Ausgeschriebene Forschungsthemen

Thesis Committee:
Prof. Riedel (ISW)

Problem to be investigated:
The Digital Twins of product and production face the challenge that once the development process is closed, they do not reflect the real status of production where events as inaccuracy in the product, equipment failures, poor quality or missing compound parts happen continuously. For achieving production resilience a holistic methodology, combining a top-down with a bottom-up approach for capturing real-time product and production parameters through enabling technologies, as 3D-scanning, intelligent sensors, and then embedding them in Digital Twins should be developed. This capturing and embedding process will result in the development of such called "cognitive Digital Twins". Nevertheless, these Digital Twins often rely on history data which can consist of inaccuracies experience. Additionally, the PhD research project is focusing on enabling resilience in Digital Twins through developing a machine learning-based approach for the Digital Twin to self-learn if there is an inaccuracy in the Digital Twin, and to correct the inaccuracy by bringing the Digital Twin to the accurate state. A motivation scenario for the further validation in an innovative set-up of an automated measurement cell, where state-of-the-art robotics technologies, e.g. stationery, collaborative, mobile components, integrated with 3D laser scanning, intelligent sensors, e.g. temperature, pressure, velocity in three axes, represents the core of demonstration activities. Additionally to the discrete-manufacturing scenario in the measurement cell, the realisation and validation of the multi-layer carbon fiber printing process represent the second demonstrator.

Relevance of the research topic:
In order to implement the Cognitive Digital Twins in the operational manufacturing environment, the project approaches a bottom-up procedure, addressing the theme in two critical manufacturing areas: 1) the product quality assurance in discrete manufacturing, exemplarily for modular production in the automotive industry and 2) the process quality assurance in continuously manufacturing, exemplarily for monitoring and optimising the multi-layer carbon fiber printing process, for the aerospace industry. Both applications face the challenge of giving life in at least near-real-time to the Digital Models of physical manufacturing entities from the factory shop floor, e.g. parts, components, equipment, tools, devices, human workers. The validation of the developed generic approach and methodology for two specific quality assessment scenarios of product and process in selected industries will be instantiated for other production domains and industries. The development of a generic approach for Real-Time Digital Twins in manufacturing is followed by developing a Road Map for migration of this generic approach in other industries and use cases, e.g. machine tool/equipment industry and processes, e.g. logistics, machining, etc.

Scientific objectives:
To conceive, develop and validate the Cognitive Digital Twins aiming at supporting the realisation of resilient production/factory the following scientific and technical objectives have been established: Objective #1: Design and development of the Reference Models for Resilient production. In product, process and factory planning, reference models exist for the product, process and production life cycle in which resilience aspects have so far been insufficiently taken into account. The aim of objective #1 is to find out how the existing reference models can be enhanced to include resilience indicators and that enhenced reference models are available for the addressed use cases. This will make it possible to evaluate and optimise production holistically from the point of view of resilience feature/characteristics. Additionally, specific KPIs for measuring the performance of the process optimisation and resilience achievement will be developed. Objective #2: Methodology for the implementation of the Reference Model in a Cognitive Digital Twin and a virtual engineering environment. The Reference Model form the basis for the enhancement and implementation of a newly designed engineering environment based on state-of-the-art digital manufacturing technologies, e.g. Siemens, Dassault Systems. This new engineering environment has to feature the following characteristics: open, expandable, service-based and safety-oriented. The Digital Twins of all factory objects are extended by the captured context from the real-time shop floor; supported 3D scanning, wireless intelligent sensor and digital manufacturing technologies. The achieved cognitive status of digital twins enables the realisation of resilience as a balance between robustness and flexibility. Objective #3: Design and development of a Cognitive Digital Twin-centered learning assistance system towards resilient Production. The aim is to develop a learning and context-aware assistance system as the main enabler for achieving resilient production. The process flow of this new system starts with the creation of the Digital Twins of all factory objects; capturing real-time data from the shop floor, adding cognition to the digital twin, based on the developed methodology in Objective #2; analysing the current data with history data based on AI and deep learning algorithms; elaborating and documenting actions for resilient processes and for supporting user decision-making. Objective #4: Development of an approach to self-learn if there is a deviation from accurate asset/process representation in the Digital Twin. It is achieved through the development of a probabilistic risk-based approach to identify where the deviation in the accuracy originates and to automatically understand its data and model sources. Additionally, a machine learning-based approach to self-adapt the digital twin to increase its accuracy of representativeness and a simulation toolkit with machine learning features to optimise the accuracy of the Digital Twin should be developed. Objective #5: Validation, incremental improvement and Roadmaps for migration of the generic approach and methodology for other manufacturing processes and industries. The achievement of production resilience and the resilience od Digital Twins and the process optimisation in the two developed use cases will be performed based on the identified KPIs in Objective #1. A validation test-bed, scientifically founded will be elaborated. Additionally, the employment of the concept of Cognitive Digital Twins in other manufacturing processes and industries will be developed, as well.

Thesis Committee:
Prof. Mehring (IMVT)

Problem to be investigated:

Relevance of the research topic:

Scientific objectives:

Thesis Committee:
Prof. Graf (IFSW), Prof. Rademacher (INT)

Problem to be investigated:
Hollow-core fibers have been attracting increasing attention over the past decade for a wide range of applications, from telecommunication, where fiber lengths of tens or even hundreds of kilometers are required, to the delivery of high-power pulsed laser radiation, both in fundamental and multimode operation, where only a few meters (typically 10–20 m) suffice for most laser-based applications. Additionally, they are gaining interest in quantum technology such as quantum communication, quantum sensing, and precision metrology. Recently, it was reported that inhibited-coupling hollow-core double-nested fibers (IC-HCFs) exhibit confinement losses as low as 0.08 ± 0.03 dB/km at a wavelength of 1550 nm, the lowest attenuation ever achieved in an optical fiber, according to the authors [https://opg.optica.org/abstract.cfm?uri=OFC-2024-Th4A.8]. This makes IC-HCFs highly promising for a broad range of applications. However, their fabrication remains complex, requiring in-depth investigations across the entire development process. A multidisciplinary approach, encompassing thermodynamics, material science, optics, and laser physics, is essential to optimize their design, manufacturing, and qualification.

Relevance of the research topic:
Photonics is a key enabling technology in many industrial branches including communication, manufacturing, computing, health care and many more. Photonic technologies are therefore also of relevance for the GSaME, be it in the field of data transmission, sensing, metrology, diagnostics or even directly in form of the laser beam as a manufacturing tool. In all these fields, fiber-optic beam delivery is an essential approach to enhance performance and flexibilty for a given application. While the specific requirements that the fibers need to satisfy are as diverse as the potential applications, the scientific challenges basically always boil down to tasks such as cutsomizing the guided modes, reducing the losses and the sensitivity to bending, controlling dispersion, and - last but not least - advance the manuacturing techniques to be able to reproducibly produce the desired fibers.

Scientific objectives:
Current IC-HCFs are primarily designed for efficient guidance of fundamental-mode radiation, limiting their applicability for multimode laser beam transmission. The work planned in the present project aims at developing comprehensive simulation approaches and design models that account for different aspects such as field distribution, fiber losses, bending sensitiviy, dispersion effects and so on, in order to be able to design and optimize fibers depending on specific applications. Leveraging the fiber production facilities at the IFSW, established fabrication techniques like the stack-and-draw method will be further developed, and new manufacturing approaches will be explored to ensure a reliable and reproducible production process of bespoke fibers. The fabricated fibers will undergo detailed experimental characterization and their performance will be tested both at the IFSW and the INT.

Thesis Committee:
Prof. Verl (ISW)

Problem to be investigated:
Laser-based processes offer great potential with regard to a completely flexible, universal and self-configuring production of complex components (software-defined manufacturing). For this, manufacturing systems are needed that can adapt independently to new manufacturing processes and can be used universally. An important component here is universal process control that functions independently of material and process parameters. In the current state of the art, control parameters for, for example, welding depth control or height control in wire build-up welding are manually optimised for every welding task with a measurement signal, above all material but also process parameters dependent. As a result, a modification such as a material change or a change in the welding task leads to lengthy adjustment operations on the production system with a high level of effort.

Relevance of the research topic:
Laser processing methods have an undisputed potential to be the tool for individualised and function-optimised product design in combination with resource-efficient manufacturing for future industrial production. Laser beam welding of metallic materials is an indispensable manufacturing process for many applications in the future of mobility, such as in the production of battery cells, joining tasks in the manufacture of electric motors, but also in the production of components using additive manufacturing processes such as laser powder bed fusion. In all these areas, control of the welding process is the basic requirement for efficient and high-quality production. As a production technology process, laser processing is located in the thematic core of the GSaME and the topic meets its scientific ambition with its interdisciplinary research needs.

Scientific objectives:
How can a universal control system i.e. at least material and process parameters but also perhaps process-independent method for process control be designed using self-learning algorithms? For this purpose, a control system has to be developed on the basis of established measurement methods, starting with simple control tasks such as the welding depth control in laser beam welding or the height control in laser wire deposition welding, which independently adjusts to the changes in the measurement signals and process dynamics for different materials and process parameters. For this purpose, it is necessary to determine the differences in the signal acquisition and the changes in the process dynamics experimentally and to take them into account when designing the control system. For this purpose, modern imaging methods in process analytics are available, such as the X-ray diagnostics available at IFSW, state-of-the-art high-speed video methods with up to 100,000 images per second with high spatial resolution, but also experiments at large-scale research facilities such as the German Electron Synchrotron (DESY).

Thesis Committee:
Prof. Riedel (ISW), Prof. Weihe (IMWF)

Problem to be investigated:
Additive manufacturing offers a flexible process, which is well suited for the creation of individual components. It opens up a wide range of possibilities for the flexible and individual production of complex elements. Moreover, additive processes offer the possibility of producing components close to their final geometries, which minimizes the use of raw materials and saves resources. Virtual commissioning (VC) is an established method for the development of production machines and plants and their control systems. It uses simulations for tests and optimizations before the real commissioning. The focus of VC is on kinematic behavior with rigid bodies which allows to depict the behavior of many machines and robots. Additive manufacturing processes are also to be tested in VC in particular for continuous collision monitoring with the component being built up layer by layer. The current models, as well as classic simulation models which are employed in additive manufacturing, are not suitable for this process.

Relevance of the research topic:
The growing complexity of additive manufacturing processes and the increasing demands for process safety, quality and throughput time require sound process modeling. VC has the potential to test and to optimize manufacturing processes before the actual realization and to identify sources of error early on. This is particularly relevant for additive manufacturing due to the interactions between multiple independent physical, thermal and mechanical phenomena, which lead to complex relationships. Research on model development for VC contributes significantly to increasing efficiency, to quality assurance and to better integration of additive manufacturing technologies into existing production environments.

Scientific objectives:
The goal of the project is to develop innovative modeling approaches for virtual commissioning of additive manufacturing systems. Additive processes are particularly influenced by material-specific properties such as density, viscosity, or thermal conductivity. In addition, thermal effects such as absolute temperature in relation to material-dependent process limits, melting and solidification behavior, and heat transfer play a central role in the overall process and the resulting component quality. The central question of the project is therefore which physical effects are relevant for commissioning in a production context, and how these can be depicted with suitable models that can be integrated into simulation-based commissioning processes. Additive processes, in contrast to subtractive processes, are highly dependent on time and geometry. Process dynamics are significantly defined by the layered structure, which leads to continuous interaction between thermal, mechanical, and material flow-related effects. These characteristics result in a strong coupling between process parameters, machine movements, and the increasing geometry of the component. Based on these particularities, model architectures shall be conceptualized which fulfil the requirements of VC, using methods of artificial intelligence. A key issue is how the architectures can be designed to depict different process configurations. The developed model approaches will finally be integrated into a suitable simulation environment and validated based on experimental process data.