Tomographic analysis method for detecting anomalies
A method for the tomographic analysis detects anomalies in a part. The method includes the steps of acquiring at least one three-dimensional image of the part by means of a tomography device; subdividing the image into elementary subparts; analyzing a grayscale distribution in each subpart; obtaining at least one parameter representative of said grayscale distribution for each subpart; and comparing the one or more parameters obtained for each subpart with standard values characteristic of a defect-free region. The method further includes the steps of detecting abnormal subparts for which the one or more parameters differ from the standard values; determining risk regions, which comprise each abnormal subpart and each subpart adjacent to at least one abnormal subpart; and analyzing the risk regions in order to detect the anomalies in the part.
1 . A method for a tomographic analysis of a part in order to detect anomalies, the method comprising the following steps:
acquiring at least one three-dimensional image of the part by means of a tomography device,
subdividing the at least one three-dimensional image into elementary subparts,
analyzing a grayscale distribution in each subpart of the elementary subparts and obtaining at least one parameter representative of the grayscale distribution for each subpart,
comparing the at least one parameter obtained for each subpart with standard values characteristic of a defect-free region and detecting abnormal subparts for which the at least one parameter differs from the standard values,
determining risk regions, which comprise abnormal subparts and each subpart adjacent to at least one abnormal subpart, and
analyzing the risk regions in order to detect the anomalies in the part.
2 . The method according to claim 1 , wherein the subdividing step comprises determining at least one standard dimension of the anomalies in the part, each subpart having dimensions between half the at least one standard dimension and double the at least one standard dimension.
3 . The method according to claim 1 , wherein the at least one parameter representative of the grayscale distribution for each subpart is selected from among an average, a maximum, and a minimum in the grayscale distribution for each subpart.
4 . The method according to claim 1 , wherein the at least one parameter representative of the grayscale distribution for each subpart is selected from among a gradient, a divergence, and a curl of the grayscale distribution for each subpart.
5 . The method according to claim 1 , wherein the comparing step comprises making use of at least one digital processing tool selected from among a nearest neighbor analysis, a classification tree analysis, a support vector machine analysis, and a neural network analysis.
6 . The method according to claim 1 , wherein the part comprises a woven composite material.
7 . The method according to claim 1 , wherein the part comprises a metal material.
8 . The method according to claim 1 , wherein the part comprises a turbomachine casing part, a blade of a compressor rotor, a stator, or a fan of a turbomachine.