Researchers at IMDEA Materials Institute in Madrid, working with Lawrence Berkeley National Laboratory in the US, have developed an algorithm that detects hidden behavioral differences between supposedly identical 3D printers and automatically decides how to correct for them. The work, published in Advanced Engineering Informatics, targets a problem that grows worse as manufacturing scales. Rather than applying one blanket optimization strategy, the system profiles each machine individually, running a diagnostic assessment to build a performance signature for every printer and using statistical analysis to quantify differences. It then makes a routing decision: if machines are similar enough, it applies a shared optimization strategy; if it detects meaningful divergence, it switches to optimizing each machine individually. To validate the approach, the team ran a case study on three theoretically identical 3D printers, and statistical divergence measures showed each printer was operating in its own distinct output regime. The payoff was faster convergence and a substantial cut in errors in the weight of printed parts. The methodology extends beyond 3D printing to any high-throughput, parallelized experimentation that depends on nominally identical hardware behaving consistently. The study was carried out by Dr. Christina Schenk, Miguel Hernández del Valle, Luis Calero, Dr. Maciej Haranczyk of IMDEA Materials, and Dr. Marcus Noack of Berkeley Lab, supported by the MAD2D-CM.