1 research outputs found
Deep Learning Automated System for Thermal Defectometry of Multilayer Materials
Currently, along with growth in industrial production, the requirements for product quality testing are also increasing. In the tasks of defectoscopy and defectometry of multilayer materials, the use of thermal non-destructive testing method is promising. At the same time, interpretation of thermal testing data is complicated by a number of factors, which makes the use of traditional methods of data processing ineffective. Therefore, an urgent task is to search for new methods of thermal testing that will automate the diagnostic process and increase information content of obtained results. The purpose of article is to use the advances in deep learning for processing results of active thermal testing of products made of multilayer materials and development of an automated system for thermal defectoscopy and defectometry of such products.The proposed system consists of a heating source, an infrared camera for recording sequences of thermograms and a digital information processing unit. Three neural network modules are used for automated data processing, each of which performs one of the tasks: defects detection and classification, determination of the defect depth and thickness. The software algorithms and user interface for interacting with system are programmed in the NI LabVIEW development environment.Experimental studies on samples made of multilayer fiberglass have shown a significant advantage of the developed system over using traditional methods for analyzing thermal testing data. The defect classification (determining the type) error on the test dataset was 15.7οΌ
. Developed system ensured determination of defect depth with a relative error of 3.2οΌ
, as well as the defect thickness with a relative error of 3.5οΌ
.ΠΠ° ΡΠ΅Π³ΠΎΠ΄Π½ΡΡΠ½ΠΈΠΉ Π΄Π΅Π½Ρ, Π²ΠΌΠ΅ΡΡΠ΅ Ρ ΡΠΎΡΡΠΎΠΌ ΡΠ΅ΠΌΠΏΠΎΠ² ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° ΠΏΠΎΠ²ΡΡΠ°ΡΡΡΡ ΡΠ°ΠΊΠΆΠ΅ ΠΈ ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΡ ΠΊ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠΈ. Π Π·Π°Π΄Π°ΡΠ°Ρ
Π΄Π΅ΡΠ΅ΠΊΡΠΎΡΠΊΠΎΠΏΠΈΠΈ ΠΈ Π΄Π΅ΡΠ΅ΠΊΡΠΎΠΌΠ΅ΡΡΠΈΠΈ ΠΌΠ½ΠΎΠ³ΠΎΡΠ»ΠΎΠΉΠ½ΡΡ
ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠ² ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΡΠΌ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ΅ΠΏΠ»ΠΎΠ²ΠΎΠ³ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π° Π½Π΅ΡΠ°Π·ΡΡΡΠ°ΡΡΠ΅Π³ΠΎ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ. Π ΡΠΎ ΠΆΠ΅ Π²ΡΠ΅ΠΌΡ, ΠΈΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠ°ΡΠΈΡ Π΄Π°Π½Π½ΡΡ
ΡΠ΅ΠΏΠ»ΠΎΠ²ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΡΡΠ»ΠΎΠΆΠ½Π΅Π½Π° ΡΡΠ΄ΠΎΠΌ ΡΠ°ΠΊΡΠΎΡΠΎΠ², ΡΡΠΎ Π΄Π΅Π»Π°Π΅Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
Π½Π΅ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠΌ. ΠΠΎΡΡΠΎΠΌΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΠΌ Π·Π°Π΄Π°Π½ΠΈΠ΅ΠΌ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠΎΠΈΡΠΊ Π½ΠΎΠ²ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠ΅ΠΏΠ»ΠΎΠ²ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΠΏΡΠΎΡΠ΅ΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΈ ΠΏΠΎΠ²ΡΡΠΈΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ². Π¦Π΅Π»ΡΡ ΡΡΠ°ΡΡΠΈ ΡΠ²Π»ΡΠ»ΠΎΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΠΉ Π² ΠΎΠ±Π»Π°ΡΡΠΈ Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π΄Π»Ρ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Π°ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠ΅ΠΏΠ»ΠΎΠ²ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΠΈΠ·Π΄Π΅Π»ΠΈΠΉ ΠΈΠ· ΠΌΠ½ΠΎΠ³ΠΎΡΠ»ΠΎΠΉΠ½ΡΡ
ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠ² ΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΡΠ΅ΠΏΠ»ΠΎΠ²ΠΎΠΉ Π΄Π΅ΡΠ΅ΠΊΡΠΎΡΠΊΠΎΠΏΠΈΠΈ ΠΈ Π΄Π΅ΡΠ΅ΠΊΡΠΎΠΌΠ΅ΡΡΠΈΠΈ ΡΠ°ΠΊΠΈΡ
ΠΈΠ·Π΄Π΅Π»ΠΈΠΉ.
ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠ°Ρ ΡΠΈΡΡΠ΅ΠΌΠ° ΡΠΎΡΡΠΎΠΈΡ ΠΈΠ· ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ° Π½Π°Π³ΡΠ΅Π²Π°, ΡΠ΅ΠΏΠ»ΠΎΠ²ΠΈΠ·ΠΎΡΠ° Π΄Π»Ρ ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠ΅ΠΉ ΡΠ΅ΡΠΌΠΎΠ³ΡΠ°ΠΌΠΌ ΠΈ Π±Π»ΠΎΠΊΠ° ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ. ΠΠ»Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π΄Π°Π½Π½ΡΡ
ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ ΡΡΠΈ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠ΅Π²ΡΡ
ΠΌΠΎΠ΄ΡΠ»Ρ, ΠΊΠ°ΠΆΠ΄ΡΠΉ ΠΈΠ· ΠΊΠΎΡΠΎΡΡΡ
Π²ΡΠΏΠΎΠ»Π½ΡΠ΅Ρ ΠΎΠ΄Π½Ρ ΠΈΠ· Π·Π°Π΄Π°Ρ: ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ ΠΈ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ Π΄Π΅ΡΠ΅ΠΊΡΠΎΠ², ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ Π³Π»ΡΠ±ΠΈΠ½Ρ Π·Π°Π»Π΅Π³Π°Π½ΠΈΡ Π΄Π΅ΡΠ΅ΠΊΡΠ° ΠΈ Π΅Π³ΠΎ ΡΠ°ΡΠΊΡΡΡΠΈΡ (ΡΠΎΠ»ΡΠΈΠ½Ρ). ΠΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΡΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ ΠΈ ΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ Ρ ΡΠΈΡΡΠ΅ΠΌΠΎΠΉ Π²ΡΠΏΠΎΠ»Π½Π΅Π½Ρ Π² ΡΡΠ΅Π΄Π΅ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ NI LabVIEW.
ΠΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π½Π° ΠΎΠ±ΡΠ°Π·ΡΠ°Ρ
ΠΈΠ· ΠΌΠ½ΠΎΠ³ΠΎΡΠ»ΠΎΠΉΠ½ΠΎΠ³ΠΎ ΡΡΠ΅ΠΊΠ»ΠΎΡΠ΅ΠΊΡΡΠΎΠ»ΠΈΡΠ° ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²ΠΎ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ Π½Π°Π΄ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΠΈΠΌΠΈ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΡΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
ΡΠ΅ΠΏΠ»ΠΎΠ²ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ. ΠΡΠΈΠ±ΠΊΠ° ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠΈΠΏΠ° (ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ) Π΄Π΅ΡΠ΅ΠΊΡΠ° Π½Π° ΡΠ΅ΡΡΠΎΠ²ΠΎΠΌ ΠΎΠ±ΡΠ°Π·ΡΠ΅ ΡΠΎΡΡΠ°Π²ΠΈΠ»Π° 15,7 οΌ
. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½Π°Ρ ΡΠΈΡΡΠ΅ΠΌΠ° ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ»Π° ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ Π³Π»ΡΠ±ΠΈΠ½Ρ Π΄Π΅ΡΠ΅ΠΊΡΠ° Ρ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΏΠΎΠ³ΡΠ΅ΡΠ½ΠΎΡΡΡΡ 3,2 οΌ
, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΎΠ»ΡΠΈΠ½Ρ Π΄Π΅ΡΠ΅ΠΊΡΠ° Ρ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΏΠΎΠ³ΡΠ΅ΡΠ½ΠΎΡΡΡΡ 3,5 οΌ