The KITT4SME Webinar “AI applied to quality control of fasteners – the Dimac experience, state of the art and perspectives for the future” took place on Monday, December 11th, with the participation of 62 attendees.
While AI excels in selecting surface defects undetectable by traditional methods, its implementation poses challenges such as reliability, performance, productivity, and flexibility. Addressing these challenges includes managing the training of neural networks without adequate samples, impacting false rejection rates and selection rates.
This webinar delved into the efficient application of artificial intelligence (AI) tools in sorting machines for quality control of fasteners and small parts in large-series production.
The KITT4SME Dimac and R2M pilot was presented, and it was focused on testing augmented reality as tool to expand the detective image database for AI training
The result broadened AI’s scope to previously deemed impossible contexts, approaching the performance of traditional computer vision algorithms. During the webinar both the end user adoption perspective and the technology provider deployment of this AI solution were presented.
Thank you for participating in the KITT4SME webinar “AI applied to quality control of fasteners – the Dimac experience, state of the art and perspectives for the future”.
If you missed the event, please review the following documents
If you have any comments or feedback regarding the AI solution presented during the webinar, or if you have any further questions related to the content covered, please contact us
