Deep learning-based image segmentation for defect detection in additive manufacturing: an overview

Abstract

Additive manufacturing (AM) applications are rapidly expanding across multiple domains and are not limited to prototyping purposes. However, achieving flawless parts in medical, aerospace, and automotive applications is critical for the widespread adoption of AM in these industries. Since AM is a complex process consisting of multiple interdependent factors, deep learning (DL) approaches are adopted widely to correlate the AM process physics to the part quality. Typically, in AM processes, computer vision-based DL is performed by extracting the machine’s sensor data and layer-wise images through camera-based systems. This paper presents an overview of computer vision-assisted patch-wise defect localization and pixel-wise segmentation methods reported for AM processes to achieve error-free parts. In particular, these deep learning methods localize and segment defects in each layer, such as porosity, melt-pool regions, and spattering, during in situ processes. Further, knowledge of these defects can provide an in-depth understanding of fine-tuning optimal process parameters and part quality through real-time feedback. In addition to DL architectures to identify defects, we report on applications of DL extended to adjust the AM process variables in closed-loop feedback systems. Although several studies have investigated deploying closed-loop systems in AM for defect mitigation, specific challenges exist due to the relationship between inter-dependent process parameters and hardware constraints. We discuss potential opportunities to mitigate these challenges, including advanced segmentation algorithms, vision transformers, data diversity for improved performance, and predictive feedback approaches.