Brief Paper: Process Monitoring and Fault Detection in Laser Powder Bed Fusion Using In-Situ Acoustic Emissions

Abstract

Abstract This work concerns process monitoring in the laser powder bed fusion additive manufacturing process. In this work, we developed and applied a novel in-situ solution for process stability monitoring and flaw detection using acoustic emission sensing. Current process monitoring methods in laser powder bed fusion only focus on the top surface of the deposition process, using an array of sensors to capture data on a layer-by-layer basis. Common sensors used for in-situ monitoring of the laser powder bed fusion process are optical, infrared, and highspeed imaging cameras along with pyrometers and photodiodes. A critical flaw with traditional top surface monitoring methodologies is that they are unable to reliably monitor the subsurface phenomena that occur in the laser powder bed fusion process. These subsurface effects are caused by the meltpool penetrating multiple layers below the top surface, leading to the re-solidification of the microstructure and potentially generating keyhole porosity. By only monitoring the top surface of the laser powder bed fusion process, the meltpool depth aspects and effects are ignored. To overcome the limitations of current in-situ monitoring of subsurface effects, this work utilizes four passive acoustic emission sensors attached to the build plate. These acoustic emission sensors monitor the energy emissions generated from the surface-level laser material interactions. Moreover, the acoustic emission signals are capable of traveling through the previously deposited layers, through the build plate, and to the sensors. Therefore, the acoustic waveform generated by the laser can capture process phenomena ranging from the crystallographic level to the macro-scale layer level which are at the root of flaw formation inside the deposited part. Hence, acoustic emission monitoring has the ability to monitor the subsurface effects in the laser powder bed fusion process. To monitor and analyze this acoustic waveform, novel wavelet-based decomposition is combined with heterogeneous sensor fusion to not only capture the acoustic waveform in time, but also in locational space on the build plate. Locational acoustic emission data enables the ability to determine the source of the generated acoustic waveform which is advantageous when the location of flaws is desired. This extracted spatially placed acoustic waveform data is able to detect the effect of processing parameters with a statistical fidelity of 99%. The proposed locational acoustic waveform monitoring method correlates to the resulting surface roughness of manufactured samples with a fidelity of 86%. Additionally, we show that acoustic waveform monitoring detects the onset of part failure, recoater crashes, and warpage prior a priori to the actual failure point.