The candidate will participate in a public funded research project (CompoSTLar – "Boosting the digital transformation of aviation supply chains for advanced composite aerostructures, an Horizon CL5-2024-D5-01-08 project" ) aiming at developing a holistic, AI-powered, and digitally integrated ecosystem for the advanced design, manufacturing, maintenance, and recycling of novel graphene-functionalized thermoplastic composite aerostructures, with a focus on zero-defect production, intelligent repair, and sustainable circular manufacturing.
This PhD focuses on AI model implementation for discovery of defects in composite material (CFRP) from ultrasonic measurements, by mean of automated data annotation from X-ray computed tomography data. The ultrasonic (US) data will be provided by project partners from US in-situ monitoring of the CFRP during automated tape layering.
The candidate will test and optimize new machine learning models including (but not restricted to) k-neighbours, support vector machines, ensemblings, and neural networks.
OTHER DETAILS
Ref. num. 2024-FS-R1-194
PhD candidate – AI-Driven Optimization of Ultrasonic Inspection in Composite Materials with Validation via Computed Tomography
IMDEA Materials Institute is a public research organization founded in 2007 by Madrid's regional government to carry out research of excellence in Material Science and Engineering by attracting talent from all over the world to work in an international and multidisciplinary environment. IMDEA Materials has grown rapidly since its foundation and currently includes more than 120 researchers from 22 nationalities and has become one of the leading research centers in materials in Europe which has received the María de Maeztu seal of excellence from the Spanish government. The research activities have been focused on the areas of materials for transport, energy, and health care and the Institute has state-of-the-art facilities for processing, characterization and simulation of advanced materials.
Description
The presence of defects in carbon fibre reinforced composite materials is a common manufacturing defect that could endanger the in-service performance of components. To ensure quality standards, the industry relies on ultrasonic non-destructive testing due to its cost and ease of use. However, to date, ultrasonic methods have not been able to assess porosity levels independently of other attributes such as pores morphology, size, and distribution, or even other types of defects. One possible solution is to address the problem of different defect types with ultrasonic propagation utilizing data-driven methodologies. The use of machine learning models could discover the hidden patterns of the interaction between the defects and the ultrasound wave. A possible solution relates features of the ultrasound wave with porosity characteristics obtained from three-dimensional (3D) reconstructed XCT volumes of carbon fiber composites via machine learning models. We tested some machine learning models that improve the prediction of pore volume fraction by using these data.
On the other hand, X-ray tomography (XCT) is by far the best technique for non-destructive damage assessment in composite materials, being able to identify in 3D manufacturing defects as well as damage generated upon external forces. Thus, part of the job is to take advantage of this non-destructive technique for the determination of defects (porosity, ply gaps, wrinkles, delamination, etc.) and implement automated classification of defects.
The work involves material testing, ultrasonic inspection, X-ray characterization, data analysis and programming. Therefore, a high interest in programming is mandatory. Some programming knowledge (preferable in python) is desirable, as well as in artificial intelligence techniques, data visualization, image analysis.
In this study, the candidate's main task is to develop AI-based model for defect identification in composite materials, from ultrasonic and XCT inspection data. In detail, the main research tasks include:
Collection of US and XCT data (assisted by technicians from IMDEA)
Implementation of data fusion techniques for both types of data
Implementation of AI-based models to identify microstructural features from US and XCT data.
Determination of best models and implementation into a software for in-line inspection.
Requirements
For PhD candidates, the position is most appropriate for recent master's graduates (or soon to graduate) in fields related to informatics, masters in AI, material science and engineering, or related disciplines with excellent academic credentials pursuing a PhD in computer science and AI.
Experience or knowledge in AI applied to XCT images or any other image is highly valuable. Close interactions with industrial stakeholders are expected; therefore, the ability to work as part of a team is essential.
Programming knowledge in any language, preferably Python for compatibility with already developed work will be valued.
Full proficiency in English, oral and written, is mandatory.
Interested candidates should submit their Curriculum Vitae, a brief cover letter addressing their motivation, as well as academic credentials.
Conditions
3.2 years contract with 1 year evaluation period.
Full-time contract including social security coverage.
The post will remain active and open until filled.
Expected start date: as soon as viable candidate is found.
Applications are processed upon reception. The position might be closed once ten working days have passed since publication, so we encourage early application.
The working language of the Institute is English. Full command of the English language is required in all positions.
WHAT YOU WILL FIND AT IMDEA :
Stimulating environment where you can grow professionally.
IMDEA Materials Institute is committed to equal opportunities, diversity and the promotion of a healthy work environment and work-life balance. Female applicants are encouraged to apply to our research and technical positions.
Besides on-the-job technical training, IMDEA Materials Institute is committed to training the Institute's scientists and staff in "soft" or transversal skills.
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