Triangulation-based anomaly detection in three dimensional printers
AssigneePeridot Print LLC
InventorsPrakash Reddy, Prateek Kumar Sikdar, Amit Kumar, Manivannan Srinivasan
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Abstract
Examples of systems for triangulation-based detection of anomaly in a print job performed by a three-dimensional printer are described herein. In an example, a data pertaining to a set of layers printed based on the print job of the 3D printer may be provided to two models to obtain respective predicted anomalies. Thereafter, the obtained predicted anomalies may be triangulated to detect an anomaly in the layer being printed by the 3D printer.
Claims19
A method comprising:
- providing, by a processor, a data set pertaining to a sequence of layers that have been printed by a 3D printer in a print job for one or more objects, for interpretation by a first machine learning model and a second machine learning model;
- providing, by the processor, real-time data of a current layer being printed by the 3D printer, which is subsequent to the sequence of layers that have been printed, to the first machine learning model and the second machine learning model;
- based on the interpretation and the real-time data, obtaining, by the processor, a first predicted anomaly and a second predicted anomaly for the current layer being printed, from the first machine learning model and the second machine learning model, respectively;
- detecting an anomaly in the current layer being printed based on triangulation of the first predicted anomaly and the second predicted anomaly;
- and
- after detecting the anomaly in the current layer, either terminating the print job to lessen build material wastage, or adjusting printing of the print job to minimize quality degradation of the objects.
A non-transitory machine-readable medium storing instructions executable by a processor to perform processing comprising:
- providing a data set pertaining to a sequence of layers that have been printed by a 3D printer in a print job for one or more objects, for interpretation by a first machine learning model and a second machine learning model;
- providing real-time data of a current layer being printed by the 3D printer, which is subsequent to the sequence of layers that have been printed, to the first machine learning model and the second machine learning model;
- based on the interpretation and the real-time data, obtaining a first predicted anomaly and a second predicted anomaly for the current layer being printed, from the first machine learning model and the second machine learning model, respectively;
- detecting an anomaly in the current layer being printed based on triangulation of the first predicted anomaly and the second predicted anomaly;
- and
- in response to detecting the anomaly in the current layer, either terminating the print job to lessen build material wastage, or adjusting printing of the print job to minimize quality degradation of the objects.
A system comprising:
- a processor;
- and
- a memory storing instructions executable by the processor to perform processing comprising:
- providing a data set pertaining to a sequence of layers that have been printed by a 3D printer in a print job for one or more objects, for interpretation by a first machine learning model and a second machine learning model;
- providing real-time data of a current layer being printed by the 3D printer, which is subsequent to the sequence of layers that have been printed, to the first machine learning model and the second machine learning model;
- based on the interpretation and the real-time data, obtaining a first predicted anomaly and a second predicted anomaly for the current layer being printed, from the first machine learning model and the second machine learning model, respectively;
- detecting an anomaly in the current layer being printed based on triangulation of the first predicted anomaly and the second predicted anomaly;
- and
- in response to detecting the anomaly in the current layer, either terminating the print job to lessen build material wastage, or adjusting printing of the print job to minimize quality degradation of the objects.
Dependencies are inferred from the claim text.