Survey on Additive Manufacturing, Cloud 3D Printing and Services

Cloud Manufacturing (CM) is the concept of using manufacturing resources in a service oriented way over the Internet. Recent developments in Additive Manufacturing (AM) are making it possible to utilise resources ad-hoc as replacement for traditional manufacturing resources in case of spontaneous problems in the established manufacturing processes. In order to be of use in these scenarios the AM resources must adhere to a strict principle of transparency and service composition in adherence to the Cloud Computing (CC) paradigm. With this review we provide an overview over CM, AM and relevant domains as well as present the historical development of scientific research in these fields, starting from 2002. Part of this work is also a meta-review on the domain to further detail its development and structure

A framework for large-scale relativistic simulations in the characteristic approach

We present a new computational framework (LEO), that enables us to carry out the very first large-scale, high-resolution computations in the context of the characteristic approach in numerical relativity. At the analytic level, our approach is based on a new implementation of the ``eth'' formalism, using a non-standard representation of the spin-raising and lowering angular operators in terms of non-conformal coordinates on the sphere; we couple this formalism to a partially first-order reduction (in the angular variables) of the Einstein equations. The numerical implementation of our approach supplies the basic building blocks for a highly parallel, easily extensible numerical code. We demonstrate the adaptability and excellent scaling of our numerical code by solving, within our numerical framework, for a scalar field minimally coupled to gravity (the Einstein-Klein-Gordon problem) in 3-dimensions. The nonlinear code is globally second-order convergent, and has been extensively tested using as reference a calibrated code with the same boundary-initial data and radial marching algorithm. In this context, we show how accurately we can follow quasi-normal mode ringing. In the linear regime, we show energy conservation for a number of initial data sets with varying angular structure. A striking result that arises in this context is the saturation of the flow of energy through the Schwarzschild radius. As a final calibration check we perform a large simulation with resolution never achieved before.Comment: RevTeX4, 22 pages, 21 figures, to appear in Phys. Rev.

An Application of Gaussian Process Modeling for High-order Accurate Adaptive Mesh Refinement Prolongation

We present a new polynomial-free prolongation scheme for Adaptive Mesh Refinement (AMR) simulations of compressible and incompressible computational fluid dynamics. The new method is constructed using a multi-dimensional kernel-based Gaussian Process (GP) prolongation model. The formulation for this scheme was inspired by the GP methods introduced by A. Reyes et al. (A New Class of High-Order Methods for Fluid Dynamics Simulation using Gaussian Process Modeling, Journal of Scientific Computing, 76 (2017), 443-480; A variable high-order shock-capturing finite difference method with GP-WENO, Journal of Computational Physics, 381 (2019), 189-217). In this paper, we extend the previous GP interpolations and reconstructions to a new GP-based AMR prolongation method that delivers a high-order accurate prolongation of data from coarse to fine grids on AMR grid hierarchies. In compressible flow simulations special care is necessary to handle shocks and discontinuities in a stable manner. To meet this, we utilize the shock handling strategy using the GP-based smoothness indicators developed in the previous GP work by A. Reyes et al. We demonstrate the efficacy of the GP-AMR method in a series of testsuite problems using the AMReX library, in which the GP-AMR method has been implemented

Design of an Advanced Layered Composite for Energy Dissipation using a 3D-Lattice of Micro Compliant Mechanism

This work introduces a new Advanced Layered Composite (ALC) design that redirects impact load through the action of a lattice of 3D printed micro-compliant mechanisms. The first layer directly comes in contact with the impacting body and its function is to prevent an intrusion of the impacting body and uniformly distribute the impact forces over a large area. This layer can be made from fiber woven composites imbibed in the polymer matrix or from metals. The third layer is to serve a purpose of establishing contact between the protective structure and body to be protected. It can be a cushioning material or a hard metal depending on the application. The second layer is a compliant buffer zone (CBZ) which is sandwiched between two other layers is responsible for the dampening of most of the impact energy. The compliant buffer zone, comprised by the lattice of micro-compliant mechanism, is designed using topology optimization to dynamically respond by distributing localized impact in the normal direction into a distributed load in the radial direction (perpendicular to the normal direction). The compliant buffer zone depicts a large radial deformation in the middle but not on the surface, which only moves in the normal direction. The effect is a significant reduction of the interfacial shear stress with two adjacent layered phases. A low interfacial shear stress translates into a reduced delamination. The ALC’s response to the impact is tested by using dynamic finite element analysis. The proposed ALC design is intended to be used for the design of protective devices such as helmets and crashworthy components in vehicle structures

Gaussian Process Modeling for Upsampling Algorithms With Applications in Computer Vision and Computational Fluid Dynamics

Across a variety of fields, interpolation algorithms have been used to upsample lowresolution or coarse data fields. In this work, novel Gaussian Process based methodsare employed to solve a variety of upsampling problems. Specifically threeapplications are explored: coarse data prolongation in Adaptive Mesh Refinement(AMR) in the field of Computational Fluid Dynamics, accurate document imageupsampling to enhance Optical Character Recognition (OCR) accuracy, and fastand accurate Single Image Super Resolution (SISR). For AMR, a new, efficient,and “3rd order accurate” algorithm called GP-AMR is presented. Next, a novel,non-zero mean, windowed GP model is generated to upsample low resolution documentimages to generate a higher OCR accuracy, when compared to the industrystandard. Finally, a hybrid GP convolutional neural network algorithm is used togenerate a computationally efficient and high quality SISR model

The 1st Advanced Manufacturing Student Conference (AMSC21) Chemnitz, Germany 15–16 July 2021

The Advanced Manufacturing Student Conference (AMSC) represents an educational format designed to foster the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings at the conference. The AMSC provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. Conference Proceedings of the conference will benefit readers by providing updates on critical topics and recent progress in the advanced manufacturing engineering and technologies and, at the same time, will aid the transfer of valuable knowledge to the next generation of academics and practitioners. *** The first AMSC Conference Proceeding (AMSC21) addressed the following topics: Advances in “classical” Manufacturing Technologies, Technology and Application of Additive Manufacturing, Digitalization of Industrial Production (Industry 4.0), Advances in the field of Cyber-Physical Systems, Virtual and Augmented Reality Technologies throughout the entire product Life Cycle, Human-machine-environment interaction and Management and life cycle assessment.:- Advances in “classical” Manufacturing Technologies - Technology and Application of Additive Manufacturing - Digitalization of Industrial Production (Industry 4.0) - Advances in the field of Cyber-Physical Systems - Virtual and Augmented Reality Technologies throughout the entire product Life Cycle - Human-machine-environment interaction - Management and life cycle assessmen

Applying the ADS-B Out to Facilitate Flight Data Analysis for General Aviation

The International Civil Aviation Organization (ICAO) and major airlines believe that flight data analysis is an effective approach to mitigate the risk of aviation accidents (International Civil Aviation Organization, 2010; International Air Transport Association, 2016). In the United States, flight data analysis is encouraged by the Federal Aviation Administration (FAA) through the flight operational quality assurance (FOQA) program. Among all aviation activities, general aviation (GA) has the highest accident rate (National Transportation Safety Board, 2014). However, implementation of flight data analysis for GA not only requires expensive investment on flight data recording devices, but also increases long-term labor cost due to regular data collection and data analysis. Automatic Dependent Surveillance Broadcast Out (ADS-B Out) is a precise satellite-based surveillance system that periodically broadcasts flight data retrieved from satellites and onboard avionics of the ADS-B Out capable aircraft. Based on the standard technical provisions of the ADS-B Out, the use of ADS-B data is expected to be a possible approach to facilitate the flight data analysis for general aviation. This research explored the use of ADS-B data to facilitate flight data analysis for general aviation. Researchers started the current study phase from analyzing the structure and content of the ADSB message by referring to the ICAO technical provisions (2008) and the operational performance standard of ADS-B from the Radio Technical Commission for Aeronautics (RTCA) (2009). Based upon the findings of the ADS-B data structure and content, a set of retrievable aircraft parameters was identified, and additional aircraft parameters were derived from the basic ADS-B information. Furthermore, sets of flight metrics were developed using the aircraft parameters broadcasted by ADS-B Out. The development of flight metrics was expected to be essential for measuring flight operational performance to support flight data analysis. In addition, exceedance detection was adopted to analyze the flight metrics in flight data analysis. ADS-B data were collected using an ADS-B receiver, and 40 sets of ADS-B data were selected to detect five operational exceedances of the Cirrus SR-20 aircraft of the Purdue Fleet. Exceedances were detected from the 40 sets of data. However, researchers noticed that the sparse ADS-B data caused by the low reception rate might affect the exceedance detection. Therefore, a preliminary analysis was conducted to investigate the difference of exceedance detection using ADS-B data with different reception rates. The results of analysis indicated that sparse ADS-B data could affect the detection of exceedances, but some exceedances might be less sensitive to the sparse data. Based on the findings of this research, recommendations were proposed for future studies