AEC CARE Process
Last updated
Last updated
The AEC process starts from high-definition 3D scans of the artwork with advanced devices that guarantees extremely fast digitalization and accuracy up to 0.05mm. Paintings, statues, books, jewels: each object has its own three-dimensionality and superficial characteristics that defines it uniquely. The scanning service is carried out by certified operators (ARTMEN) and is very fast. It requires an average of 2 minutes to scan a complete artwork. Thanks to that kind of technology, AEC gets the most complete and accurate reproduction of a piece of art in a specific moment: this is at the same time the best digital fingerprint and the most detailed condition report available. Together with the 3D file, all the information of the process is certified (the scanner ID, the operator identity, the owner identity, etc.) and reported in AEC portal: a specific algorithm creates an NFT for each artwork that contains all information that can be update in the time with more 3D scans or other digital documentation. 3D scan technology has been selected to guarantee the higher ratio between information and speed to support conservative activities of heritage. Anyway, as the next step up from 2D NFTs, 3D NFTs have become a significant breakthrough in the growth of the NFT marketplace, by integrating with gaming assets, metaverse building, and tokenising digital manufacturing assets with NFTs for better security and privacy. This double option seems to be very promising for the AEC process.
Given at least two 3D scans of the same artwork at two different times, specific algorithms developed by AEC can verify if any modification has occurred to the artwork. Such a service provides relevant results in various fields: insurance verification of the condition of an object before and after a move; detailed tracking of restoration activities; documentation of the desired or unwanted modification status of an object; support for restoration following damage; etc. The presence of an object in AEC platform can effectively support the recognition of the artwork itself in case of discovery after a theft or in case of duplication.
Future improvement, thanks to machine learning technologies and the increase of database size, will be able to offer valuable information for predictive maintenance.
