Project R-15261

ADDIL_IRVA: Active Defects Detection In Line (Research)

The ADD-IL project targets the inline inspection of surface defects (and other small surface details) of (moving) parts in a production floor. In practice, comparable systems are often commercially viable only in case of high volume production, e.g. in automotive. Indeed, these high volumes justify the huge investment needed to make vision tunnels creating a fully controlled environment, hiring experts to train vision models, automation engineers to program robots, etc. The goal of this project is to prove that similar solutions can also be made accessible for high-mix production and deal intelligently with high production variability. There are various technical barriers to solve this problem. First, a high-mix environment is much less predictable and inherently has more variability. Second, the introduction of a new part should be sufficiently fast both in terms of design and commissioning. More detailed elaboration of concrete technical barriers is given in the next section. The project tackled these barriers in an end-to-end approach, starting from the creation of design tools to build optimal automated inspection systems and going up to the creation of state-of-the-art multi-image defect detection algorithms capable of reducing false positive/negative rates with 50% w.r.t. state-of-the-art single-image approaches. The project is building upon the results developed in the Flanders Make ADAVI ICON2 project and extending the challenges to deal with larger parts (> 1m³) and uncontrolled conditions in production floors (unknown pose and/or CAD-file, uncontrolled lighting, etc.). Typical use-cases that will be investigated are the inline inspection in painting production lines of unpainted and / or painted parts hanging from a trolley while the parts are prepared for painting and / or the paint is drying. As a challenge example, the glossy nature of the parts together with the variable ambient lighting makes it very difficult to design an automated off-the-shelf inspection setup having correct lighting to detect the different defects. As a result, inspection is typically performed by humans once the paint has dried. However, significant economic benefits can be obtained if this process could be performed in a more traceable and reliable way by an actuated (set of) optimized camera(s) system coupled to a performant deep learning computer vision algorithm. This is the main goal of ADD-IL. To accelerate the lead time of the E2E solution, the project will develop a standardized workflow integrating the above elements. As a result, compared to what is possible today, for the surface defect detection of a new part using existing inspection hardware: (i) the design will be completely offline (avoiding production downtime) and 3 times faster, (ii) the commissioning (online) will also be 3 times faster.

01 March 2025 - 28 February 2027