An overview of traditional and advanced methods to detect part defects in additive manufacturing processes

Abstract

Additive manufacturing (AM) or 3-dimensional (3D) printing processes have been adopted in several industrial sectors including aerospace, automotive, medical, architecture, arts and design, food, and construction for the past few decades due to their numerous advantages over other conventional subtractive manufacturing processes. However, some flaws and defects associated with 3D-printed components hinder its extensive adoption in industries. Therefore, real-time detection and elimination of these defects by analyzing the defects-causing process parameters is very important to obtain a defect-free final component. While global efforts are in progress to develop defect detection techniques with the rise of Industry 4.0, there is still a limited scope of comprehensive research that encapsulates various defect detection techniques in the AM sector on a global scale. Thus, this systematic review explores defects in parts manufactured via metallic and non-metallic AM processes. It covers traditional defect detection methods and extends to recent advanced machine learning (ML) and deep learning (DL) based techniques. The paper also delves into challenges associated with the implementation of ML and DL approaches for defect detection, providing a comprehensive understanding of the current state and future directions in AM research.