You are currently viewing Implementing Convolutional Neural Networks for Product Quality Management

Implementing Convolutional Neural Networks for Product Quality Management

Ensuring optimum product quality is very important for most manufacturers, and utterly essential for many others who produce products pertaining to healthcare and overall human safety. To ascertain predictable and highest quality end product every single time, every manufacturer invests significant amount of attention, effort, and revenue for rapid and accurate product inspection during and especially at the end of the manufacturing process.

Infolob attests that with the recent advancements in Artificial Intelligence and Deep Learning, it is possible to minimizing human intervention and yet achieving human-level accuracy, and beyond, in terms of product quality assurance. Following is an Infolob use case:

Leveraging Deep Learning for Product Quality Assurance

The adjacent implementation demonstration of Convolutional Neural Networks, a flavor of Deep Neural Networks, is leveraged to understand, learn, and distinguish the correctly manufactured product (tiles) from the incorrect ones accurately, even during times when the defects are not present in the actual knowledge base.

Deep Learning gained its prominence being a black box for years and recent state-of-art methods are able to alter the existing neural networks in a way as to make the output self-explanatory. The proposed solution also made use of those advanced techniques in order to narrow down the region of defect in the product, so as to further make the inspection process even more interpretable and predictable.

Infolob’s Data Science Solution Framework
The proposed Infolob data science solution framework can be leveraged by manufacturers of any domain to achieve real time exhaustive product quality management via image data analysis using state-of-art deep learning techniques. This framework can not only be used to identify the defective manufactured components but also in narrowing down where the defect is present at scale, elegantly, in a cost-effective manner.

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