The IAIOA project develops state-of-the-art online inspection systems combined with AI tools

Ceit has taked part in the IAIOA project, which seeked to complete destructive quality control using non-destructive digital inspection systems to enable all parts to be controlled and results analysed in real time.

The manufacturing industry continues a constant search for excellence in terms of production and quality control, where a range of techniques and systems have been developed to ensure that end products meet established standards. However, in many cases, quality control remains challenging, especially with regard to processes such as thermal treatment, where inspections are carried out using selective destructive measures on one part per batch. This approach means that either all parts are disregarded from a batch owing to there being one faulty part, or that any production process errors are not detected in time.

The IAIOA (Artificial Intelligence applied to the Auxiliary Service Industry) project came into being, which takes the form of an innovative solution that seeks to complete destructive quality control using non-destructive digital systems, thus enabling all parts to be controlled and results analysed in real time. This initiative represents a significant step towards an improvement in competitiveness of small and medium-sized enterprises that offer auxiliary services to the manufacturing industry.

The project has been developed by a consortium made up of Ceit, TTT Group, Arrola, STT Systems, Zitu, Cidetec and Linq Case, a startup launched by Ceit. The project has also received the support of the Hazitek aid programme provided by the SPRI Group.

Innovation in terms of quality control

Specifically, the project has been foucused on the development of state-of-the-art online inspection, control and monitoring systems for processes, combined with artificial intelligence (AI) tools. These AI tools include automatic learning algorithms, pattern recognition, data analysis and prediction software. Monitoring and control systems in real time can be implemented when using this AI, which enables companies to identify and proactively deal with any production problems.

Consortium work has been focused on two key areas of research. On the one hand, research has been conducted into characterisation of hardness following quenching and tempering and, on the other, an approach has been taken to characterise surface hardness and layer thickness – both using magnetic techniques.