Heieck, Frieder
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This study presents a method for early identification of AM-suitable components and circular design routes. A structured, data-lean questionnaire with AHP-based weighting, combined with rule-based R-strategy identification and an AM-specific feasibility gate, enables transparent screening in early development. Demonstrated on a structural bus component for both diesel and electric operation, the approach proposes a dominant R-strategy and integrates a prospective LCA-in-the-loop workflow, showing how early circularity signals and life-cycle feedback inform robust redesign decisions.
Metal additive manufacturing (AM) using laser powder bed fusion (LPBF) is often associated with material and energy savings when compared to conventional manufacturing (CM). In industrial applications such as commercial vehicle components, structurally optimized designs can reduce environmental impacts – provided that increased complexity and build time do not negate these gains. Given that up to 80 % of a product’s environmental impact is determined during the design phase, prospective product carbon footprint (PCF) assessment tools could support more sustainable design choices. However, existing solutions remain insufficiently validated for use in metal AM, particularly regarding their scope, precision, and usability in real-world industrial workflows. This study examines the applicability of existing prospective PCF tools using simulated and physically produced parts. In a comparative pilot case study, a commercial bus component is optimized for AM using multiple design approaches and compared to an initial and an optimized CM variant. While the optimized CM parts are not physically produced, their resource demand is assessed using a combination of simulation and database information. For AM parts, process data including energy consumption, inert gas use, powder input, build time, and part weight is recorded and analyzed. The PCF for each design and manufacturing scenario is calculated, covering both production (cradle-to-gate) and use-phase emissions. Environmental impacts are evaluated using a classical life cycle assessment (LCA), considering functional equivalence between designs. The goal is to evaluate whether weight savings from AM-optimized designs translate into net life cycle CO2eq- reductions, and to assess the accuracy and usability of early-stage PCF prospection tools against detailed LCA results. The study highlights research gaps and integration challenges, laying the groundwork for extensive investigations.
Reinforcement Learning (RL) has proven effective in optimizing complex processes, yet its application in real-world scenarios often encounters the Sim-to-Real Gap—discrepancies in performance between simulated training and real-world deployment. This paper explores the Sim-to-Real Gap in the context of a simplified but dynamic environment: color mixing. The study focuses on training a RL agent to mix target colors from three ink colors, examining the effect of incrementally increasing simulation complexity on the agent’s ability to generalize to real-world scenarios. Motivated by the challenges in automating precision tasks like CAR-T cell therapy, this work investigates how the level of simulation detail impacts RL performance in real-world applications. The findings offer valuable insights into the role of simulation fidelity in achieving robust real-world performance, contributing to a deeper understanding of RL in high-precision, control-intensive tasks.
Federated Learning (FL) offers a solution to the challenges of traditional centralized machine learning by enabling decentralized training and exchanging only model updates instead of raw data. This approach addresses key issues such as privacy concerns and high data transfer costs. However, integrating FL into existing Machine Learning Operations (MLOps) pipelines presents challenges, particularly regarding model versioning, synchronization, and scalability. This paper introduces a concept for centralized model management that enables the integration of FL into existing MLOps pipelines without the need to overhaul the existing architecture. The concept is specifically developed for deployment in an industrial setting, with plans for implementing both FL and Transfer Learning (TL) in the future. The proposed approach emphasizes flexibility, ensuring that it can be easily extended to accommodate additional methods and seamlessly integrated into diverse, pre-existing infrastructure. The management of the system is facilitated using the open-source tool MLflow, which offers significant advantages over specialized FL frameworks, particularly in terms of adaptability and resource optimization.
Die individuelle Markierung von Gussteilen ermöglicht die direkte Zuordnung von Prozesseinflussgrößen aus Teilprozessen wie Formen, Schmelzen und Gießen zu beliebigen Qualitätskenngrößen. Sie bietet damit den Schlüssel zu einer datenbasierten Analyse der Wirkzusammenhänge zwischen Prozess und technischen, ökologischen und ökonomischen Eigenschaften eines Gussteils sowie einer darauf basierenden KI-gestützten Prozesskontrolle.
This paper provides an overview and examples of the potential, but also the challenges of Metal Additive Manufacturing in the future Circular Economy paradigm. The Circular Economy, aimed at reducing resource consumption, waste generation, and the overall carbon footprint of our industries, presents significant challenges to traditional, linear production systems. In this context, Additive Manufacturing emerges as a highly flexible technology with the potential to overcome many of these hurdles: The ability to reduce material usage through optimized lightweight and functional designs, the use of recycled materials, Additive Manufacturing-based repair and remanufacturing of tools and products or the optimization of production through hybrid manufacturing approaches (e.g. combinations of subtractive and additive manufacturing) are just a few examples.
However, the lack of automated and digitalized production processes, complex process chains with potential health and safety concerns, and high demands on material and component qualification still pose significant hurdles to the widespread adoption of Metal Additive Manufacturing in the Circular Economy framework. In addition, the Circular Economy alone does not necessarily mean a lower carbon footprint over the entire product life cycle. On the contrary, depending on the specific process used, the carbon footprint of metal additive part production is often higher than in conventional manufacturing scenarios due to the high energy requirements of the additive process and long production times. Therefore, in this study, a comprehensive literature review is performed to outline the potential of Additive Manufacturing for Circular Economy principles, providing several beneficial examples of the so-called “R-strategies” covered with the help of Additive Manufacturing. In addition, an outlook is given on the development of new technologies to increase the potential of Additive Manufacturing to make an important contribution to circular production scenarios.
Machine Learning Based Parameter Estimation of Energy Models in Digital Production Environments
(2025)
The growing relevance of data science in engineering highlights the challenges of modelling complex systems using conventional methods, which are often time-consuming. This paper explores the application of various machine learning techniques to create energy models, aiming to minimize model deviation errors against real measurements. By comparing different approaches, this study seeks to optimize energy models, enhancing both accuracy and efficiency.
Tensile properties of 2D braid reinforced composites are analyzed with regards to process-induced fiber architecture variations. First, a parametric study concerning the effect of radial braiding parameters on the braid characteristics is performed and a quantitative quality index based on visual attributes is introduced. Second, test laminates are produced with vacuum resin infusion to conduct tensile tests with biaxial and triaxial braids of varying qualities. To correlate fiber architecture variations with mechanical properties, each braided layer is scanned and analyzed by means of gray scale based image processing. This allows for determining specimen-specific fiber orientations and correlating them with mechanical properties. The results show strong correlations of measured fiber architectures and mechanical properties. Thus, the study suggests that detailed information about the (local) fiber architecture should allow for significantly improving the prediction of 2D braided composite properties.
Clubfoot is a common congenital foot deformity that leads to constant pain and significant limitations if left untreated or not treated adequately. The most used method for treating clubfoot is the Ponseti method. It involves a correction phase where about five plaster casts are applied and changed weekly. This treatment lasting about 2 to 3 months, is the most chosen method due to its high success rate. However, treated babies often experience skin complications caused by stiff and tight casts. Previous research showed that viable solutions already exist including orthoses. In this research, a developed method known as VDI 2221 was applied and the printable orthosis using 3D printer was selected as an alternative to Ponseti method. Calculations and finite element method (FEM) analysis demonstrated that the orthosis made of PA6-CF provides sufficient stiffness and strength, assuming the weight force of the foot is 10 N. The selected design was developed based on requirements and functional analysis, effectively mitigating the disadvantages of the Ponseti method. The developed orthosis can be manufactured globally using the 3D printing process, with a manufacturing cost of around €150, excluding assembly costs. In summary, a new solution was proposed within the same treatment method, effectively eliminating skin complications, and enabling cost-effective manufacturability on a global scale.
Various software environments have been developed in the past to create digital twins of single cells or a digital twin of a factory. Each environment has its own strengths and weaknesses and has been designed with a specific focus. The environments that are able to holistically simulate complete factories are limited in terms of the modelling details required for the analysis of single manufacturing cells (e.g., manufacturer-independence of the individual digital twins) and their ability for virtual commissioning. This paper presents three options for realising a virtual commissioning of linked cells using a 3D integration platform with NVIDIA Omniverse, consisting of two different digital models fused into a combined model, also representing material flow. First, with a source/sink solution and unidirectional connector controlled by OPC UA; secondly, with a bidirectional connector, developed in the course of this elaboration, and an extension of the 3D integration platform controlled by Apache Kafka; thirdly, with a bidirectional connector and using only an extension of the 3D integration platform. The research demonstrates that virtually commissioning multiple linked digital twins from different manufacturers in a 3D platform with material flow makes a significant contribution to the industrial metaverse.
