Surface Roughness Measurement of Additive Manufactured Parts Using Focus Variation Microscopy and Structured Light System

Surface roughness is a significant parameter when evaluating the quality of products in the additive manufacturing (AM) industry. AM parts are fabricated layer by layer, which is quite different from traditional formative or subtractive methods. A uniform feature can be obtained along the direction of the AM printhead movement on the surface of manufactured components, and a large waviness value can be found in the direction perpendicular to printhead movement. This unique characteristic differentiates additive manufactured parts from casted or machined parts in the way of measuring and defining surface roughness. Therefore, it is necessary to set up new standards to measure surface roughness of AM parts and analyze the variation in the topographical profile. The most widely used instruments for measuring surface roughness are profilometer and laser scanner, but they cannot generate 3D topographical surfaces in real-time. In this work, two non-contact optical methods based on Focus Variation Microscopy (FVM) and Structured Light System (SLS) were adopted to measure the surface topography of the target components. The FVM captures images of objects at different focus levels. By translating the object’s position based on focus profile, a 3D image is obtained by data fusion. The lab-made microscopic SLS was used to perform simultaneous whole surface scanning with the potential to achieve real-time 3D surface reconstruction. The two optical metrology systems generated two totally different point cloud data sets. Limited research has been conducted to verify whether the point cloud data sets generated from different optical systems are following the same distribution. In this paper, a statistical method was applied to test the difference between two systems. By using data analytics approaches for comparison analysis, it was found that surface roughness based on the FVM and the SLS systems has no significant difference from a data fusion point of view, though point cloud data generated were completely different in values. In addition, this paper provided a standard measurement approach for a real-time, non-contact method to estimate the surface roughness of AM parts. The two metrology techniques can be applied for in-situ real-time surface analysis and process planning for AM.