Online in situ detection of deposited height deviation during additive manufacturing

Abstract

Deposited height deviation (DHD) of printed layers is a common surface defect that restricts vertical printing accuracy during the additive manufacturing process. The accumulation of DHD layer by layer inevitably leads to the failure of subsequent additive manufacturing tasks. Therefore, accurate online measurement of DHD is crucial. This study proposed a novel amplification computer-vision measurement (ACVM) method that effectively utilizes both melt pool images and temperature information, achieving a DHD detection sensitivity of approximately 9.96 μm. Theoretical connections between image features and DHD, as well as the theoretical associations between instantaneous temperature characteristic and DHD, have been systematically deduced. Based on these two theoretical relationships, DHD can be accurately and synchronously detected directly through the positions of image features and temperature. A single-camera dual-channel multi-signal detection (SDMD) system was developed and implemented within a laser-engineered net shaping (LENS) additive manufacturing system. Subsequently, an online measuring and verification experiment was designed to assess the height deviation of thin-walled structural parts. The experimental results demonstrated that the ACVM method provided an early response to DHD. The method exhibits significant technical application value in quality control in future.