Additive Manufacturing: Equipment, Instrumentation and In-Situ Process Monitoring: Equipment, Instrumentation and In-Situ Process Monitoring II
Sponsored by: TMS Additive Manufacturing Committee
Program Organizers: Ulf Ackelid, Freemelt AB; Joy Gockel, Colorado School Of Mines; Sneha Prabha Narra, Carnegie Mellon University; Ola Harrysson, North Carolina State University
Wednesday 8:00 AM
October 12, 2022
Room: 304
Location: David L. Lawrence Convention Center
Session Chair: Joy Gockel, Colorado School Of Mines
8:00 AM Invited
Challenges and Opportunities for In-Situ Sensing during Electron Beam Powder Bed Fusion Additive Manufacturing: Tim Horn1; 1North Carolina State University
Electron Beam Powder Bed Fusion (EB-PBF) Additive Manufacturing has demonstrated applicability for a variety of materials, including high-temperature refractory metals and crack-prone nickel superalloys, as well as control over local microstructures. However, the benefits of rapid fabrication and microstructural control are overshadowed by long certification times associated with the prediction, measurement, and mitigation of defects at different physical scales. Therefore, in-situ techniques that yield a layer-by-layer spatial distribution of defects have generated significant interest. However, for EB-PBF, key challenges exist. Sensors must be resistant to metallization, heat, powder contamination, and x-ray damage. In this presentation, we will provide a review of the recent literature and industrial application of salient sensing technologies for EB-PBF with a highlight on recent work toward the collection and interpretation of in-situ backscattered electron data. We will discuss the capabilities and limitations of each approach in the context of recent advances in materials and applications.
8:40 AM
High-Speed Thermal Imaging of the Melt Pool in Laser Powder Bed Fusion: Guadalupe Quirarte1; Alexander Myers1; Syed Uddin1; Jack Beuth1; Jonathan Malen1; 1Carnegie Mellon University
Limited understanding of the temperature fields in metal-based additive manufacturing (AM) systems represents a challenge in process monitoring improvement. Experimental measurements of the melt pool temperature using conventional infrared imaging techniques or pyrometry lack the temporal and spatial resolution needed to determine melt pool temperature profiles. This project aims to develop an experimental method to measure melt pool and surrounding temperatures using a high-speed color camera (>50,000 frames per second). This method creates real-time thermal imaging AM tool leveraging the principle of two-color pyrometry, where each pixel acts as a two-color pyrometer. Dual-wavelength pyrometry is advantageous because it is less sensitive to melt pool emissivity and plume transmissivity. To validate the setup’s alignment with Planck’s blackbody theory and test the imaging system’s temperature measuring capability, a NIST blackbody source thermal calibration was conducted. The preliminary results are composed of melt pool temperatures from single bead scans of Ti-6Al-4V and 316L-SS.
9:00 AM
Using High-Speed Thermal Imaging to Understand Melt Pool Defects in Laser Powder Bed Fusion: Alexander Myers1; Guadalupe Quirarte1; Syed Uddin1; Jonathan Malen1; Jack Beuth1; 1Carnegie Mellon University
One major challenge associated with experimental temperature measurement methods in L-PBF is capturing temperature fields within and around melt pools. Using a high-speed color camera, we implement the two-color method of pyrometry with spatial and temporal resolutions on the order of microns and microseconds, respectively. Using this technique, our team monitors the melt pool temperature on two laser powder bed fusion (LPBF) metal-based additive manufacturing systems: the EOS M290 and Trumpf TruPrint 3000. Varying the camera’s parameters and filters in the optical path allows us to capture a wider temperature range. This system has been developed with the objective of capturing temperatures within the melt pool and near its solidification boundary. Imaging a variety of combinations of power and velocity from the process maps of both Ti-6Al-4V and 316L-SS, we associate the temperature fields to corresponding defect regimes with a favorable comparison to experimental melt pool width measurements.
9:20 AM
In-situ Process Monitoring, Synchronization and Mapping Laser Powder Bed Fusion Builds of Ti6Al4V: Samuel Hocker1; Brodan Richter1; Joseph Zalameda1; Wesley Tayon1; Erik Frankforter1; Peter Spaeth1; Andrew Kitahara2; 1NASA; 2National Institute of Aerospace
The use of in-situ process monitoring is of interest to lower the cost of inspection for the qualification of laser powder bed fusion (LPBF) parts. Precise monitoring of the LPBF-AM build process constitutes a multi-scale and multi-discipline task. There are several significant challenges to the in-situ approach: the synchronization of sensor signals to process steps, the physical interpretation and classification of sensor signals, managing very large datasets, and comparing the inputs with the observed monitoring signals. At NASA Langley Research Center, a configurable architecture additive testbed has been developed to monitor the build process with synchronized sensors. The philosophy and method adopted for the synchronization of the cameras with laser power & position during LPBF are described. The synchronized in-situ monitoring signals are compared with ex-situ nondestructive inspection and optical microscopy observations. Such comparisons permit a better understanding of how the sequential process actions of LPBF-AM can affect build quality.
9:40 AM
In-situ Sensor Feature Engineering for Process Development of Energy Conversion Materials: Joy Gockel1; John Middendorf2; Joe Walker2; Vijayabarathi Ponnambalam3; Saniya LeBlanc3; Tanvi Banerjee4; 1Colorado School of Mines; 2Open Additive, LLC; 3George Washington University; 4Wright State University
In-situ sensing provides the ability to monitor the additive manufacturing (AM) processes as the material is being fabricated. Process parameter development can be accelerated through the combined use of in-situ sensors, material characterization and machine learning. However, a critical challenge in the application of machine learning is the featurization of in-situ sensor signals that are related to the properties of interest. This work focuses on an analysis of in-situ sensor data during the fabrication of bismuth telluride samples using laser powder bed fusion. Samples are selected that represent the extremes of the characterized material properties of interest including porosity, Seebeck coefficient and electrical resistivity. In-situ sensor data from thermal tomography, a high-speed spatter camera and long wavelength infrared imaging are analyzed. The definition of sensor features that are physically and correlatively related to the properties of interest will enable future AM process optimization of energy conversion materials.
10:00 AM Break
10:20 AM
Characterization of Laser Powder Bed Fusion Internal and Surface Defects as a Foundation for In Situ Monitoring: Sean Dobson1; Ashley Paz y Puente1; 1University of Cincinnati
Laser powder bed fusion (L-PBF) additive manufacturing is currently being used, at a limited capacity, as a means of small volume production. Concerns of part quality have led to hesitance in adopting L-PBF as a mainstream method of manufacturing. In-process monitoring has been identified as a tool that can be harnessed to ensure process stability. Although some in-process monitoring studies are examining surface characteristics, many only use it for maintaining recoater health. This on-going study aims to understand the connection between the top surface of L-PBF parts and their internal periodic and material defects. Laser scanning confocal microscopy is used to generate 3-D height maps and obtain high resolution surface profiles for establishing a ground truth of the sample surface. Robust 2-D and 3-D serial sectioning methods are used to characterize the internal defects. Uncovering this connection will lay the foundation for a novel laser scanning in-process monitoring system.