Scientists are building AI -driven robots to disassemble and recycle old mobile phones

In 2016, Apple announced that it had developed a recycling robot called Liam, which could disintegrate an iPhone within 11 seconds.

Six years have passed, and the machine has also changed for several generations. Apple is still reluctant to disclose how many iPhone mobile phones have recovered.

However, the potential impact of artificial intelligence robots on electronic waste recycling may soon become clear. This is due to a new research project. The project aims to develop tools driven by artificial intelligence and make robots from many different models of mobile phones from many different models. Harvest parts. If this technology can be commercialized, researchers hope that it can greatly improve the recycling of smartphones and other small portable electronic products.

科学家正在建造AI驱动的机器人来拆解回收旧手机

Although most of today’s electronic waste recychers are mostly traditional equipment such as CRT TVs, more and more small electronic products such as smartphones and tablets have begun to enter their facilities. This brings new challenges, because these devices are often difficult to disassemble and take time. Competers usually do not recover potential components like the motherboard, but remove the battery and then shatter the remaining part. In the process, the precious materials are lost, and all the energy of the manufacturing component needs to be consumed again to make new components.

For several years, scientists have been exploring whether artificial intelligence robots can simplify the recycling process and make the dead consumer electronics components recycling and reuse. In December, when the US Department of Energy provided a funding of US $ 445,000 to researchers from the National Laboratory of Aida, Bafafaro University, Aiho State University, and Electronic waste recyclable, to develop software to develop software to make robots It can automatically identify different types of smartphones online, remove batteries, and harvest various high -value components. At the end of the two -year research project, the team hopes to test the early versions of its technology in a facility of SunNKing. After that, it may seek additional funds to realize the commercialization of robotic smartphone recycling machines.

The work that these researchers are doing are essential for improving the sustainability of consumer electronics. These products contain precious metals and minerals, but today’s rough recycling process cannot be recycled. Indeed, applying robots and artificial intelligence to electronic waste is a considerable new idea, and there are not many practical examples that can explain its role. The most famous example is Apple’s recycling robot series, but only a few versions of these robots are used in the wild. They are only suitable for iPhone, and their impact on Apple’s overall electronic waste is also vague. A all -around robot that can process dozens of different types of smartphones has not been commercialized. The aim of new research projects shows that such robots can be developed at least.

Each research team will play a dominant role in different abilities of robot recycling. INL researchers will focus on the development of the method of disassembling batteries from smart phones. At the same time, researchers from the University of Bharro and the Aowl University University will determine higher -value components, such as circuit boards, cameras, and magnets. These components can be taken out of the dead electronic devices with the same robot, and these components can be taken out of the dead electronic devices, and they can Find or develop hardware for actual smartphone surgery.

The robot not only needs good hardware, but also needs software to quickly identify different types of mobile phones and query its internal anatomical structure. For this part of the project, researchers and Sunnking of Iowa State University will develop a database, including two -dimensional images and three -dimensional scanning data of various brands and models of smartphones. Using machine learning methods, the database will train the software to guide the robot to locate mobile phone batteries and high -value components and extract them.

Author: ArticleManager