In the new breakthrough, the machine learning technology helps create “32 percent more efficient hydraulic pumps”, say the researchers
The gerotor tooth profile is crucial for determining hydraulic system performance in automotive engineering. In a new development, researchers from Pusan National University, South Korea have leveraged conditional generative adversarial networks for machine learning-driven gerotor profile synthesis and optimization.
The novel approach has remarkably produced designs that outperform human efforts and lead to 32 percent more efficient hydraulic pumps, potentially revolutionizing the automotive industry.
Gerotor pumps for oil circulation and lubrication are crucial components in automotive and hydraulic systems. They possess a compact design, excellent flow rate per rotation, and high suction capability. [Read more…] about Pusan National University researchers use AI to create optimized engine components that ‘outperform human designs’