Predicting Liquefier Tip Service Life in Industrial FDM: A Data-Driven Analytical Modeling Approach

The service life of model and support liquefier tips is an important consideration in maintaining extrusion stability, component quality, and maintenance efficiency in industrial fused deposition modeling (FDM) systems. This study develops manufacturer-data-driven nonlinear regression models to estimate liquefier-tip service life using service-life recommendations for Stratasys Titan, Vantage, and Fortus 400mc systems. The proposed framework incorporates normalized nozzle diameter as the geometric variable and a dimensionless material life factor to account for material-dependent differences in recommended service life. Logarithmic transformation and ordinary least-squares regression of the model-tip dataset yielded a nozzle-diameter exponent of 0.4309 and an intercept of 0.0377, indicating a positive but sublinear relationship between nozzle diameter and recommended service life. Separate predictive equations were formulated for model and support liquefier tips using ABS-M30 reference configurations: T10 (0.127 mm diameter; 10,160 cm 3 reference service life) and T12SR (0.178 mm diameter; 4,420 cm 3 reference service life), respectively. The material life factors determined for ABS-M30, PC-ABS, PC, and PPSF were 1.000, 2.299, 0.842, and 3.375, respectively. The model-tip formulation showed approximately ±6% deviation for the ABS-M30 reference configuration, whereas larger deviations for several support-tip configurations highlighted the limitations associated with the restricted availability of support-tip diameter data. The proposed equations provide a normalized analytical framework for estimating manufacturer-recommended service life across compatible nozzle configurations and materials, with potential application in usage-based maintenance planning. However, the evaluation is based on manufacturer data used for model development and calibration; therefore, independent experimental validation is required to establish predictive reliability under actual operating conditions.