Monitoring process stability in gas metal arc additive manufacturing based on arc sensing

Abstract

Gas metal arc additive manufacturing (GMA AM) is a promising technology for fabricating large-size metal parts. Nevertheless, process monitoring to achieve repeatable and reliable depositions is yet to be completely realized. This study aims to monitor the process stability, i.e., nozzle to top layer distance (NTLD), in GMA AM based on arc sensing. Relationships between arc signals and the NTLD in three droplet transfer modes are studied and discussed, and four filtering algorithms are applied to remove noises in arc signals. The arc current is superior to the arc voltage to characterize the deposition height and is most sensitive to the NTLD in the globular transfer mode. The adaptive least mean square method can eliminate noises in arc current more effectively than the median average, first-order lag, and Kalman algorithms. Validation tests indicate that the arc current sensing can detect the NTLD in GMA AM.