Successfully set up the 10 kW laser system!
This laser system includes a dual-laser adjustable beam mode heat source, a ground-standing robot, a laser scanner head, a optical table, a chiller, and related hardware and software
New paper published
This study presents a Python-based image analysis algorithm to investigate spatter behavior and melt pool stability during the laser powder bed fusion (LPBF) process. Using high-speed video data, the algorithm detects spatter characteristics and melt pool dynamics, introducing a stability index that correlates fewer spatters with higher melt pool stability, aiding in optimizing additive manufacturing quality.
Deep Learning to Analyze Spatter and Melt Pool Behavior During Additive Manufacturing
This study uses high-speed imaging and deep learning models (YOLOv5, Fast R-CNN, RetinaNet, and EfficientDet) to analyze spatter and melt pool behavior in laser powder bed fusion. Results show a correlation between melt pool stability, spatter count, and ejection dynamics, offering insights to improve additive manufacturing efficiency, quality, and process reliability.
In Laser Powder Bed Fusion (LPBF) 3D printing, forces like vapor recoil and surface tension affect melt pool stability, spatter formation, and part quality. To tackle this, we applied four deep learning algorithms to detect and track spatter behavior and melt pool stability using high-speed recorded videos. In addition, a novel spatter index is proposed to correlate the spatter count and ejection speed based on the vapor recoil force and surface tension force.
Founded in 1932, Alpha Sigma Mu is the international professional honor society dedicated to recognizing excellence in materials science and engineering. The society honors outstanding academic achievement and professional distinction among students and leaders in the field. We are proud to announce the establishment of the Iowa State University Chapter, an exciting milestone that brings new opportunities for recognition, leadership, and advancement within our academic community!
Conference Presentation: Powder Melting Efficiency in Laser Powder Bed Fusion (LPBF)
SPIE PHOTONICS WEST, JANUARY2025
I had the opportunity to present my research on powder melting efficiency in the Laser Powder Bed Fusion (LPBF) process, focusing on how process parameters like laser power, scanning speed, and layer thickness affect melting behavior in stainless steel 316 and Ti6Al4V powders. Our findings showed that increasing laser power or reducing scan speed and layer thickness improves melting efficiency, with Ti6Al4V performing better under similar conditions. We also introduced melting efficiency maps and a dimensionless powder melting index to evaluate energy utilization. It was a great experience to share our work, reconnect with old friends, and make some new ones along the way.
Attending the 2024 American Welding Society (AWS) Program at the FABTECH Conference was an insightful experience. The event showcased cutting-edge advancements in welding technology, offering a glimpse into the future of the industry. It was inspiring to engage with innovative ideas and learn about the latest tools and techniques shaping modern welding practices.
The visit to IPG Photonics in Davenport, IA, was an enlightening experience, showcasing advanced machinery and state-of-the-art technology in laser systems. Observing the precision equipment and automation in action was fascinating, reflecting the cutting-edge innovation driving the industry forward. It was a great opportunity to explore the integration of robotics and laser technology, while also gaining insights from experts in the field.