US 12,333,702 B2
Method for automatic quality inspection of an aeronautical part
Sylvaine Picard, Moissy Cramayel (FR); Nacim Belkhir, Moissy Cramayel (FR); and Enrico Sartor, Moissy Cramayel (FR)
Assigned to SAFRAN, Paris (FR)
Appl. No. 18/006,985
Filed by SAFRAN, Paris (FR)
PCT Filed Jun. 29, 2021, PCT No. PCT/FR2021/051190
§ 371(c)(1), (2) Date Jan. 26, 2023,
PCT Pub. No. WO2022/023633, PCT Pub. Date Feb. 3, 2022.
Claims priority of application No. 2007866 (FR), filed on Jul. 27, 2020.
Prior Publication US 2023/0274409 A1, Aug. 31, 2023
Int. Cl. G06T 7/00 (2017.01); G01N 35/00 (2006.01)
CPC G06T 7/0004 (2013.01) [G01N 35/00613 (2013.01); G06T 2207/10072 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30164 (2013.01)] 11 Claims
OG exemplary drawing
 
1. A method for automatic quality inspection of an aeronautical part, comprising:
training an artificial neural network on a first training database;
acquiring at least one image of the aeronautical part;
detecting faults on the image of the aeronautical part using the trained artificial neural network;
unsupervised training of an auto-encoder on a second training database comprising a plurality of training images of aeronautical parts without fault, comprising the following sub-steps:
projecting each image of the second training database on a mathematical space of smaller dimension than the dimension of each image of the second training database, so that the set of projections of the images of the second database in the mathematical space follows a predefined probability law;
reconstructing each image of the second training database from the projection of the image of the second training database onto the mathematical space to obtain a reconstructed image;
for each image of the second training database, calculating a plurality of metrics from the projection of the image of the second training database onto the mathematical space or from the corresponding reconstructed image, to obtain a metric value for each metric;
supervised training of a classifier from the metric values obtained;
detecting faults or anomalies in the image of the aeronautical part using the trained auto-encoder and classifier;
when the artificial neural network detects at least one fault on the image of the aeronautical or when the artificial neural network detects no fault on the image of the aeronautical part and the classifier detects at least one fault or anomaly in the image of the aeronautical part, the aeronautical part is considered non-compliant
otherwise, the aeronautical part is considered compliant.