TRansferability of experiential knowledge in industrial inspection by explainable AI
| Reference number | |
| Coordinator | Lunds universitet - Lunds Tekniska Högskola Inst f maskinvetenskaper |
| Funding from Vinnova | SEK 500 000 |
| Project duration | September 2022 - December 2023 |
| Status | Completed |
| Venture | Individual mobility and increased attraction value for research-based competence |
| Call | Mobility for innovation, learning and knowledge exchange 2022 |
Important results from the project
The project fulfilled the main goal to create the Explainable AI system which suggests the product quality based on the given cutting parameters. The system is based on the Computer Vision and AI techniques which enables the intelligent analysis of the drilling-induced defects. The collaboration between academic and industrial partners provided unique combination of expertise need to develop the AI solution. The results has been published and presented in national and international conferences.
Expected long term effects
As planned, project delivered a robust solution which enables the transferability of the experiential knowledge in industrial inspection. The developed AI solution is based on the results on the manual inspection and contained all unique knowledge from industrial experts regarding the product quality and defect formation. The AI solution as it is can be used and expended by the unexperienced users in both industrial R&D and academia. The AI system is passed validation and field tests which confirmed its reliability.
Approach and implementation
The delivered AI system, which provides the transferability of industrial experience is based on the set from both mechanical engineering and computer science field. The combination of different approaches to the data generation and processing provided efficient and highly accurate training of AI using experimental data. The Validation of the trained AI was done in the various environments which confirmed its versatility and stability.