Laser powder-bed fusion (L-PBF) is an additive manufacturing (AM) process that enables fabrication of functional metal parts with near-net-shape geometries. The drawback to L-PBF is its lack of precision as well as the formation of defects due to process randomness and irregularities associated with laser powder fusion. Over the past two decades much research has been conducted to control laser powder fusion in order to provide parts of higher quality.

This paper addresses online quality monitoring in AM by in-situ automated visual inspection of each layer which is aimed to geometric objects and defects from high-resolution visual images. A scheme for online defect detection system is presented that consists of three levels of processing: low-level, intermediate-level, and high-level processing. Each level is described and appropriately divided to several stages, when insightful. Techniques that are feasible in each level for successful defect detection and classification are identified and described. Requirements and specifications of the measurement data to achieve desired performance of the online defect detection system are stated.

Image processing algorithms are developed for first level of processing and implemented for segmentation of geometric objects. Due to the large variation of intensities within the powder region and fused regions, and also the non-multi-modal nature of the image, the basic segmentation algorithms such as thresholding do not produce appropriate results. In this work, morphological operations are effectively designed and implemented following thresholding to achieve the desired object segmentation. Examples of implementations are given. The paper provides the results of object segmentation which is the initial stage of development of an in-situ automated visual inspection for L-PBF process.

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