Meniscus-Guided Micro-Printing of Prussian Blue for Smart Electrochromic Display

Using energy-saving electrochromic (EC) displays in smart devices for augmented reality makes cost-effective, easily producible, and efficiently operable devices for specific applications possible. Prussian blue (PB) is a metal-organic coordinated compound with unique EC properties that limit EC display applications due to the difficulty in PB micro-patterning. This work presents a novel micro-printing strategy for PB patterns using localized crystallization of FeFe(CN)(6) on a substrate confined by the acidic-ferric-ferricyanide ink meniscus, followed by thermal reduction at 120 degrees C, thereby forming PB. Uniform PB patterns can be obtained by manipulating printing parameters, such as the concentration of FeCl3 center dot K3Fe(CN)(6), printing speed, and pipette inner diameter. Using a 0.1 M KCl (pH 4) electrolyte, the printed PB pattern is consistently and reversibly converted to Prussian white (CV potential range: -0.2-0.5 V) with 200 CV cycles. The PB-based EC display with a navigation function integrated into a smart contact lens is able to display directions to a destination to a user by receiving GPS coordinates in real time. This facile method for forming PB micro-patterns could be used for advanced EC displays and various functional devices

Advanced thermal fluid leakage detection system with machine learning algorithm for pipe-in-pipe structure

Pipe-in-pipe (PIP) system is essential for high thermal and high pressure fluid transportation. However, in the existing PIP systems, fluid leakage between inner and outer pipe has been difficult to discover or detect, which has worked as bottle neck to utilize PIP system in high risk industries as nuclear reactor, chemical plant or oil drilling systems. Here, we propose a noble PIP leakage detection system utilizing distributed temperature sensing (DTS) with Machine Learning (ML). With the Fourier transformed spectrogram data from DTS, the ML assisted system was able to detect 0.2 similar to 7 ml/min liquid leakage between inner and outer pipe with the accuracy of 91.67% with a single embedded optical fiber. Under varying operating temperature, the system successfully distinguished leakage and non-leakage states using the optimized convolutional neural network. Our developed PIP leakage detection system can be deployed in safety-critical industrial systems for autonomous leakage detection

Transfer learning-based ensemble convolutional neural network for accelerated diagnosis of foot fractures

The complex shape of the foot, consisting of 26 bones, variable ligaments, tendons, and muscles leads to misdiagnosis of foot fractures. Despite the introduction of artificial intelligence (AI) to diagnose fractures, the accuracy of foot fracture diagnosis is lower than that of conventional methods. We developed an AI assistant system that assists with consistent diagnosis and helps interns or non-experts improve their diagnosis of foot fractures, and compared the effectiveness of the AI assistance on various groups with different proficiency. Contrast-limited adaptive histogram equalization was used to improve the visibility of original radiographs and data augmentation was applied to prevent overfitting. Preprocessed radiographs were fed to an ensemble model of a transfer learning-based convolutional neural network (CNN) that was developed for foot fracture detection with three models: InceptionResNetV2, MobilenetV1, and ResNet152V2. After training the model, score class activation mapping was applied to visualize the fracture based on the model prediction. The prediction result was evaluated by the receiver operating characteristic (ROC) curve and its area under the curve (AUC), and the F1-Score. Regarding the test set, the ensemble model exhibited better classification ability (F1-Score: 0.837, AUC: 0.95, Accuracy: 86.1%) than other single models that showed an accuracy of 82.4%. With AI assistance for the orthopedic fellow, resident, intern, and student group, the accuracy of each group improved by 3.75%, 7.25%, 6.25%, and 7% respectively and diagnosis time was reduced by 21.9%, 14.7%, 24.4%, and 34.6% respectively

Selective Laser Melting Process for Sensor Embedding into SUS316L with Heat Dissipative Inner Cavity Design

Artificial intelligence and Internet of Things (IoT) technology, which are the core of the 4th industrial revolution, can resolve many problems that optimization of production times in the manufacturing process and reduction of materials required etc. In order to utilize the 4th industrial revolution technology, real-time monitoring technology of metal parts is essential, so technology for embedding sensors and IC chips into parts is essential. Using metal 3d printing technology, it is possible to embed IC chips into metal parts, which was impossible because of the existing high-temperature metal manufacturing process of casting or forging. Here we introduce a novel new method for sensor embedding into SUS316L by hemisphere design to avoid direct laser exposure onto sensors during selective laser melting process. Thermal and microstructural analysis was carried out to characterize the property of inner hemisphere for safe thermal couple embedding into SUS316L

High strength aluminum alloys design via explainable artificial intelligence

Here, we have approached to discover new aluminum (Al) alloys with the assistance of artificial intelligence (A.I.) for the enhanced mechanical property. A high prediction rate of 7xxx series Al alloy was achieved via the Bayesian hyperparameter optimization algorithm. With the guide of A.I.-based recommendation algorithm, new Al alloys were designed that had an excellent combination of strength and ductility with a yield strength (YS) of 712 MPa and elongation (EL) of 19%, exhibiting a homogeneous distribution of nanoscale precipitates hindering dislocation movement during deformation. Adding Mg and Cu was found to be the critical factor that decides the relative ratio of strength and EL. We also demonstrate an explainable A.I. (XAI) system that reveals the relationship between input and output parameters. Our A.I. assistant system can accelerate the search for high-strength Al alloys for both experts and non-experts in the field of Al alloy design. (c) 2022 Published by Elsevier B.V

Laser powder bed fusion for AI assisted digital metal components

This paper proposes a novel method to impart intelligence to metal parts using additive manufacturing. A sensor-embedded metal bracket is prototyped via a metal powder bed fusion process to recognise partial screw loosening or total screw missing or identify the source of vibration with the assistance of artificial intelligence (AI). The digital metal bracket can recognise subtle changes in the screw fixation state with 90% accuracy and identify unknown sources of vibration with 84% accuracy. The von Mises stress distribution in the prototyped metal bracket is evaluated using a finite element analysis, which is learned by AI to match the real-time deformation analysis of the metal bracket in augmented reality. The proposed prototype can contribute to hyper-connectivity for developing next-generation metal-based mechanical components.11Nsciescopu