holes was examined using an optical microscope, and the morphology of holes was observed and compared. The images of jet generation showed that graphene EAL can assist in the transfer of SA with a low laser energy absorption rate. The viscosity of the SA also plays a significant role in the generation of a stable jet for SA transfer. For the cases with the same laser energy input, the jet generated using higher viscosity bioink had a smaller initial velocity, which eventually led to a shorter jet length. The findings in this study will facilitate the development of new EAL in LIFT bioprinting

AUC-mixup: Deep AUC Maximization with Mixup

While deep AUC maximization (DAM) has shown remarkable success on imbalanced medical tasks, e.g., chest X-rays classification and skin lesions classification, it could suffer from severe overfitting when applied to small datasets due to its aggressive nature of pushing prediction scores of positive data away from that of negative data. This paper studies how to improve generalization of DAM by mixup data augmentation -- an approach that is widely used for improving generalization of the cross-entropy loss based deep learning methods. %For overfitting issues arising from limited data, the common approach is to employ mixup data augmentation to boost the models' generalization performance by enriching the training data. However, AUC is defined over positive and negative pairs, which makes it challenging to incorporate mixup data augmentation into DAM algorithms. To tackle this challenge, we employ the AUC margin loss and incorporate soft labels into the formulation to effectively learn from data generated by mixup augmentation, which is referred to as the AUC-mixup loss. Our experimental results demonstrate the effectiveness of the proposed AUC-mixup methods on imbalanced benchmark and medical image datasets compared to standard DAM training methods.Comment: 3 pages, 4 figure

Towards a reliable prediction of the infrared spectra of cosmic fullerenes and their derivatives in the JWST era

Fullerenes, including C60, C70, and C60+, are widespread in space through their characteristic infrared vibrational features (C60+ also reveals its presence in the interstellar medium through its electronic transitions) and offer great insights into the carbon chemistry and stellar evolution. The potential existence of fullerene-related species in space has long been speculated and recently put forward by a set of laboratory experiments of C60+, C60H+, C60O+, C60OH+, C70H+, and [C60-Metal]+ complexes. The advent of the James Webb Space Telescope (JWST) provides a unique opportunity to search for these fullerene-related species in space. To facilitate JWST search, analysis, and interpretation, an accurate knowledge of their vibrational properties is essential. Here, we compile a VibFullerene database and conduct a systematic theoretical study on those species. We derive a set of range-specific scaling factors for vibrational frequencies, to account for the deficiency of density functional theory calculations in predicting the accurate frequencies. Scaling factors with low root-mean-square and median errors for the frequencies are obtained, and their performance is evaluated, from which the best-performing methods are recommended for calculating the infrared spectra of fullerene derivatives which balance the accuracy and computational cost. Finally, the recommended vibrational frequencies and intensities of fullerene derivatives are presented for future JWST detection.Comment: 19 pages, 8 figures, 5 tables. Accepted for publication in MNRA

Synthesizing and Printing of Tin Oxide Nanoparticles Using a Single Ultrafast Laser System: A Feasibility Study

In laser-based manufacturing, processing setup customization is one of the popular approaches used to enhance diversity in material processing using a single laser. In this study, we propose setup design modification of an ultrafast laser system to demonstrate both Tin Oxide (SnO2) nanoparticle synthesis from bulk metal, and post printing of said nanoparticles using Laser Induced Forward Transfer (LIFT) method. Using the Pulse Laser Ablation in Liquid (PLA-L) method, nanoparticles were synthesized from a bulk tin metal cube submerged in distilled water. Such nanoparticles dispersed in water can form colloidal ink that can be used for different printed electronics applications. Pulse energy was varied to investigate the influence on morphological properties of the nanoparticles. It was observed that a decrease in average particle size, and an increase in the number of particles synthesized occurred as the pulse energy was increased. In our study, we adapted the same laser system to enable LIFT operation for printing of the SnO2 nanoparticles. The colloidal ink prepared was then used in LIFT method to study feasibility of printing the synthesized nanoparticles. By varying not only the laser parameters but process parameters such as coating thickness and drying time, printed results can be improved. Experimental results show great potential for both synthesizing and printing of the nanoparticles using a single laser system. This study serves as a proof of concept that a single laser system can turn bulk metal into nanoparticles-based applications without the need for extra processing from other machines/systems, opening the door to highly customizable prints with reduced lead times

Application of optimized laser surface re-melting process on selective laser melted 316L stainless steel inclined parts

Lower surface quality of selective laser melting (SLM) manufactured parts remains to be a key shortcoming particularly for high performance functional components. In this paper, the authors utilized Box–Behnken methodology to explore the effect of laser surface re-melting process parameters. The process parameters are:laser power, laser exposure time, laser point distance, and shell layer thickness. The experiments were conducted using Renishaw AM-250 machine. SLM manufactured parts with inclination of 45˚ up-skin were treated with a given surface roughness using laser surface re-melting (LSR). The optimization of process parameters was conducted using response surface methodology and the validation tests was carried out utilizing the determined input parameters. The results verified the effectiveness of the integrated approach and the proposed statistical model. The outcomes of this study demonstrated that selective laser melting process followed by the laser surface re-melting process is very likely to become a fast and economic integrated method for improving the inclined surface quality of SLM manufactured parts