One of the 3D printers used by IMDEA Materials' Accelerated Materials Discovery research group in the recent study. Credit: Miguel Hernández del Valle, IMDEA Materials Institute
Imagine buying three identical 3D printers. Despite being the same brand, the same model and even having similar serial numbers, each machine may behave slightly differently. Over time and at scale, these differences can accumulate into significant manufacturing defects.
To address this issue, researchers from IMDEA Materials Institute, in collaboration with Lawrence Berkeley National Laboratory (U.S.), have developed an intelligent algorithm to detect these hidden "personalities," optimizing the reliability of automated manufacturing.
The system, published in Advanced Engineering Informatics, analyzes subtle operational differences between theoretically identical machines and selects the most appropriate optimization strategy.
In doing so, it reduces errors and improves the quality of the final product in parallel production systems, such as 3D printing farms.
