Predicting Respone: 3d printed biopolymer block dataset

Dataset consisting of 87 edge shrinkage recording of 87 3d printed biopolymer components printed with inhouse receipe 10.5281/zenodo.5557218 This dataset is reported in the paper Rossi G., Chiujdea R., Hochegger L., LHarchi A., Harding J., Nicholas P., Tamke M., Ramsgaard Thomsen M. (forthcomming 20...

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Bibliographische Detailangaben
Hauptverfasser:	Rossi, Gabriella, Chiujdea, Ruxandra, Harding, John, Nicholas, Paul, Tamke, Martin, Ramsgaard Thomsen, Mette
Format:	Dataset
Sprache:	eng
Schlagworte:	biopolymer cellulose d printing machine learning shrinkage tracking
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Beschreibung
Zusammenfassung:	Dataset consisting of 87 edge shrinkage recording of 87 3d printed biopolymer components printed with inhouse receipe 10.5281/zenodo.5557218 This dataset is reported in the paper Rossi G., Chiujdea R., Hochegger L., LHarchi A., Harding J., Nicholas P., Tamke M., Ramsgaard Thomsen M. (forthcomming 2022) Statistically modelling the curing of cellulose-based 3d printed components: Methods for material dataset composition, augmentation and encoding. In Design Modelling Symposium Berlin: Towards Radical Regeneration. 26-28 September 2022. University of the Arts Berlin, Germany
DOI:	10.5281/zenodo.6631766