Session: 15-01-01: ASME International Undergraduate Research and Design Exposition

Paper Number: 151903

151903 - Investigation of Mechanical Properties of Waam Er120s Steel Components 

Wire Arc Additive Manufacturing (WAAM) is an additive manufacturing technique that utilizes an arc welding head and a robot to deposit sequential layers of metal weld wire to construct complex three-dimensional geometries. WAAM can enhance manufacturing by allowing large custom metal components to be produced rapidly due to its high material deposition rate. This unlocks the ability to use design for additive manufacturing techniques for large metal parts. Existing research has established WAAM as a feasible manufacturing process for high strength low alloy (HSLA) welding steels, such as ER100 and ER120S.  However, most work has focused the proving the feasibility while less is known about the impact of process parameters on material properties and process repeatability. The current work evaluates the WAAM processing of gas metal arc welding (GMAW) cold metal transfer (CMT) ER120S welding wire for single bead thin wall components, and the impact of print orientation on material properties. Process parameters similar to previously published results were used to allow for comparison of print properties and mechanical properties across different machines and research groups. Walls were printed on a Fronius TPS 400i CMT system with at ABB 6-axis robotic arm and 2-axis positioner. The walls were 290mm x 120mm x 10mm and were printed with a 1.2mm diameter feedstock with a torch travel speed of 0.4m/min, an interlayer dwell time of 60s, a 80% Argon and 20% CO2 gas flowrate of 15 L/min, and a voltage of 13.3 V set on a Fronius synergic line of for ER70S. The layer height was set at 2.4 mm. Printed walls were separated from the individual baseplates via horizontal bandsaw, machined square, and milled into a tensile testing geometry based on ASTM E8M-22 for material analysis.  The samples were analyzed for their microstructure and mechanical properties.  During mechanical material analysis, tensile tests were performed on printed and machined WAAM specimens, during which mechanical data was collected, while a digital image correlation (DIC) system provided detailed strain field information.  The tensile test provided five critical mechanical properties including ultimate tensile strength, yield strength, ductility, modulus of elasticity, and toughness. Images of the microstructure of the WAAM parts were captured on a digital microscope and were used to understand how the observed microstructures affect the mechanical properties and provide insight into the thermal history of the part during the manufacturing process.  The images revealed horizontal striations in the microstructure from thermal cycling during manufacturing, resulting in alternating bands of columnar and equiaxed grains formed by varying nucleation and growth rates due to differing thermal gradients. The microstructure images identified that the grain size increases as the height of the wall increases, due to decreasing thermal gradients between the molten layer and the substrate as the wall height increases.  The WAAM samples produced during the research had comparatively lower strength and ductility than previously published data with similar process parameters. This revealed that further research must be conducted to understand the variability across similar WAAM systems and processes. Additionally, more work is needed to fully understand the impact of cooling rates and welding parameters for ER120s steel on print orientation and material properties.  

Presenting Author: Jack Zupfer University of St. Thomas

Presenting Author Biography: Jack Zupfer is a senior undergraduate mechanical engineering student at the University of St. Thomas, where he has been involved in wire arc additive manufacturing research. He has been an intern at PAR Systems in Shoreview, Minnesota. This company specializes in custom automation equipment for medical devices, aerospace, and nuclear handling. He worked on implementing machine learning into friction stir welding processes. He has aspirations to attend graduate school for mechanical engineering beginning in the Fall of 2025.

Authors:

Jack Zupfer University of St. Thomas
Aidan Hilger University of St. Thomas
Jack Taggart University of St. Thomas