Quality control is the key for the widespread adoption of metal additive manufacturing (AM). However, online quality estimation is challenging because high-frequency stream data derived from in situ metrology have to be processed in a timely manner to figure out the complicated interactions among material, machine, and part. To tackle such issue, this article proposes an intelligent AM metrology (IAMM) architecture to decouple and evaluate quality variations caused by material properties, machine issues, and process parameters when building an AM part. The IAMM architecture can also estimate the online layer-to-layer quality (e.g., roughness and density of an AM part) by applying the robust parameters derived from the uniform design (UD) method via the enhanced automatic virtual metrology technology in a parallel computing environment, as soon as the microfeatures of melt-pools and the macrofeatures of each layer are extracted. In addition, the associated indices and features in the IAMM architecture can also be used to evaluate the defects caused by machine issues in the given process parameters. The results of case studies validate the applicability of the IAMM architecture and show that the proposed estimation models are prospective for future closed-loop control in an AM process. Note to Practitioners - The proposed intelligent AM metrology (IAMM) architecture can be implemented modularly in a metal additive manufacturing (AM) machine that possesses the capability of estimating the coating and printing qualities of the AM process by extracting features from the optical data of the melt pools and chamber layer by layer. The estimated indices and qualities can be derived online using parallel computation to timely diagnose the machine issues and control the process of the next layer. Hence, when an AM machine is equipped with the IAMM architecture, it can efficiently manufacture a 3-D part with reduced defects in real time.
|頁（從 - 到）||269-281|
|期刊||IEEE Transactions on Automation Science and Engineering|
|出版狀態||Published - 2021 1月|
All Science Journal Classification (ASJC) codes