In the relentless pursuit of innovation, the energy sector is increasingly turning to metal additive manufacturing (MAM) to produce parts that can withstand the harshest conditions. However, the very processes that make MAM so powerful also introduce significant challenges, such as high-temperature gradients and thermal stresses that can compromise the integrity of printed metallic parts. A recent study published in the Journal of Materials Research and Technology, titled “A review on scan strategies in laser-based metal additive manufacturing,” delves into these issues and offers insights that could revolutionize the way we approach MAM, particularly in the energy industry.
At the heart of this research is Junaid Dar, a researcher at Oregon State University’s School of Mechanical, Industrial and Manufacturing Engineering and the Advanced Technology and Manufacturing Institute (ATAMI). Dar and his team have been exploring how scan strategies—including laser parameters, scan speed, power, hatch spacing, layer thickness, and scan pattern—influence the microstructure, residual stress, and mechanical properties of additively manufactured parts. Their findings could pave the way for more reliable and efficient production of critical components in the energy sector.
“The extreme thermal and mechanical coupling in MAM results in a wide variation in part properties,” Dar explains. “Controlling these attributes remains challenging due to the complexity of the process. But by optimizing scan strategies, we can significantly improve product efficiency and quality.”
One of the key areas of focus in Dar’s research is laser powder bed fusion (PBF-LB), a popular MAM technique. In PBF-LB, a laser fuses metallic powder layer by layer to create a three-dimensional object. The scan strategy used in this process plays a critical role in defining thermal input and temperature gradients, which in turn affect the final properties of the printed part.
The energy sector, with its demanding requirements for material performance, stands to benefit greatly from these advancements. For instance, components used in power generation, such as turbine blades and heat exchangers, often operate under extreme conditions. Any defects or inconsistencies in these parts can lead to costly downtime and even catastrophic failures. By optimizing scan strategies, manufacturers can produce parts with more uniform microstructures and lower residual stresses, enhancing their reliability and longevity.
Moreover, the research highlights recent advancements in MAM, such as multi-laser systems, inline parameter control, machine learning, and in-situ monitoring. These technologies, when combined with optimized scan strategies, can further enhance the quality and consistency of additively manufactured parts. For example, machine learning algorithms can analyze vast amounts of data from in-situ monitoring systems to predict and prevent defects in real-time, driving the mainstream adoption of metal additive manufacturing in the energy sector.
Dar’s work, published in the Journal of Materials Research and Technology, which translates to the Journal of Materials Science and Technology, provides a comprehensive review of scan strategies and their effects on various attributes of additively manufactured parts. It serves as a valuable resource for researchers and industry professionals alike, offering insights into the latest developments in MAM and their potential applications in the energy sector.
As the energy industry continues to evolve, the demand for high-performance, reliable components will only increase. Dar’s research on scan strategies in laser-based metal additive manufacturing offers a promising path forward, one that could shape the future of manufacturing in the energy sector and beyond. By embracing these advancements, manufacturers can produce parts that are not only stronger and more durable but also more efficient and cost-effective, ultimately driving innovation and growth in the energy industry.