Spain's IMDEA Materials Institute, in collaboration with the Lawrence Berkeley National Laboratory (LBNL), has developed an AI algorithm that can detect performance variations among theoretically identical machines and select the optimal optimization strategy. The algorithm first performs a diagnostic assessment of each printer to build an individual performance profile, then quantifies inter-device differences through statistical analysis—applying a joint optimization strategy for similar devices and an independent optimization strategy for those with significant discrepancies. The team validated the method using three theoretically identical 3D printers: the algorithm detected measurable performance differences, confirming that each printer required independent optimization rather than a uniform approach. After adopting independent optimization, print weight errors were substantially reduced and system convergence was faster; the uniform strategy failed to correct individual deviations. The approach is also applicable to high-throughput experiment fields such as new material discovery, chemical synthesis, and sensor calibration.