{"number":"US12697781B1","kindCode":"B1","documentType":"grant","title":{"original":"Machine learning for real-time monitoring and control of additive manufacturing","english":"Machine learning for real-time monitoring and control of additive manufacturing"},"abstract":{"original":"A machine learning (ML) approach enables real-time direct ink write (DIW) print-parameter optimization through in-situ monitoring of printed line geometry. The method can use an invertible neural network (INN) to solve both forward and inverse, or optimization, problems using a single network. By combining in-situ computer vision and INNs, DIW printing parameters can be autonomously optimized to print a target line width in a matter of seconds. Furthermore, defects that occur during printing can be rapidly identified and corrected autonomously.…","english":"A machine learning (ML) approach enables real-time direct ink write (DIW) print-parameter optimization through in-situ monitoring of printed line geometry. The method can use an invertible neural network (INN) to solve both forward and inverse, or optimization, problems using a single network. By combining in-situ computer vision and INNs, DIW printing parameters can be autonomously optimized to print a target line width in a matter of seconds. Furthermore, defects that occur during printing can be rapidly identified and corrected autonomously.…"},"applicants":["National Technology & Engineering Solutions of Sandia, LLC"],"inventors":["Devin John Roach","Andrew Rohskopf","William Derek Reinholtz","Adam Wade Cook","Leah N. Appelhans"],"filingDate":"2023-10-10","publicationDate":"2026-08-04","grantDate":"2026-08-04","verifiedWithOffice":true,"pdfUrl":"https://downloadpatent.com/pdf/US12697781B1.pdf","pages":{"en":"https://downloadpatent.com/patent/US12697781B1","zh-Hant":"https://downloadpatent.com/zh-Hant/patent/US12697781B1","ja":"https://downloadpatent.com/ja/patent/US12697781B1"}}