Characterization of Nanomaterials: Selected Papers from 6th Dresden Nanoanalysis Symposiumc

This Special Issue “Characterization of Nanomaterials” collects nine selected papers presented at the 6th Dresden Nanoanalysis Symposium, held at Fraunhofer Institute for Ceramic Technologies and Systems in Dresden, Germany, on 31 August 2018. Following the specific motto of this annual symposium “Materials challenges—Micro- and nanoscale characterization”, it covered various topics of nanoscale materials characterization along the whole value and innovation chain, from fundamental research up to industrial applications. The scope of this Special Issue is to provide an overview of the current status, recent developments and research activities in the field of nanoscale materials characterization, with a particular emphasis on future scenarios. Primarily, analytical techniques for the characterization of thin films and nanostructures are discussed, including modeling and simulation. We anticipate that this Special Issue will be accessible to a wide audience, as it explores not only methodical aspects of nanoscale materials characterization, but also materials synthesis, fabrication of devices and applications

Development, Implementation, and Validation of an Acoustic Emission-based Structural Health Monitoring System

Entwicklung, Implementierung und Validierung eines schallemissionsbasierten Strukturüberwachungssystems Die Strukturüberwachung eng. Structural Health Monitoring (SHM) ist ein grundlegender Prozess für die Kontrolle der Betriebssicherheit und Zuverlässigkeit von Strukturen und Bauteilen während des Betriebs. Ein Überwachungssystem soll die Strukturdegradation in einer frühen Phase erkennen und quantifizieren, um den Totalausfall zu verhindern und somit menschliche und finanzielle Verluste zu vermeiden. Mit der wachsenden Nachfrage nach kosteneffizienten und robusten Produkten ist SHM mit besonders hohen Anforderungen konfrontiert. Diese Arbeit befasst sich mit der Entwicklung, Implementierung und experimenteller Validierung eines innovativen SHM-Systems, das auf umfassende Weise Schädigungsmechanismen von unterschiedlichen Materialen erkennt, identifiziert und klassifiziert. Für in-situ-Strukturüberwachung können verschiedene Methoden angewendet werden. Hier wird die Schallemissionsanalyse eng. Acoustic Emission Technik (AET) eingesetzt. Acoustic Emission ist eine passive zerstörungsfreie Prüfund Überwachungsmethode. Sie basiert auf der Analyse elastischer Wellen, die durch freigesetzte Energie während mikrostrukturelle Änderungen wie z. B. Risse, Brüche, und Verschleiß entstehen. Unter Verwendung geeigneter Hardware und fortgeschrittener Signalverarbeitungsverfahren können diese Wellen kontinuierlich und in Echtzeit erfasst und analysiert werden. Die Leistungsfähigkeit und Zuverlässigkeit einer AE-basierten Schadensdiagnose sind stark abhängig von Material/Werkstoff, Konstruktion und möglichen Schadensszenarien. Der Fokus dieser Arbeit liegt daher auf der Entwicklung einer hocheffizienten und leicht anpassbaren Field Programmable Gate Array (FPGA)–basierten Messkette zum Abtasten und Erfassen der erzeugten AE-Signale. Neben der Verwendung von sehr leistungsfähiger Hardware ist eine zuverlässige Interpretation der AE Signale von zentraler Bedeutung. Deswegen erfordern die Entwicklung und Umsetzung von Multi-Level-Signalverarbeitungsansätzen und Mustererkennungsverfahren eine besondere Beachtung. Die experimentelle Validierung des entwickelten Systems erfolgt durch die Untersuchungen von drei verschiedenen Materialien/Strukturen: Verschleißfeste Metallbleche, Faserverbundwerkstoff Platten und elektrochemische Zelle. Aufgrund der Diversität der untersuchten Strukturen werden drei Verarbeitungsprozesse entwickelt. Die implementierten Algorithmen können AE-Signale erkennen, quantifizieren und qualifizieren, so dass AE-basierte Eigenschaften identifiziert und mit den entsprechenden AE-Quellen korreliert sind. Die Diagnose konzentriert sich hauptsächlich auf die Schadenserkennung (Merkmalsextraktion), Schadensabschätzung (Merkmalsauswahl) und Schadensklassifizierung unter Anwendung von Zeit-Frequenz-Analyse, statistischen Ansätzen und überwachten Klassifikationsverfahren. Die gewonnenen Ergebnisse zeigen eine bemerkbare Verbesserung der Identifizierung und Klassifizierung von Schadensmechanismen und beweisen die Effizienz des angewandten Multi-Level-Verarbeitungsansätze. Die vorgestellte Methodik ermöglicht eine automatisierte Zustandsüberwachung und stellt daher einen wichtigen Schritt in der Entwicklung von sicheren und zuverlässigen Strukturen dar.In engineering, Structural Health Monitoring (SHM) is an important field of study representing a fundamental process to control the longevity and reliability of structures during service. The objective of an SHM is to detect and quantify the structure degradation at an earlier stage. The acquisition of such information can contribute to prevention of total failure and hence avoiding human and financial losses becomes more possible. With the growing demands for cost-efficient and robust products, SHM is facing particularly high requirements. This thesis focuses on the development, implementation, and experimental validation of an innovative SHM system able to detect, identify, and classify in an extensive way damage mechanisms occurring in different materials. Several techniques can be applied for in situ health monitoring. In this work, Acoustic Emission Technique (AET) is used. Acoustic Emission is a passive nondestructive evaluation technique referring to the elastic waves generated by energy release during microstructural changes in the material. Those changes arise as a result of mechanical and environmental stresses. Monitoring of such a conversion can be continuously done in real-time using suitable hardware and advanced signal processing methods. The performance and reliability of an AE-based damage diagnosis approach are highly dependent on material, structure design and the damage scenarios. Therefore, a Field Programmable Gate Array (FPGA)-based measurement chains developed for sensing and acquiring the generated AE signals. This chain is easily adaptable to different structures and materials. It was therefore kept so far constant as possible throughout all tests conducted. Additionally to the use of highly efficient hardware that enhance the sensing quality and the data acquisition speed, the implementation of advanced filtering techniques with high processing accuracy is of central importance. The main objective of this thesis is to prove the function of the system developed to analyze AE waves under different damage scenarios. For this purpose, three different materials namely wear resistant plates, laminated composite plates, and electrochemical cells are investigated. Owing to the diversity of the studied materials, special attention is paid to the development and implementation of multilevel signal processing approach and pattern recognition methods. The processing chains are capable to detect, quantify and qualify the AE data, whereby AE-based characteristics are identified and correlated with the corresponding AE sources. The designed diagnosis methodology concentrates/focuses on damage detection (feature extraction), damage estimation (feature selection), and damage classification by using time-frequency analysis, multilevel statistical approaches, and supervised classification methods. The results obtained show a noticeable/remarkable enhancement of the identification and classification of damage mechanisms. The efficiency of applying multilevel processing approach is/(could be) thus proved. The methodology presented here, allows an automated structural health monitoring. Hereby, an important step forward in future development of safe and reliable structures is represented

Energy: A special bibliography with indexes, April 1974

This literature survey of special energy and energy related documents lists 1708 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1, 1968, and December 31, 1973. Citations from International Aerospace Abstracts (IAA) and Scientific and Technical Aerospace Reports (STAR) are grouped according to the following subject categories: energy systems; solar energy; primary energy sources; secondary energy sources; energy conversion; energy transport, transmission, and distribution; and energy storage. The index section includes the subject, personal author, corporate source, contract, report, and accession indexes

Energy: A continuing bibliography with indexes, issue 39

This bibliography lists 1377 reports, articles and other documents introduced into the NASA scientific and technical information system from July 1, 1983 through September 30, 1983

Tool wear monitoring in machining of stainless steel

monitoring systems for automated machines must be capable of operating on-line and interpret the working condition of machining process at a given point in time because it is an automated and unmanned system. But this has posed a challenge that lead to this research study. Generally, optimization of machining process can be categorized as minimization of tool wear, minimization of operating cost, maximization of process output and optimization of machine parameter. Tool wear is a complex phenomenon, capable of reducing surface quality, increases power consumption and increased reflection rate of machined parts. Tool wear has a direct effect on the quality of the surface finish for any given work-piece, dimensional precision and ultimately the cost of parts produced. Tool wear usually occur in combination with the principal wear mode which depends on cutting conditions, tool insert geometry, work piece and tool material. Therefore, there is a need to develop a continuous tool monitoring systems that would notify operator the state of tool to avoid tool failure or undesirable circumstances. Tool wear monitoring system for macro-milling has been studied using design and analysis of experiment (DOE) approach. Regression analysis, analysis of variance (ANOVA), Box Behnken and Response Surface Methodology (RSM). These analysis tools were used to model the tool wear. Hence, further investigations were carried out on the data acquired using signal processing and Neural networks frame work to validate the model. The effects of cutting parameters are evaluated and the optimal cutting conditions are determined. The interaction of cutting parameters is established to illustrate the intrinsic relationship between cutting parameters, tool wear and material removal rate. It was observed that when working with stainless steel 316, a maximum tool wear value of 0.29mm was achieved through optimization at low values of feed about 0.06mm/rev, speed of 4050mm/min and depth of cut about 2mm

Annual Report 2013 - Institute of Resource Ecology

The Institute of Resource Ecology (IRE) ISone of the eight institutes of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR). The Research activities are mainly integrated into the program “Nuclear Safety Research (NUSAFE)” of the Helmholtz Association (HGF) and focused on the topics “Safety of Nuclear Waste Disposal” and “Safety Research for Nuclear Reactors”. Additionally, various activities have been started investigating chemical and environmental aspects of processing and recycling of strategic metals, namely rare earth elements. These activities are located in the HGF program “Energy Efficiency, Materials and Resources (EMR)”. Both programs, and therefore all work which is done at IRE, belong to the research sector “Energy” of the HGF. The research objectives are the protection of humans and the environment from hazards caused by pollutants resulting from technical processes that produce energy and raw materials. Treating technology and ecology as a unity is the major scientific challenge in assuring the safety of technical processes and gaining their public acceptance. Namely, we investigate the ecological risks exerted by radioactive and non-radioactive metals in the context of nuclear waste disposal, the production of energy in nuclear power plants and in processes along the value chain of metalliferous raw materials. A common goal is to generate better understanding about the dominating processes essential for metal mobilization and immobilization on the molecular level. This in turn enables us to assess the macroscopic phenomena, including models, codes and data for predictive calculations, which determine the transport and distribution of contaminants in the environment