POWDER BED FUSION MONITORING

A method of monitoring an additive manufacturing build process includes first and second phases. The first phase includes depositing a powder layer onto a powder bed (14). A topographical profile (36) of the powder bed (14) is captured with a profilometer (24, 24'). An image (38) of the powder...

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Bibliographische Detailangaben
Hauptverfasser:	RUNKLE, Rebecca L, MORGANSON, David W, SURANA, Amit, HIHEGLO, Eklou E, BENNETT, Michael Walter
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 COUNTING MAKING METALLIC POWDER MANUFACTURE OF ARTICLES FROM METALLIC POWDER MEASURING MEASURING ANGLES MEASURING AREAS MEASURING IRREGULARITIES OF SURFACES OR CONTOURS MEASURING LENGTH, THICKNESS OR SIMILAR LINEARDIMENSIONS PERFORMING OPERATIONS PHYSICS POWDER METALLURGY SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDEDFOR SHAPING OR JOINING OF PLASTICS TESTING TRANSPORTING WORKING METALLIC POWDER WORKING OF PLASTICS WORKING OF SUBSTANCES IN A PLASTIC STATE, IN GENERAL
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Beschreibung
Zusammenfassung:	A method of monitoring an additive manufacturing build process includes first and second phases. The first phase includes depositing a powder layer onto a powder bed (14). A topographical profile (36) of the powder bed (14) is captured with a profilometer (24, 24'). An image (38) of the powder bed (14) is captured with a camera (26). The image (38) and topographical profile (36) are combined to create a data set (40) that is transferred to a machine learning algorithm. A set of training data is generated and includes a set of deviations from a nominal model. The second phase includes depositing a powder layer onto the powder bed (14). An image (38) of the powder bed (14) is captured and compared to the set of training data. Deviations from the nominal model of the first powder bed (14) are determined. Any deviations that are greater than a numerical threshold are labelled and identified as a defect which includes its type and severity.