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Group III nitrides are promising materials for light emitting diodes (LEDs). The occurrence of structural defects strongly affects the efficiency of these LEDs. We investigate the optical properties of basal plane stacking faults (BFSs), and the assignment of specific spectral features to distinct defect types by direct correlation of localized emission bands measured by cathodoluminescence in a scanning electron microscope with defects found in high resolution (scanning) transmission electron microscopy and electron beam induced current at identical sample spots. Thus, we are able to model the electronic structure of BSFs addressing I-1, I-2, and E type BSFs in GaN and AlGaN with low Al content. We find hints that BSFs in semipolar AlGaN layers cause local changes of the Al content, which strongly affects the usability of AlGaN as an electron blocking layer in nitride based LEDs.
Punching is a process that is sensitive to a multitude of parameters. The estimation of part and punch quality is often based on expert knowledge and trial-and-error methods, mostly carried out as a separate offline process analysis. In a previous study, we developed an optical inline monitoring system with subsequent image processing which showed promising results in terms of capturing every manufactured part, but was limited by slow image processing. Here, we present a more efficient image processing technique based on neural networks. For our approach, we manually identify the burnish parts in images based on criteria established via an expert survey in order to generate a training dataset. We then employ a combination of region-based and boundary-based losses to optimize the neural network towards a segmentation of the burnish surface which allows for an accurate measurement of the burnish height. The hyperparameter optimization is based on custom evaluation metrics that reflect the requirements of the burnish surface identification problem as well. After comparing different neural network architectures, we focus on optimizing the backbone of the UNet++ structure for our task. The promising results demonstrate that neural networks are indeed capable of an inline segmentation that can be used for measuring the burnish surface of punching parts.
Various flexibility options in power systems, such as storage, grid expansion, and demand flexibility, gain increasing importance to balance the intermittent power supply of renewables. On the demand side, especially the industrial sector represents promising potential for Demand Response, i.e., the alignment of its power demand with the current power supply of renewables. However, there exist various obstacles that currently prevent companies from investing in new or (fully) exploiting existing flexibility potentials. In this paper, we investigate how economic, regulatory, technological, organizational, behavioral, informational, and competence obstacles pose barriers for companies to adjust their power consumption flexibly. For this purpose, we combine both a structured literature analysis and a case study. For the case study, we conduct 16 interviews with energy experts from companies from different industries. Our findings reveal that due to technical risk of disrupting the production process, lacking revenues, and too low cost savings, companies do not flexibilize their power consumption. Moreover, in particular, contradictory legislative incentives and missing IT standardization and interoperability represent key obstacles. Therefore, our results constitute a basis for targeted policy making in order to foster the exploitation of (existing) flexibility potential of industrial companies on the demand side.
Executive Summary
Um die Ziele des Pariser Klimaabkommens zu erreichen, besteht rascher Handlungsbedarf in allen Sektoren. Dies gilt insbesondere für den Verkehrssektor, für den eine umfassende Transformation unumgänglich ist.
Aktuelle Forschungsarbeiten zeigen, dass neben der Elektrifizierung des motorisierten Individualverkehrs insbesondere der Ausbau und die Umsetzung alternativer Mobilitätsformen mit geringeren Treibhaus-gasemissionen (z.B. ÖPNV, Radfahren und Carsharing) aus Klimasicht zu bevorzugen und weiter voranzutreiben msind. Daher reicht es nicht aus, den derzeitigen Fahrzeugbestand mit Verbrennungsmotoren „einfach“ durch Fahrzeuge mit batterieelektrischem Antrieb zu ersetzen. Vielmehr müssen Wege gefunden werden, das Verhältnis von hergestellten Fahrzeugen und ihrer tatsächlichen Nutzung zu verbessern, um eine umfassende Reduktion der Treibhausgase im Bereich der individuellen Mobilität zu erreichen. Es ist daher notwendig, ein Bewusstsein für die Relevanz alternativer Mobilitätsformen (z.B. Mobilität-on-De-mand) zu schaffen und entsprechende Maßnahmen zum Ausbau dieser Mobilitätsformen zu ergreifen.
The growing share of renewable energy generation based on fluctuating wind and solar energy sources is increasingly challenging in terms of power grid stability. Industrial demand-side response presents a promising way to balance energy supply and consumption. For this, energy demand is flexibly adapted based on external incentives. Thus, companies can economically benefit and at the same time contribute to reducing greenhouse gas emissions. However, there are currently some major obstacles that impede industrial companies from taking part in the energy markets. A broad specification analysis systematically dismantles the existing barriers. On this foundation, a new end-to-end ecosystem of an energy synchronization platform is introduced. It consists of a business-individual company-side platform, where suitable services for energy-oriented manufacturing are offered. In addition, one market-side platform is established as a mediating service broker, which connects the companies to, e.g., third party service providers, energy suppliers, aggregators, and energy markets. The ecosystems aim at preventing vendor lock-in and providing a flexible solution, relying on open standards and offering an integrated solution through an end-to-end energy flexibility data model. In this article, the resulting functionalities are discussed and the remaining deficits outlined.
Industrial demand response uses a multitude of energy flexibility measures. Their planning and control requires various production IT systems. A widely accepted approach to classify these inhouse IT systems are the levels of the automation pyramid in companies. This paper broadens the scope of this concept to overcome the limitation to companies’ (virtual) borders by including required IT systems that refine and monetarize a company’s energy flexibility, e.g. energy markets, aggregators, etc. Therefore, a holistic approach for the classification of functionalities for industrial demand response across companies and energy markets is developed and applied exemplarily.