In-Situ monitoring and modeling of metal additive manufacturing powder bed fusion

One of the major challenges in metal additive manufacturing is developing in-situ sensing and feedback control capabilities to eliminate build errors and allow qualified part creation without the need for costly and destructive external testing. Previously, many groups have focused on high fidelity numerical modeling and true temperature thermal imaging systems. These approaches require large computational resources or costly hardware that requires complex calibration and are difficult to integrate into commercial systems. In addition, due to the rapid change in the state of the material as well as its surface properties, getting true temperature is complicated and difficult. Here, we describe a different approach where we implement a low cost thermal imaging solution allowing for relative temperature measurements sufficient for detecting unwanted process variability. We match this with a faster than real time qualitative model that allows the process to be rapidly modeled during the build. The hope is to combine these two, allowing for the detection of anomalies in real time, enabling corrective action to potentially be taken, or parts to be stopped immediately after the error, saving material and time. Here we describe our sensor setup, its costs and abilities. We also show the ability to detect in real time unwanted process deviations. We also show that the output of our high speed model agrees qualitatively with experimental results. These results lay the groundwork for our vision of an integrated feedback and control scheme that combines low cost, easy to use sensors and fast modeling for process deviation monitoring.