RWTH Aachen University (RWTH)
Canvas Category Consultancy : Research : Academic
The mission of the Chair of Production Engineering is to drive business development in industry from the perspective of continuous productivity increases. Digital solutions and business models are developed to ensure sustainable production. A particular strength lies in the close connection between engineering and business research. At the Chair of Manufacturing Technology, we research numerous manufacturing technologies and jointly design the digitally networked, sustainable production of the future. The digital twin - a virtual image of the component based on manufacturing data and a wide variety of models and simulations - plays a decisive role in this. The Chair of Machine Tools deals with the design, investigation and optimization of machine elements that are combined into mechatronic systems. The chair also focuses on intelligent control and automation concepts based on the Internet of Production. By linking process data with models, a detailed digital image of the component, machine and process can be generated.
Assembly Line
Mastering Ramp-up of Battery Production
The ramp-up phase of a gigafactory for the production of battery cells, modules and packs for electric mobility and other applications is crucial for its subsequent success. In the jointly published white paper “Mastering Ramp-up of Battery Production”, the Fraunhofer FFB and the Chair of Production Engineering of E-Mobility Components (PEM) at RWTH Aachen University provide information on strategies and resources for an efficient and successful start-up of a gigafactory. The following figures illustrate the importance of this: according to the publication, scrap rates of 15 to 30 per cent in the first few years are common in battery cell production. Even after five years, however, scrap rates are still high at around ten per cent. Each percentage point costs around 30.000 € per day and around ten million euros per year. A rejection rate of 30 per cent at full capacity utilisation therefore means costs of around 900.000 € per day. It is a threat to the entire European electric mobility industry if local battery cell manufacturers are unable to increase their production capacities due to problems with factory ramp-up. The white paper therefore begins by outlining the organisational and technical hurdles associated with ramping up a gigafactory, and then offers insights into how these hurdles can be overcome and how the ramp-up process can be effectively managed.
Recurrent neural networks as virtual cavity pressure and temperature sensors in high-pressure die casting
High-pressure die casting (HPDC) is a permanent mold-based production technology that facilitates the casting of near net shape components from nonferrous alloys. The pressure and temperature conditions within the cavity impact the cast product quality during and after the conclusion of the die filling process. Die surface cavity sensors can deliver information describing the conditions at the die-casting interface. They are associated with high costs and limited service lifetimes below the achievable total cycle count of the die inserts and therefore ill-suited for industrial use cases. In this work, the suitability of long short-term memory (LSTM) recurrent neural networks (RNN) for substituting physical cavity temperature and pressure sensors virtually after the production ramp-up or at the end of the sensor service life is investigated. Training LSTMs with data of 233 casting cycles with different process parameters provides networks which are then applied to 99 further cycles. The prediction accuracy is investigated for different time interval lengths in the solidification and cooling phase. For longer time intervals, the cavity pressure prediction deteriorates, potentially due to a highly individual and hardly ascertainable buildup of casting distortion and internal stresses. Overall, however, the accuracy of the developed LSTMs is excellent for the cavity temperatures and good for the cavity pressures.
Membion secures €5M for wastewater treatment tech
German wastewater treatment company Membion announced an investment of around €5 million led by TechVision Fonds (TVF) and DeepTech & Climate Fonds (DTCF). The company develops and produces membrane bioreactor (MBR) modules for wastewater treatment.
With the multi-patented technology, municipal and industrial wastewater treatment plant operators can meet the growing demands on water quality and significantly reduce operating costs. Membion founders Dr Klaus Vossenkaul and Dirk Volmering studied process engineering at RWTH Aachen University and have over 20 years of experience in the industry.
The company’s unique membrane filters, so-called hollow fibre MBR modules, achieve significant energy and thus, operating cost savings while maintaining space efficiency. There is no need for secondary clarification as in conventional systems.
Germany-based cylib raises €8M for its new lithium battery recycling facility
Germany-based cylib, a company that offers an “innovative” and sustainable technology for Lithium-ion battery recycling, announced on Wednesday that it has raised €8M in an extension Seed round of funding. With this, the total Seed round comes to €11.6M. The company says the funds will be used to establish a recycling facility.
The round was led by Europe’s leading climate tech VC, World Fund. It only backs entrepreneurs building climate tech solutions that have the potential to save at least 100 megatonnes of CO2 every year, which it believes cylib can achieve through its technology.
Aachen-based cylib was spun out of RWTH Aachen University and its proprietary technology is now patent pending.
3D Printing Helps Realize the Promise of Distributed Manufacturing
Additive manufacturing offers a solution to the challenges of distributed manufacturing by enabling local and highly flexible production of small quantities. For many use cases, additive manufacturing systems and processes are now technologically ready for small-series production. Applying 3D printing in distributed manufacturing will be most beneficial for producing high-value parts, such as those used in the aerospace and medical-technology industries, or low-volume replacement parts. These are among the transformative technology applications that constitute Industry 4.0.
In 2022, BCG undertook a study, in collaboration with RWTH Aachen University and the ACAM Aachen Center for Additive Manufacturing, to capture insights into how the application of 3D printing in distributed manufacturing adds value and what the prerequisites are for successful use cases. The study included interviews with a panel of approximately 15 leading experts in business and academia, from a variety of countries.
Transfer learning with artificial neural networks between injection molding processes and different polymer materials
Finding appropriate machine setting parameters in injection molding remains a difficult task due to the highly nonlinear process behavior. Artificial neural networks are a well-suited machine learning method for modelling injection molding processes, however, it is costly and therefore industrially unattractive to generate a sufficient amount of process samples for model training. Therefore, transfer learning is proposed as an approach to reuse already collected data from different processes to supplement a small training data set. Process simulations for the same part and 60 different materials of 6 different polymer classes are generated by design of experiments. After feature selection and hyperparameter optimization, finetuning as transfer learning technique is proposed to adapt from one or more polymer classes to an unknown one. The results illustrate a higher model quality for small datasets and selective higher asymptotes for the transfer learning approach in comparison with the base approach.
Fields of action towards automated facility layout design and optimization in factory planning – A systematic literature review
The success of a factory planning project is significantly influenced by the layout design. It contributes to make the production process more economical and reliable. Studies show that an effective layout can reduce the operating costs of a factory by up to 30%.
Layout design is a very complex planning problem characterized by the conflict between competing goals and restrictions. Both quantitative goals, such as material flow, and qualitative goals, like communication and adaptability of a layout, must be taken into account. In addition, regulatory requirements and norms, which are the restrictions the design is based on, must also be met.
Despite the high complexity, the arrangement of the operational functional units is usually done manually, either on paper or with a digital layout design software. The layout variants are then evaluated by experts to identify the optimal layout.