The IntelSight color-codes items so that human pickers can more easily sort them by hand. | Courtesy of Neatco Engineering Services

Neatco Engineering Services is using a combination of human and machine learning to improve e-scrap sorting. 

Taking sortation fully automatic is expensive, a press release noted, and retaining a human labor force skilled in sorting is difficult. 

Neatco’s AI-vision Controller, IntelSight, uses deep learning networks to color-code items based on how they should be sorted. Instead of the machine moving the objects directly, a human picker puts them in the correct bin after they have been identified.

“The advantages of this intermediate solution are a drastic reduction in training required to perform complex sorting tasks,” the press release noted.

This method can be particularly useful for batteries and printed circuit boards. 

IntelSight can also perform data collection, analytics, tracking and reporting, which will be useful as more extended producer responsibility laws are passed that require OEMs to track recycling data. 

Neatco noted that its technology is also useful for OEM detection, identifying devices with embedded batteries, hazard detection prior to shredding, and identifying contamination in aluminum and ferrous lines. 

The system’s proprietary AI-Assisted Sorting technology is scalable and compatible with common machine vision standards, the press release stated. It’s user-friendly, meaning it’s easy to install, train, maintain and operate without the need for special AI skills. 

 

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