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And a few months ago , we read that major electric battery manufacturers from Scandinavia and Asia are planning investments totalling around 10 billion euros in new European gigafactories .

New facilities and the modernisation of existing sites to support electric vehicle production are prime opportunities to rethink tooling and processes to maximise efficiency , quality , and labour . News outlets reported that an electric-vehicle maker has removed over one hundred steps from its battery-making process , 52 pieces of equipment from the body shop and over 500 parts from the design of its flagship vehicles .

AI , particularly deep learning , thrives on data – volume , variety and velocity of superior quality data is key to training and testing deep learning models , so they deliver the outcomes expected when deployed in real life .

Donato Montanari , General Manager and VP Machine Vision , Zebra Technologies

The result of rethinking its processes has been a 35 % reduction in the cost of materials for vans and savings of similar scale for its other vehicles .

We know that when it comes to developing new , existing factories , and procuring solutions , there is a site level focus with input and sign-off shared at site and corporate level .

Experience and time available can vary between teams and sites , which can create silos and make achieving data quality more challenging . Data needs to be stored , annotated , and used for training models , with other data sets needed for model testing . It makes no sense for company data in these cases to remain siloed , to the detriment of better training for machine vision models .

But there is always the possibility of different sites using different solutions for similar workflows , and the risk of expertise and data not being shared across sites , including when using newer AI-powered solutions where data quality is essential . This can also be true for visual inspection teams using machine vision systems for quality and compliance .

Among machine vision leaders in the automotive industry , almost 20 % in Germany and the UK say their Artificial Intelligence machine vision could be working better or doing more , according to a Zebra report looking at AI machine vision in the automotive industry .

Are there ways that technologies like deep learning machine vision could be better deployed and used ? Could we balance discussions about cloud security and governance with opportunities to leverage it for high value workflows like testing and quality control with deep learning machine vision , and new computing and collaboration resources for engineers and data scientists ?

Machine vision teams across manufacturing industries need new ways to leverage deep learning machine vision , which should include using the cloud .

A deep learning neural network should be exposed to as much variation as possible , including different hours and days of production . A mix of random dates in the dataset is needed which may be inconvenient as it requires data capture over a period , unless using a platform for simulating training data , but it is crucial for training a robust model .

Industrial processes are also subject to various environmental factors , such as changing ambient light , materials with slight variations , vibrations , noise ,

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