# A Review of In Situ Quality Monitoring in Additive Manufacturing Using Acoustic Emission Technology

## TL;DR

This review explores how acoustic emission technology can monitor and improve the quality of metal additive manufacturing in real time.

## Contribution

The paper systematically categorizes AE applications in MAM into hardware setup, parametric characterization, and intelligent monitoring.

## Key findings

- AE systems can detect process anomalies like spattering and melting instability during MAM.
- AE features correlate with defects such as porosity and cracking in manufactured parts.
- Integrating AE with feedback control systems enables real-time quality assurance in MAM.

## Abstract

Additive manufacturing (AM) has emerged as a pivotal technology in component fabrication, renowned for its capabilities in freeform fabrication, material efficiency, and integrated design-to-manufacturing processes. As a critical branch of AM, metal additive manufacturing (MAM) has garnered significant attention for producing metal parts. However, process anomalies during MAM can pose safety risks, while internal defects in as-built parts detrimentally affect their service performance. These concerns underscore the necessity for robust in-process monitoring of both the MAM process and the quality of the resulting components. This review first delineates common MAM techniques and popular in-process monitoring methods. It then elaborates on the fundamental principles of acoustic emission (AE), including the configuration of AE systems and methods for extracting characteristic AE parameters. The core of the review synthesizes applications of AE technology in MAM, categorizing them into three key aspects: (1) hardware setup, which involves a comparative analysis of sensor selection, mounting strategies, and noise suppression techniques; (2) parametric characterization, which establishes correlations between AE features and process dynamics (e.g., process parameter deviations, spattering, melting/pool stability) as well as defect formation (e.g., porosity and cracking); and (3) intelligent monitoring, which focuses on the development of classification models and the integration of feedback control systems. By providing a systematic overview, this review aims to highlight the potential of AE as a powerful tool for real-time quality assurance in MAM.