Automatic FPI

The project has now at its end built a cell for automatic penetrant inspection of metallic components. The work was hit hard by the pandemic, and the completion of the work became difficult. The learning of AI has not been completed and so the technology has not proved viable yet. But the project has completed everything leading up to the validation. It now appears that IoT has fertile ground for a new product, which is now in long-term testing in production at GKN. There is also a willingness among the partners to look at the opportunities to start up a new business that will develop the main technology further. The technology is patent-pending. Unfortunately, the project is ended GKN early, but there is no room for further operation in a business hit by a severe crisis.

Partners IoT and Masmec are looking into the possibilities to bring the main result, automatic PT process, to commercialisation. In addition IoT are also looking at bringing a spin off to the market as well, the GUI now named Fama3D. The latter can become a product on the market quite quickly, where GAN is a test subject for implementation in the production environment. Several new positions will be generated if a new company is established in order to finalize the AutoFPI technology. The technology will with time also revolutionize the PT proceess. Fama3D will most likely end up as a product for IoT labs, and increase their turnover. Depending on the success rate, it may also cause new job positions at IoT. The Fama3D is a product that will increase the data capture and quality from production enabling analysis in a new way for a manufacturer. The existing functionality can be increased from only PT to other processes as well.

Fluorescent Penetrant Inspection (FPI) is a nondestructive test method which is very effective in detecting porosity, cracks, fractures, laps, seams and other flaws that are open to the surface of the test piece and may be caused by fatigue, impact, quenching, machining, grinding, forging, bursts, shrinkage or overload. FPI is currently used at GKN Aerospace Norway (GAN) on lots of machined parts, in particular for stator vanes, and involves a several stages process where the part is cleaned and then dipped in a fluorescent penetrant bath. Then, the penetrant is removed and a developer (dry powder) is sprayed for pulling the trapped penetrant material out of surface defects and spread it out on the surface of the part so it can be seen by an inspector. After the inspection, the part is cleaned. Some of these operations have been partially automated during the years at GAN, and today the NDI workshop is equipped with a crane for loading parts and dipping them into the penetrant tubs; however, the last part of the process is still purely visual and relies on the skills of the operator. Due to the very high number of vanes produced at GAN every week (some thousands), the human factor becomes significative in this process, since inspectors cannot ensure long-term consistency in their evaluations due to fatigue, needing breaks and rest periods to ensure high quality output which are required when inspecting jet engines parts. Besides that, inconsistency is also observed between the outputs of different inspectors. These factors highly contribute to increase the cost of the process and to undermine its reliability. The project proposes to develop and test a vision-based system for automating the FPI process, including artificial intelligence based algorithms developed for training the automatic inspection system and confirm its evaluation by comparing them with the operators' output