Abstract

To address the future challenges in quality monitoring of metal laser powder bed fusion, a novel method is proposed to detect topographic anomalies on layer surfaces, which may appear during the manufacturing process. The method combines light scattering and principal component analysis. Scattering patterns, experimentally generated from real surfaces representative of in-control processes and encoded as digital images, are collected and used to build a reference set, which is then further populated by simulation. Principal component analysis is then applied to the set. A certain number of principal components is extracted and used to define a transform to map any scattering pattern to principal component space. Using the created transform, any new scattering pattern can be transformed to principal component space and then back into the original space (reconstruction), with some reconstruction error. The error is expected to be low if a pattern from the reference set is processed. However, if a different pattern is processed, e.g. generated by an out-of-control layer topography, then the reconstruction error is larger. In this work, a layer monitoring system is proposed, capable of detecting out-of-control topographies through observation of the reconstruction error. The system was implemented and experimentally validated through application to a selected test case.