Machine learning enhanced high dynamic range fringe projection profilometry for in-situ layer-wise surface topography measurement during LPBF additive manufacturing

Abstract

Fringe Projection Profilometry (FPP) is a cost-effective and non-destructive method, typically used for measuring finer features and reconstructing 3D topography of objects. However, to use the FPP method for measuring the dynamic topography of powder bed and printed layers during Laser Powder Bed Fusion (LPBF) based additive manufacturing (AM) process, unique challenges exist due to the varying material properties and ambient conditions in the build chamber. In this work, we aim to enhance the discernibility, accuracy, and resolution of FPP in the specific application scenario of measuring layer-wise surface topography during LPBF AM by integrating our recently developed LPBF-specific FPP sensing model that features localized sensor calibration and Fourier filter-aided unwrapping with an equipment-based High dynamic range (HDR) method and machine learning (ML) aided FPP data analysis. First, a projector based HDR method is applied to mitigate the shadowing and intensity saturation problems by projecting sinusoidal fringe patterns of varying intensities. Secondly, a ML framework is developed to improve the surface topography measurement accuracy (RMSE from 10.57 μm to 7.49 μm or even 4.35 μm for directly measurable points) and enhance resolution that is currently subjected to hardware limitations (from 38 μm to 5 μm laterally and from 10 μm to 1 μm vertically). Several different types of candidate neural networks (NNs) are trained and tested using the in-situ FPP measurement data and ex-situ standard optical microscopy characterization data. Multiple NN-based models are resulted and compared in terms of their ability to enhance the accuracy and resolution of FPP's end-result (height measurement). By selecting the best-performance NN enabled image super resolution model, the proposed ML integrated HDR FPP method is expected to measure the surface topography of printed layers during LPBF-AM more capably and efficiently, thus advancing the existing state-of-the-art methods towards the desired online inspection of LPBF print defects.