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Additive Manufacturing (AM)—commonly known as 3D printing—involves manufacturing processes that depend on a user-defined set of optimized parameters. Monitoring and control of these processes in real-time can help achieve operational stability and repeatability to produce high quality parts. By applying in-situ monitoring methods to AM procedures, defects in the printed parts can be detected.
In a new review in the Elsevier journal Materials & Design, Nikhil Gupta, professor of mechanical and aerospace engineering and director of the Composite Materials and Mechanics Laboratory at NYU Tandon, and Youssef AbouelNour, a doctoral student under Gupta's guidance, examine the application of both imaging and acoustic methods for the detection of sub-surface and internal defects.
The imaging methods consist of visual and thermal monitoring techniques, such as optical cameras, infrared (IR) cameras, and X-ray imaging. The data is abundant as numerous studies have been conducted proving the reliability of imaging methods in monitoring the printing process and build area, as well as detecting defects.
Acoustic methods rely on acoustic sensing technologies and signal processing methods to acquire and analyze acoustic signals, respectively. Raw acoustic emission signals can correlate to particular defect mechanisms using methods of feature extraction. In their review Gupta and AbouelNour discuss processing, representation and analysis of the acquired in-situ data from both imaging and acoustic methods. They also introduce ex-situ testing techniques as methods for verification of results gained from in-situ monitoring data.
Among their revelations: