We report on the development of hypocycloid core-contour inhibited-coupling (IC) Kagome hollow-core photonic crystal fibers (HC-PCFs) with record transmission loss and spectral coverage that include the common industrial laser wavelengths. Using the scaling of the confinement loss with the core-contour negative curvature and the silica strut thickness, we fabricated an IC Kagome HC-PCF for Yb and Nd:Yag laser guidance with record loss level of 8.5 dB/km associated with a 225-nm-wide 3-dB bandwidth. A second HC-PCF is fabricated with reduced silica strut thickness while keeping the hypocycloid core contour. It exhibits a fundamental transmission window spanning down to the Ti:Sa spectral range and a loss figure of 30 dB/km at 750 nm. The fibers' modal properties and bending sensitivity show these HC-PCFs to be ideal for ultralow-loss, flexible, and robust laser beam delivery.
The capability of measuring melt pool variation is the key evaluating metal additive manufacturing quality. To measure the variation, a metrology architecture with in situ melt pool measurement and an estimation module is required. However, it is a challenge to effectively extract significant features from the huge data collected by the in situ metrology for quality estimation requirement. The purpose of this letter is to propose an intelligent metrology architecture with an in situ metrology (ISM) module and an enhanced automatic virtual metrology (AVM) system. The ISM module can extract the melt pool features with a coaxial camera and a pyrometer. On the other hand, the AVM system is improved with a feature selection method to solve the issue of limited samples in the component modeling quality. The examples with different metals are adopted to illustrate how the system works for estimating surface roughness and density of components, and, in the future, the system can even serve as the feedback signal for adaptive control of the process parameters by layering in an additive manufacturing system. Index Terms-Metal additive manufacturing, intelligent metrology architecture, in-situ metrology, melt pool, AVM. I. INTRODUCTION M ETAL additive manufacturing (AM) recently gains popularity due to its enormous benefits over traditional manufacturing, e.g., the flexibility in design, customized product on demand, and small-scale manufacturing. Metal AM utilizes 3D design to build a component directly, layer-by-layer, using the laser beam to melt the metal powder. Due to process complexity, condition variations, and hardware constraints of the layer Manuscript
The abilities to both monitor and control additive manufacturing (AM) processes in real-time are necessary before the routine production of quality AM parts will be possible. Currently, neither ability exist! The major reason is that AM processes are different from traditional manufacturing processes in many ways and so are the sensors and the monitoring data collected from them. In traditional manufacturing, that data is mostly numeric in nature. To that numeric data, AM monitoring data add large volumes of a variety of in situ, high-speed, image data. Collecting, fusing, and analyzing all that AM data and making the necessary control decisions is not possible using traditional, rigid, hierarchical-control architectures. Therefore, researchers are proposing to use real-time, machine-learning algorithms to analyze the data and to execute the other control functions. This paper identifies those control functions and proposes a new architecture to integrate them. This paper also shows an example of using that architecture to analyze the melt-pool, shape analysis using a clustering method.
Global Terrorist Dataset (GTD) is a vast collection of terrorist activities reported around the globe. The terrorism database incorporates more than 27,000 terrorism incidents from 1968 to 2014. Every record has spatial data, a period stamp, and a few different fields (e.g. strategies, weapon sorts, targets and wounds). There were few earlier studies to find interesting patterns from this textual gamut of data. The author believes that GTD has numerous interesting patterns still hidden and the full potential of this resource is still to be divulged. In this Independent Study, the author tries to investigate the GTD through co-clustering method for pattern discovery. Author has extracted textual data from GTD as per motivation to cluster the data in space and time simultaneously, through co-clustering. Co-clustering has become an important and powerful tool for data mining. By using co-clustering, bilateral data can be analysed by describing the connections between two different entities. There are many applications in the real world that can extensively benefits from this approach of co-clustering, such as market basket analysis and recommendation system. In this study, the effectiveness of coclustering model will be described by performing experiment on database of global terrorist events.
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