MEMAS: Metadata Enriched Manufacturing data for Automated Simulation

Manufacturing of composite parts involves multiple process steps, which each involve large datasets generated from processing machines or surrounding sensors. With help of the recently developed data management system shepard, the project MEMAS aims at storing and connecting these manufacturing data in a persistent way.

Manufacturing of composite parts involves multiple process steps, from the production of semi-finished materials to their processing and assembly. At each level of production, large datasets can be produced to trace back each state of the composite material and relate it to the final quality of the manufactured structure. With help of the recently developed data management system shepard, the project MEMAS aims at storing and connecting these manufacturing data in a persistent way. It focuses particularly on the standardization, collection and annotation of meta-data and on their automatic transfer in a simulation environment to estimate the actual structural performances. The consideration of potential defects resulting from the manufacturing techniques or induced by the surrounding environment will allow for the improvement of finite-element methods and of their accuracy. Furthermore, the developed tools will support the manufacturing field and highlight the consequences of manufacturing parameters on the structural behaviour, enabling adjustments of the process parameters after each produced part. Finally, the persistent and structured storage of research data and their metadata in form of FAIR Digital Objects or with help of DataCrates will support long-term data analysis and the further comprehension of manufacturing techniques.

To this goal, software solutions will be developed for the two exemplary manufacturing processes tape laying and additive manufacturing and combined in a general toolchain. The potential of the developed methodology will be tested by performing mechanical tests on representative parts and by comparing the results with the numerically predicted behaviour. Overall acquired experimental, manufacturing and simulation data, meta-data formats and scientific results will be transferred via open-source solutions like the Zenodo platform in the HMC community.

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