# Literature This section contains a summary of the background and related work in the field of machine learning lifecycle management and its application in the context of recycled plastic processing that did not fit into the paper due its page limit. We added it here for interested readers. ## Machine Parameter Optimization for Recycled Plastic Processing Recent research has explored the use of machine learning techniques to predict and optimize process parameters. Pelzer et al. have shown that Invertible Neural Networks (INNs) are promising for generating process parameters to achieve desired part properties with high accuracy. Pazhamannil et al. and Manoharan et al. successfully employed Artificial Neural Networks (ANNs) to predict tensile strength based on various process parameters, demonstrating good agreement with experimental results. Seifert et al. (2025) developed an analytical model to predict the shear viscosity of polypropylene compounds, a key parameter for ensuring efficient processing and consistent product quality. In a related study, Seifert et al. (2024) proposed an analytical model for predicting the tensile modulus of polypropylene compounds with various fillers and additives. Their work also compared the model’s performance against an artificial neural network, highlighting the strengths and limitations of both approaches. Other methodologies for optimizing plastic extrusion processes include artificial neural networks, fuzzy logic, genetic algorithms, and response surface methodology (Raju et al.). Overall, it can be stated that the integration of machine learning techniques and analytical models has greatly enhanced the prediction and optimization of process parameters in polymer processing. ## Life Cycle Management for Machine Learning Thopalle et al. presents a unified ML approach for artifact management in Jenkins CI/CD pipelines, addressing multiple functions such as retention prediction and compression optimization with a single model. Schlegel and Sattler provide an overview of systems and platforms that support the management of ML artifacts, including datasets, models, and configurations. They establish assessment criteria and apply them to over 60 systems. Additionally, Schlegel and Sattler define a typical machine learning lifecycle with four stages: the Requirements, Data-oriented, Model-oriented, and Operations-oriented stages. The Requirements Stage focuses on defining the functional and technical prerequisites of the ML model, determining the model types and data sources best suited for the given problem. The Data-oriented Stage encompasses data collection, cleaning, labeling, and feature engineering to ensure the availability of high-quality datasets for training. The Model-oriented Stage includes model selection, training, evaluation, and optimization to develop a robust ML model. Finally, the Operations Stage involves model deployment, continuous monitoring, and integration with production systems, ensuring optimal performance and reliability. ![Schlegel Life Cycle Model](../../../resources/schlegel.png) Besides the lifecycle model by Schlegel and Sattler, alternative process models for ML lifecycle management exist. Wirth and Hipp introduced CRISP-DM (Cross Industry Standard Process for Data Mining), a widely used framework that provides a structured approach to data mining and machine learning projects. Another notable extension is Huber et al.'s DMME (Data Mining Methodology for Engineering Applications), which builds upon CRISP-DM by integrating engineering-specific considerations for a more holistic approach to ML lifecycle management. Overall, it can be stated that numerous concrete implementations exist for achieving lifecycle management. Effectively realizing an ML lifecycle system corresponds to integrating elements of the process model, such as the lifecycle stages introduced by Schlegel and Sattler, into a cohesive software architecture. ## Literature Sources
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## Plans for Future Work In the future, we plan to implement the following features. This will result in a new Repository. This Repo will be archived. - Add contract negotiation: replace the current Dataspace Connector Service with a Released Version of the Eclipse Dataspace Connector - Sequential Release of Labelled Data: currently the hole dataset is added to the database at once. In the future, we plan to release the data in a sequential manner. That way one can observe how the trained models get better over time. ## Contact If you have any questions or feedback, feel free to contact me via [email](mailto:alexander.nasuta@wzl-iqs.rwth-aachen.de) or open an issue on repository. ## Credits This project and the corresponding technical communication was made possible through the contributions of the following individuals: - **Alexander Nasuta**, M.Sc. – Conceptualization, Software, Writing - **Sylwia Olbrych**, M.Sc. – Conceptualization, Writing - **Prof. Christoph Quix** – Conceptualization, Writing - **Dipl.-Ing. Tim Kaluza** – Conceptualization - **Dipl.-Ing. Florian Schaller** – Data Curation - **Sabrina Steinert**, M.Sc. – Conceptualization - **Hans Aoyang Zhou**, M.Sc. – Writing (Review) - **Dr. Anas Abdelrazaq** – Writing (Review) - **Prof. Robert H. Schmitt** – Supervision, Funding Acquisition