The large scale of mass manufacturing has its challenges when it comes to ensuring consistent quality in every product. One of them is the lack of providing real-time information on product quality. An efficient and effective way of conducting quality assurance within the mass manufacturing pipeline should be developed to provide transparency to the users on cost impact resulting from waste, delays, losses, etc. Our AI-powered system will hopefully create an easy-to-use system that can quickly determine product defects and their remedies in a highly dynamic manufacturing environment to ensure a smooth mass manufacturing process and happy customers.

What it does

We simply automate the humanly impossible task to identify defective products from assembly lines in real-time to ensure consistent product quality and be able to make informed decisions and act upon existing manufacturing issues.

How we built it

We orchestrated azure components in order to create the best image classifier to detect defects and visualize the relevant data in Power BI.

Challenges we ran into

We were unaware of exactly know how the features of the currently used software work under the hood which made us unable to create certain flexible changes such as implementing different classifier techniques and augmentations.

Accomplishments that we're proud of

We're able to make this system in less than two days.

What we learned

We learn how to hyper compress technical information into a high-level pitch presentation

What's next for Dago-AInspector

We will win automation.

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