Current Projects

Internet marketplaces such as Amazon or eBay are dominating more and more areas of the retail trade as "digital checkpoints". One exception so far has been the trade with durable, highly varied and customizable consumer goods. The effect of the configurable product on the consumer's own four walls can only be checked after delivery. The ARBAY project brings the consultation and configuration of individualized goods into the living room. Virtual and augmented reality technologies are used in the project to create new digital distribution channels for highly varied goods. The aim is to develop a sales platform that extends the principle of known sales platforms for the sale of these goods. The main focus of the ISSE project is the development of a semantic product model as a basis for the platform.

Contact:
E-Mail: Dr. Christoph Knieke

Software ecosystems are complex system groups of interacting, distributed individual systems that require continuous, autonomous optimization. In DevOpt we understand an emergent, distributed system as a three layers architecture: Local IoT ecosystems negotiate their work configuration / resource usage under framework conditions. A control layer can correct local decisions through comprehensive, distributed optimization. A DevOps layer enables analysis, maintenance and further development through human intervention.

DevOpt aims at the development of controlled emergent systems through distributed modelling, local negotiation of device configurations / resource usage, increased development efficiency through model-based design, as well as combination of emergence and DevOps. The demonstration / evaluation takes place as electric grid scenarios using self-learning predictive SmartMeters. The ISSE will implement a component-based emergence framework for the local layer and environments, which enables local optimization and mutual dynamic use of resources. Furthermore, the emergent integration should be monitored and optimized with regard to functional and non-functional requirements.

Contact:
E-Mail: Mohamed Toufik Ailane
E-Mail: Mohammad Abboush

Weit e. V. has commissioned the Institute for Software and Systems Engineering to provide the V-Modell XT in the form of a public REST interface. After the fundamental revision of the V-Modell XT metamodel, an interface will be offered that is specified with OpenAPI and implemented in Java.

First software developments of Weit e. V. implementing the V-Modell XT REST API are the newly developed Web Assistant (formerly Project Assistant) and the Digital Projects App(https://dipa.online). The revision of this software ecosystem is intended to offer a simple and contemporary entry into project management with V-Modell XT.

In this context, there is research interest in the software life cycle as well as the software architecture with the background that there are already several editions in the form of metamodels and derivations for different user groups of the V-Modell.

Contact:

E-Mail: Dr. Christoph Knieke

The electronic products currently in use are designed for only short-lived use cycles and are contributors to the current increase in global waste volumes and excessive use of raw materials. [1] As a result, electrical and electronic products generate emissions that can be reduced through repair and reuse. However, emissions are also generated during the storage, sale and disposal of equipment. Particularly in view of global warming, these emissions represent a major potential for reduction. The aim of the project Life_TWIN is to investigate smart solutions for condition assessment and to identify opportunities for reuse, thereby reducing_{CO2 emissions}. The project proposal within the funding line "Resource Efficiency in the Context of the Energy Transition" within the 7th non-nuclear funding program of the German Federal Ministry of Economics and Climate Protection (BMWK) is thus in direct line with other research projects to pave the way for the Circular Economy. Research into solutions for improving the condition monitoring of products plays a central role in this, as it enables damage and material fatigue to products to be detected. Digital twins are to be used in the project, which will enable the condition of products to be recorded and controlled.

PROJECT TPARTNERS: IMW TU Clausthal, Bernhard Olbrich Elektroinstallationen-Industrieanlagen GmbH, Robert Bosch GmbH, Hellmann Process Management GmbH & Co. KG

Contact:
E-Mail: Dominique Briechle

[1] V. Forti, C. P. Balde, R. Kuehr, and G. Bel, "The Global E-waste Monitor 2020: Quantities, flows and the circular economy potential," 2020.

Projectwebsite

The aim of the project Recycling 4.0 is to improve the recycling process through a targeted and controlled exchange of information. All players, from raw material producers to OEMs and recyclers, are to be networked with each other and form a circular economy. The focus of our subproject is on the development of an information and data marketplace where the different actors can network to exchange information.

The project video "Digitization as the key to the Advanced Circular Economy".

Due to the advancing expansion of electromobility, more and more lithium-ion batteries are being sold. The scarce raw materials they contain, such as lithium and cobalt, make efficient recycling essential. This presents manufacturers and recyclers with new challenges in terms of technology and available information. The ERDF innovation alliance "Recycling 4.0" is looking at possible applications of digitalization in recycling to meet the challenges posed by increasingly complex products and a lack of information. The focus is on the extraction, dissemination and use of information through Industry 4.0 along the entire value chain. To this end, information dissemination will be enabled via an information marketplace as well as direct sharing between actors. The additional information will be used to increase efficiency in recycling, close material cycles, recover critical raw materials and enable decision support. The project thus contributes to sustainable e-mobility, enables resource conservation and reduces Germany's dependence on raw materials.

Click here for the project video:https://video.tu-clausthal.de/film/recycling-4-0_1170.html

Contact:
E-Mail: Marit Mathiszig

Environmental perception systems of autonomous vehicles are nowdays using extensive AI-based algorithms. Established techniques and methods to proof correctness regarding safety are reaching its limits. Even if a lot of driving scenarios and millions of kilometers are used for testing, an overall safety assurance during design time is not possible. The goal for the project SafeWahr is to duly detect violations of safety critical specifications and uncertainities of AI-based environmental perception systems of autonomous vehicles. In case a violation was detected the autonomous vehicle will then continue its driving task with restricted functionality in a so called fail-operational mode.

One approach for handling situations, which are not known during design time is to partially shift the safety assurance to the operating time. Ultimately, some kind of "operation time certification" is aimed. For this purpose an operation time monitoring architecture for self-diagnosis will be developed in SafeWahr. Within this operation time monitoring architecture three types of monitors will be included: (1) A Situation Monitor, which determines if the current situation was considered during design time, (2) a Validity Monitor, which determines if the AI-based perception is safe regarding its results and (3) a Function Monitor, which determines if the target function acts corretly regarding safety specifications.

Contact person:
E-Mail: Iqra Aslam

Today’s increasing logistic operations constitute increasing challenges for traffic in urban areas. In addition to traffic congestion caused by logistic operations, safety problems are also getting increasingly critical. Aiming to tackle these challenges, the LogiSmile partners are piloting a fully autonomous delivery system in different pilot cities: Esplugues de Llobregat, Spain, Hamburg in Germany, and Debrecen in Hungary. This autonomous delivery system is realized based on the cooperation between an autonomous hub vehicle (AHV) and smaller robots, which are autonomous last-mile delivery devices (ADD). Furthermore, an additional remote back-end control center for managing the communication between the AHV and the ADD, acquiring the data while operating, and optimizing the fleet operations with efficient routing algorithms. As an NFF member, ISSE participates in the development of the remote back-end control center, aiming to provide fail-operational solutions in the case of critical situations that cannot be handled by the AHV autonomously.

Contact:
E-Mail: Adina Aniculaesei

Project-Website (incl. Live Stream): Link

The MushR project aims to close the gap between the usage of digital tools and modular, adaptable micro-farming and agricultural systems. It introduces four innovative novelties extending the state of the art of gourmet mushroom production, facilitating process automation, more sustainable mushroom provision, increased yield and improved quality of harvested mushrooms. The project proposes a modular and scalable gourmet mushroom growing and harvesting system using an image recognition setup that detects when and which mushrooms are ready to be harvested in combination with an automated mushroom harvesting mechanism for harvesting the mushrooms.

The mushrooms are grown in a sensor-array controlled growing chamber similar to traditional industrial mushroom farms. However, instead of using non-reusable plastic bags, the mushrooms are grown in reusable mushroom pods, which are linked to a digital twin. The growth status and the health of the mushrooms are regularly checked via cameras. The camera input is processed by a YOLO model to detect whether the mushrooms are ready to be harvested or not. The later integrated automated harvesting system will then be activated towards carrying out its task. Finally, the mushrooms are ready for consumption, and the mushroom pods remain in the growing chamber.

Contact:
Prof. Dr. Benjamin Leiding benjamin.leiding@tu-clausthal.de
 

Project-Website: Link

In research, teaching, and technology transfer, the overarching theme for the Clausthal University of Technology, its guiding principle, is the Circular Economy. While degree-specific courses deal with selective aspects of the Circular Economy, there is a gap with respect to a course for all students that teaches the essential concepts and fundamentals of sustainability, environmental pollution, resource scarcity, and the Circular Economy.
The course “The Limits to Growth – Sustainability and the Circular Economy” aims to close this gap. As a general foundation course, there are no access restrictions. Furthermore, the course can be taken by Bachelor and Master students and PhD students. In order to accommodate a large number of international students, the course is conducted in English.

The Limits to Growth (LTG) course is open to all students of the Clausthal University of Technology as well as other organizations. Therefore, an adapted teaching format and appropriate technical support are required. The LTG course will be implemented as a Massive Open Online Course (MOOC), which scales traditional forms of knowledge transfer (slides, recordings, etc.) via an IT-driven infrastructure and expands them with additional technical features such as forums, quizzes and other forms of (semi-) automated tasks or tasks to be assessed via peer feedback. In addition, corresponding MOOC platforms offer participants interaction and networking opportunities and the possibility of asynchronous learning, in which students study according to their schedule. They are given access to all teaching and learning materials, which are discussed in regular meetings – followed by optional question-and-answer sessions to clarify any questions.

All teaching and learning materials, recordings, homework, tutorials, etc. are published under an open-source licence (CC-BY-SA-4.0) and are thus freely available as directly integrable teaching units or as a basis for further courses.

Contact:
Prof. Dr. Benjamin Leiding benjamin.leiding@tu-clausthal.de
 

Der Einsatz von KI soll dazu dienen, die spezifischen Kosten der Ölförderung zu senken, ohne Kompromisse in der Sicherheit, des Umweltschutzes oder der Integrität einzugehen. Die Studie soll vor diesem Hintergrund aufzeigen, welche Einsatzbereiche für KI in einem maturen Ölfeld bestehen und wie diese wirtschaftlich zu bewerten sind. Die Einsatzmöglichkeiten zur Mehrwertbestimmung künstlicher Intelligenz im maturen Ölfeld sollen in einer Literaturstudie durchgeführt werden. Betrachtungsgegenstand ist hierbei der abgegrenzte Bereich von der Sonde selbst (inkl. Pumpe) bis zur Übergabestation inkl. Pipelinesystem. Hierbei werden die Einsatzmöglichkeiten und Potentiale von KI-Techniken aus den Bereichen Knowledge Discovery in Databases, Predictive Maintenance, Zeitreihenprognose, Modellbildung, modellbasierten und modellfreien Optimalsteuerungen und -regelungen untersucht und beschrieben.

Ansprechpartner: Stefan Wittek

This project is part of the CircularLIB research training group on the cycle-oriented production of lithium-ion batteries. In this program the three universities TU Braunschweig, TU Clausthal and Leibnitz Universität Hannover are cooperating with the Fraunhofer Institute for Surface Engineering and Thin Films IST.

The aim is to research a hybrid AI-based modeling approach for lithium-ion batteries. Recently, machine learning has also been increasingly used in the context of classical engineering disciplines. In particular, deep learning approaches provide fast and relatively accurate modeling approaches based on learning specific tasks from examples. This is of crucial interest especially in the context of high-dimensional complex systems, where the underlying physics is not fully known or the computational effort for simulation with conventional numerical methods is high. Another aspect driving the use of deep learning approaches is the need for fast models that can be used in iterative tasks such as optimization and control. However, data-driven models are typically black-box approaches that are developed based only on data and do not explicitly incorporate physical knowledge or constraints into model development. This can result in such models lacking robustness and accuracy, especially with limited training data.

Recent advances in physically-informed machine learning have led to a number of approaches that are well suited for solving various types of partial differential equations (PDEs). This project aims to develop models based on both data and available physical knowledge for lithium-ion batteries using physically informed machine learning techniques that allow for a hybrid AI-based modeling approach.

Contact: Hamidreza Eivazi Kourabbaslou