Internship offer l AI SOlution for Non-DEstructive Ultrasonic Testing of Critical Systems (SONDES)
- context
- IBISC Laboratory (Évry Univ.)
- Context
- Profile & Skills required
Internship offer l AI SOlution for Non-DEstructive Ultrasonic Testing of Critical Systems (SONDES)
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The IBISC Laboratory (Informatique, Bioinformatique, Systèmes Complexes EA 4526) is a laboratory of the University of Evry Paris-Saclay, structured into four research teams: AROBAS, COSMO, IRA2 and SIAM. A particular feature of the laboratory is its multi-disciplinary research and its location on two university sites: IBGBI and PELVOUX. This specificity is also reinforced by its attachment to two distinct scientific departments: Sciences Fondamentales et Applications (SFA) and Science et Technologie (ST). The IBISC laboratory is resolutely developing a strategy of collaboration and valorization of research with industry, as well as a research strategy open to the international arena. In 2023, the IBISC laboratory welcomed 23% of the UEVE's teaching and research staff, who hold a number of responsibilities at both the University of Evry (LMD, UFRs, IUT, VPs) and the University of Paris-Saclay (Graduate schools in Computer Science and Digital Sciences (ISN) and Engineering and Systems Sciences (SIS)).
Ultrasound is used for non-destructive testing (NDT) of industrial parts without damaging their integrity. This involves emitting acoustic waves and detecting their interaction with defects present in the part. The re-emitted waves (echo) are then converted, in real time, into a digital image of the defect thus located and characterized.This internship concerns the identification by deep neural network of possible defects on fasteners of a critical system. The identification of these defects will be based in particular on several ultrasonic (multimodal) measurements, carried out in situ by maintenance teams at various industrial partner sites. A promising approach is to first estimate the quality of an acquisition, as many factors can directly lead to poor analysis when it comes to determining the presence or absence of a defect, or render the acquisition uninformative for this task.
Objectives
The (SMART) objectives of this study are as follows:
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(main): to be able to automatically detect, in an unsupervised way, acquisitions of poor quality;
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to compare the results to make them consistent with those of experts;
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to improve existing models for supervised estimation of acquisition quality.
The person recruited will be in his/her 3rd year of engineering school or Master's degree. He/she will be able to understand and develop and/or adapt learning algorithms in an industrial context, index them and use them in an operational system to carry out the mission described above.