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In-situ Additive Manufacturing Process Monitoring with an Acoustic Technique: Clustering Performance Evaluation Using K-means Algorithm

This publication relates to work that WIU-QCML performed with collaborators at Iowa State University dealing with in-situ process monitoring of a metal additive manufacturing (AM) process.  The quality of AM parts is dependent on many factors such as energy input and material feedstock flow.  The development of suitable in-situ process monitoring technologies will enable the capability of early detection of process faults and build defects in AM processes. Within this study an in-situ acoustic monitoring system was developed and implemented in a Directed Energy Deposition AM process utilizing Ti-6Al-4V alloy.  Good correlation between acoustic signatures and process conditions ranging from optimal processing, low powder flow, and low laser power were observed, thus enabling high probability of detecting conditions that can result in build defects through the in-situ process monitoring of acoustic signatures.  This technology has the potential to be incorporated in AM processes in order to help assure the quality, safety, and integrity of 3D printed parts.

Taheri, L.W. Koester, T.A. Bigelow, E.J. Faierson, L.J. Bond, “In-situ Additive Manufacturing Process Monitoring with an Acoustic Technique: Clustering Performance Evaluation Using K-means Algorithm,” Journal of Manufacturing Science and Engineering, vol. 141, 2019.

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