Using Artificial Intelligence to Detect Defects on Surfaces Better and Faster

Why don't we just use correct surfaces as training?

Conspicuity detection algorithms are not yet able to handle the variation caused by complex textures and focus on the often minor changes caused by surface defects.
 

Can we use artificially generated images to support AI?

Yes, this is also the latest state of development. There are a lot of open questions, and we are addressing many of them with our SynosIs software. Our core motivation here is that no two produced objects are exactly alike, and the image simulation pipeline needs to be able to reproduce that exact idea. It should also retain control over the key production parameters.

Approaching Reality Automatically With AI and Building up an Image Data Portfolio

In this interdisciplinary project, methods from physics, mathematics and computer science are combined to obtain synthetic images of typical defects on metallic surfaces – at a level of realism not currently achieved. This image data is openly accessible and can be used for training and validating AI models. This simplifies and accelerates the development of AI models for optical surface inspection.

For the first time, the image data portfolio will provide guaranteed correct and objectively annotated defect images. Furthermore, this synthetic data enables an objective quantitative comparison of different solutions as well as the prediction of the probability of detection (POD) of surface defects depending on defect, component and inspection system.

Our Expertise: Define Defects, Classify, Train AI

In the initial stage, our ITWM experts, in close cooperation with Fraunhofer IOF, design two free-form specimens with complex geometry for validation. The defects to be introduced are defined in such a way that, on the one hand, typical defect classes such as impact points, scratches, and (oil) contamination are represented, and, on the other hand, the manufacturing effort remains reasonable. In the next step, the researchers train the AI-based inspection system using synthetic image data. Another task is to validate the inspection solution and simulate a representative portfolio of synthetic image data in collaboration with the Rhineland-Palatinate Technical University Kaiserslautern-Landau (RPTU).

This project has a clear application scenario, but is still designed as a basic research project because we are developing new methods in all three disciplines. In addition to data synthesis, the project addresses the topic of »parameters for measuring suitability, quality or bias-freeness of the data«, since suitability, quality and bias freedom of the data are continuously checked and guaranteed for both the defect-free surfaces and the defects to be introduced.

Current Status in the »SynosIs« Project

In the first year of »SynosIs«, we as a team realized that there are too few controlled data sets and modeling processes for physical surfaces. We therefore developed a process chain in the project to meet the need. In doing so, the team designed a dataset of ten aluminum test specimens that focuses on difficult surfaces that contain textures created by spiral milling, parallel milling, or sandblasting.

Based on topology measurements of the manufactured surfaces, we created models capable of copying the appearance of the surfaces using stochastic geometry modeling techniques. In this way, we digitally create an arbitrary number of surfaces that nevertheless have different appearances while maintaining the same properties. Moreover, we thus control the models by production-related parameters that influence the final surface.  

The surface generation models developed can be used again and again and combined to apply to any 3D model.

In the second year of the project, the team will focus on:

• modeling of defects
• creating public data sets
• investigating the benefits and challenges of simulation-based image processing

Our Research Partners

• Fraunhofer Institute for Applied Optics and Precision Engineering IOF(Dr. Marcus Trost)
• RPTU Kaiserslautern (Prof. Dr. Claudia Redenbach)
• RPTU Kaiserslautern  (Prof. Dr. Hans Hagen)
   

Project Duration and Funding

The project is scheduled to run for two years from 01.10.2021 to 30.09.2023. It is funded by the German Federal Ministry of Education and Research (BMBF).