When people write about of the future of manufacturing, there’s a myriad of initialisms that get thrown around: AI, IIOT, AR, VR, you get the idea. These types of advanced technologies are the way forward for manufacturing businesses, and in this post, we’re looking into the latest developments, as well as an initialism in the context of climate change: DAC.

Cognitive technologies in manufacturing

The first article we’re digging into is by Len Calderone and was published on ManufacturingTomorrow. Calderone describes cognitive technologies, which are methods that help the completion of tasks that require human intelligence and skills. It uses technologies similar to AI, but the difference is that AI tells the manufacturer which actions to take, whereas cognitive technologies just supply the information and leave the decision to a person. Data is mainly being supplied by IOT, and then analyzed by AI or another method, but AI can’t do all of the analytics, which is why cognitive technologies are a necessity to keep acting on the incoming stream of data. It’s another piece in the technology puzzle. Go here to see what space cognitive technology can occupy.

Developments in carbon capture

It’s becoming clear that carbon emissions reduction is insufficient to combat climate change, so carbon capture is on the rise. We’re happy to report that it’s gaining more traction. Andrew Wade talks about the latest developments on The Engineer. He digs more deeply into direct air capture (DAC) which is a technological alternative to natural methods of capture. A Swiss company, Climeworks, uses a type of fan with a filter in it, which is then heated to convert the CO₂ into a purified gas that has several applications. Storing the captured carbon is another link in the chain that Climeworks is working on. This is just one example of many that the article discusses, so go check out the rest!

AI in discrete manufacturing

Smart Industry focused on AI in discrete manufacturing – the production of distinct items – where AI can have uses beyond what was previously imagined. Notably the increased labor compared to output creates a challenge where AI can offer a solution. Tony DeLima describes how each finished assembly can be unique, so there’s a lot more factors and variables that have to be controlled. Additionally, making predictions is an area where AI continues to shine and will continue to grow. Think about predictive maintenance for example, or predicting future demand, inventory shortages, and decisions that will need to be made. The options are limitless, but it can be rough trying to implement AI in the right way. Any discrete manufacturers who want to find out how they can do so, go here!

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