SYSTEMS AND METHODS FOR FORMULATING MATERIAL IN A DATA-DRIVEN MANNER

Systems and methods for optimizing the formulation of materials are provided. The systems and methods employ a data-driven, iterative approach to derivate optimal material formulations. One portion of the system includes a sample automation system that outputs the material samples to be tested, and...

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Bibliographische Detailangaben
Hauptverfasser:	von Vacano, Bernhard Ulrich, Foshey, Michael J, Matusik, Wojciech, Shou, Wan, Lukovic, Mina Konakovic, Stoll, Klaus, Erps, Timothy P, Goetzke, Hanns-Hagen
Format:	Patent
Sprache:	eng
Schlagworte:	ADHESIVES AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING CALCULATING CHEMICAL PAINT OR INK REMOVERS CHEMISTRY COATING COMPOSITIONS, e.g. PAINTS, VARNISHES ORLACQUERS COMPUTING CORRECTING FLUIDS COUNTING DYES FILLING PASTES HANDLING RECORD CARRIERS INKS METALLURGY MISCELLANEOUS APPLICATIONS OF MATERIALS MISCELLANEOUS COMPOSITIONS NATURAL RESINS PAINTS PASTES OR SOLIDS FOR COLOURING OR PRINTING PERFORMING OPERATIONS PHYSICS POLISHES PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDEDFOR SHAPING OR JOINING OF PLASTICS TRANSPORTING USE OF MATERIALS THEREFOR WOODSTAINS WORKING OF PLASTICS WORKING OF SUBSTANCES IN A PLASTIC STATE, IN GENERAL
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Beschreibung
Zusammenfassung:	Systems and methods for optimizing the formulation of materials are provided. The systems and methods employ a data-driven, iterative approach to derivate optimal material formulations. One portion of the system includes a sample automation system that outputs the material samples to be tested, and a second portion of the system includes an optimization engine that analyzes data extracted from the material samples and generates additional formulations for materials to be printed and tested. This process continues so that optimal material formulations can be determined based on desired mechanical properties of the material to be optimized. The optimization engine can further be capable of predicting results of formulation that have not yet been tested and using those predictions to further drive the next suggested materials to be tested.