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How to apply AI in the manufacturing sector.

 

   Since the advent of the internet, the world’s leading manufacturing plants have significantly digitized their operations. Today, terabytes of data streams are generated from virtually every piece of equipment inside a factory, giving companies more information than they know what to do with it.

Unfortunately, many companies lack the resources to convert this information into possible decisions to reduce costs and increase production efficiency. To get there, companies are forced to need Artificial Intelligence.

* What drives the urgency to apply AI?

High revenue volatility • Constantly looking for ways to save costs • Short lead times • Strengthening regulations and testing processes • Learning and adapting inside the factories • Production capacity and demand supply chain demand • Increased demand for small and/or customized goods.

* The factories of the future can…
– Detect defects throughout the production process.
– Implement predictive maintenance to reduce downtime.
– Verify that complex products such as microchips are perfectly manufactured.
– Lowers the cost of small batch or one-time orders, allowing for greater customization.
– Improve employee satisfaction by transferring simple, repetitive tasks to machines.

* Key applications for AI in the manufacturing industry.
– Defect detection.
Today, many assembly lines do not have the system or technology to identify defects on the production line. Even the ones available on site are very basic, requiring skilled engineers to build and hard-code algorithms to distinguish between defective parts and perfected parts. Most of these systems are still unable to learn or tolerate new information on their own, resulting in a multitude of false positives (about the condition of goods), which must then be manually checked by on-site personnel.

– Quality Assurance (QA).

The manufacturing sector requires a high level of attention to detail, especially for electronic goods. Historically, QA has been a manual job, requiring a highly skilled engineer to ensure that the electronics and microprocessors are manufactured correctly and that all of its circuit boards are configured. correct figure.

Today, image processing algorithms can automatically confirm whether a product is perfectly manufactured. By installing cameras at key points along the factory floor, this arrangement can happen automatically and in real time.

– Integrated chain.

Today, the majority of devices used by manufacturers send huge amounts of data to the cloud. Unfortunately, this information tends to be overlooked and not put together well.

Getting an overall picture of your operations requires several different dashboards and an expert in the field to understand them all.

By creating an integrated app that pulls data from the wide range of IoT connected devices you’re using, it can help ensure that you’re getting a “magic eye” view of your activity. factory.

– Optimization of the assembly line .

Furthermore, by layering Artificial Intelligence into your IoT ecosystem, this rich data block, you can create many types of automation. For example, when the equipment operator shows signs of fatigue, the supervisor will receive a notification. When a piece of equipment fails, the system can automatically trigger backup plans or other reorganization activities.

– Creative design .
Here’s how it works: a designer or an engineer feeds design goals into generalized design algorithms. These algorithms then discover all possible permutations of a solution and generate design alternatives. Finally, it uses machine learning to test each iteration and improve it.

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