A feature-based physics-constrained active dictionary learning scheme for image-based additive manufacturing process monitoring

Abstract

Compressed sensing is a new technique for data acquisition, which reconstructs the original signal from a small number of measurements. The performance of compressed sensing depends on the design of measurement and basis matrices. To improve the reconstruction accuracy, dictionary learning methods have been developed to customize basis matrices for specific signals. However, it may be difficult to obtain the theoretically optimal results because of the restrictions in physical realization such as the sensor placement and accessibility in the working environment. In this paper, a feature-based physics-constrained active dictionary learning (FB-PCADL) framework is proposed to solve the compression and classification problems simultaneously for image-based additive manufacturing process monitoring. A new active dictionary learning strategy is developed to optimize the measurement matrix, where the reconstruction errors are gradually reduced with the new measurements taken adaptively at the positions where the largest reconstruction errors occur. In addition, important features in images are identified and used to enhance the classification accuracy. The FB-PCADL framework is demonstrated with thermal images in monitoring fused filament fabrication process and optical images in monitoring laser powder bed fusion process.