SYSTEMS, METHODS, AND MEDIA FOR ARTIFICIAL INTELLIGENCE PROCESS CONTROL IN ADDITIVE MANUFACTURING

Systems, methods, and media for additive manufacturing are provided. In some embodiments, an additive manufacturing system comprises: a hardware processor that is configured to: receive a captured image; apply a trained failure classifier to a low-resolution version of the captured image; determine...

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Bibliographische Detailangaben
Hauptverfasser:	PUTMAN, Matthew, C, NIRMALESWARAN, Aswin, Raghav, LIMOGE, Damas, PINSKIY, Vadim
Format:	Patent
Sprache:	eng ; fre ; ger
Schlagworte:	ADDITIVE MANUFACTURING TECHNOLOGY ADDITIVE MANUFACTURING, i.e. MANUFACTURING OFTHREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVEAGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING,STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING CALCULATING CASTING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONTROL OR REGULATING SYSTEMS IN GENERAL CONTROLLING COUNTING FUNCTIONAL ELEMENTS OF SUCH SYSTEMS MAKING METALLIC POWDER MANUFACTURE OF ARTICLES FROM METALLIC POWDER MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS PERFORMING OPERATIONS PHYSICS POWDER METALLURGY REGULATING SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDEDFOR SHAPING OR JOINING OF PLASTICS TRANSPORTING WORKING METALLIC POWDER WORKING OF PLASTICS WORKING OF SUBSTANCES IN A PLASTIC STATE, IN GENERAL
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Beschreibung
Zusammenfassung:	Systems, methods, and media for additive manufacturing are provided. In some embodiments, an additive manufacturing system comprises: a hardware processor that is configured to: receive a captured image; apply a trained failure classifier to a low-resolution version of the captured image; determine that a non-recoverable failure is not present in the printed layer of the object; generate a cropped version of the low-resolution version of the captured image; apply a trained binary error classifier to the cropped version of the low- resolution version of the captured image; determine that an error is present in the printed layer of the object; apply a trained extrusion classifier to the captured image, wherein the trained extrusion classifier generates an extrusion quality score; and adjust a value of a parameter of the print head based on the extrusion quality score to print a subsequent layer of the printed object.