Effective maintenance practices are critical for ensuring the availability and reliability of equipment used in Research, Development, and Innovation (R&D) laboratories. Frequent failures and lack of standardized maintenance procedures often compromise operational continuity and project schedules. The use of world-class maintenance metrics provides a structured approach to measure equipment performance and identify areas for improvement. Academic laboratories, however, face challenges in systematically applying these indicators. This study proposes and applies a methodology for determining world-class maintenance metrics in 3D printers used at the Manufacturing Laboratory of the School of Technology, Amazonas State University. The mixed-method research combined literature review, field data collection, and the calculation of indicators such as MTBF, MTTR, availability, reliability, and OEE using MCM software. Data collection included equipment mapping, operational records, and interviews with technicians and operators. Results showed that the analyzed 3D printers presented an availability of 86.96% and OEE of 72%, both below world-class benchmarks of 90% and 85%, respectively. Failures were primarily associated with calibration inconsistencies, extruder blockages, and first-layer adhesion issues. These findings highlight the need for implementing preventive and predictive maintenance routines, standardizing critical procedures, and training operators to reduce downtime. The methodology demonstrates how structured maintenance metrics can support strategic asset management and strengthen operational continuity in R&D environments.