Machine Learning Lifecycle Management Using Dataspaces for Optimized Machine Parameterization in Recycled Plastic Packaging

doi:10.1007/978-3-032-10486-1_11

Machine Learning Lifecycle Management Using Dataspaces for Optimized Machine Parameterization in Recycled Plastic Packaging

2026-01-01

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DOI：10.1007/978-3-032-10486-1_11

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The increasing demand for sustainable plastic packaging has led to a growing interest in optimizing machine parameters for processing recycled plastics. This paper presents a dataspace-driven demonstrator for machine learning (ML) lifecycle management in machine parameter optimization for thermoforming processes. Our setup involves three key dataspace participants: a thermoforming machine manufacturer, a plastic packaging producer, and an ML service provider. By leveraging dataspaces and a microservice-based architecture, we enable secure data exchange while addressing industry concerns about proprietary data sharing. The implemented demonstrator integrates the ML lifecycle in the form of microservices, facilitating efficient training, deployment, and monitoring of ML models. Our demonstrator highlights the potential of dataspaces in enabling collaborative, data-driven optimization of machine parameters while maintaining data sovereignty.

Keywords:

Dataspace

Industrial Internet of Things

Machine Learning Lifecycle Management

Gaia-X

Recycled Plastic Packaging

Machine Learning Lifecycle Management Using Dataspaces for Optimized Machine Parameterization in Recycled Plastic Packaging

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