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Spatiotemporal Monitoring of Melt-Pool Variations in Metal-Based Additive Manufacturing



Siqing Zhang, Yan Lu, Paul Witherell, Timothy Simpson, Soundar Kumara, Hui Yang


Additive manufacturing provides a higher level of flexibility to build customized products with complex geometries. However, AM is currently limited in its ability to ensure quality assurance and process repeatability. Advanced imaging provides unique opportunities for achieving real-time process monitoring and control. Existing studies about the characterization of melt pools mainly focus on the extraction and monitoring of low-dimensional features from imaging data. However, little has been done to model spatiotemporal interrelations among melt pools along the scanning path. The melt pool formation is not independent, but it can be influenced by the initial conditions and laser scanning history. Hence, this paper proposes a novel framework to model and monitor the characteristics of melt pools from the stream of melt pool imaging data. First, we align the melt pool imaging data based on the laser scanning direction and path, then model the stream of these imaging data as an order-3 tensor. Second, the uncorrelated multilinear principal component analysis is utilized to extract a sparse set of salient features, which are low-dimensional AM thermal profiles. Third, we develop an additive Gaussian process framework to model AM thermal profiles as the summation of a standard profile and a spatiotemporal deviation term to characterize melt pools. Finally, statistical tests are designed to identify different change scenarios in the AM process. Experimental results show that the proposed methodology has strong potentials for process monitoring and control of an LPBF-AM process.
IEEE Journal Of Robotics And Automation


Additive Manufacturing, Process Monitoring, Gaussian Process, Tensor Decomposition, Melt Pool, Imaging Data


Zhang, S. , Lu, Y. , Witherell, P. , Simpson, T. , Kumara, S. and Yang, H. (2022), Spatiotemporal Monitoring of Melt-Pool Variations in Metal-Based Additive Manufacturing, IEEE Journal Of Robotics And Automation, [online], (Accessed July 15, 2024)


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Created July 1, 2022, Updated November 29, 2022