The role of surface texture parameters in optical form error measurements accuracy and precision of additively manufactured components

Abstract Additively manufactured (AM) workpieces often undergo secondary machining operations to meet functional and geometric requirements, which can affect the optical properties of the surface, and thereby the performance of optical measurement systems. This study investigates which surface texture parameters most significantly influence the accuracy and precision of form error measurements obtained using a fringe projection (FP) system. Four AM workpieces, two as-built and two with secondary milling operations, were measured under five different exposure time settings ranging from 50 ms to 250 ms. Measurement accuracy was assessed by calculating form error bias relative to a reference surface acquired via contact measurements on a coordinate measuring machine (CMM). On the other hand, measurement precision was assessed by evaluating the uncertainty of the form error using Monte Carlo simulation. This uncertainty was propagated from the reference ground-truth surface acquired with a CMM, incorporating the standard uncertainty component of the FP measurement procedure through a regional Gaussian Random Field (rGRF) model. Results show that height (i.e. S z , S q , S p , S pk ) and volume surface texture parameters (i.e. V mp , V vc ) are strongly correlated with bias, while functional, hybrid and spatial parameters (i.e. S mr , S dq , S dr , S al ) impact the measurement uncertainty. Additionally, the proposed methodology demonstrated robustness across evaluated exposure time settings, as bias and expanded uncertainty variations remained below 1.3% of the mean form error value and 8.3% of the mean flatness value, respectively. This suggests that, for the surface conditions and optical setup considered in this study, exposure time does not need to be manually tuned. These findings support the integration of surface texture analysis into optical form error measurement workflows, improving the reliability and automation of FP-based inspection in industrial AM applications.