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Predicting fatigue performance of additive manufactured parts
How to use machine learning to anticipate the impact of the additive manufacturing process on component-level fatigue performance
Working with additive manufactured parts empowers the production of increasingly complex designs and enables distributed manufacturing. But predicting the fatigue performance of additive manufactured parts is challenging. The manufacturing process induces multiple local factors that cannot be separated. Therefore, the interaction and influence of these factors cannot be described by mathematical modeling. Hence, today, it is too laborious to estimate the fatigue life of 3D printed safety-critical components.
Siemens has developed an innovative methodology to predict the impact of the manufacturing process on the fatigue performance of additive manufactured parts. Integrated into an efficient durability solver, Simcenter 3D software leverages the power of machine learning technology.
This virtual approach avoids potentially hundreds, if not thousands of tests, by using machine learning together with a limited test set, resulting in a model that can interpolate/extrapolate for untested conditions.
Register for this webinar and discover how to:
Expand your reach and accurately predict the correct point of failure and fatigue life, whereas conventional methods fail.
Dr. Michael Hack
Managing Simcenter Durability Simulation Solutions, Siemens Digital Industries Software
Michael Hack has 25 years of experience at Siemens in various durability roles. In his current position as Product Line Manager for durability simulations, he combines customer needs with academic research and he has presented and published numerous research projects on hysteresis operators, rainflow counting, thermal fatigue, composite fatigue as well as reliability and optimization topics.
CAE 3D Senior Research Engineer, Siemens Digital Industries Software
Hunor Erdelyi has been with Siemens Digital Industries Software for more than ten years, working in various research and development roles. He is currently Senior Manager in the CAE 3D R&D team, focusing on solutions for new materials and additive manufacturing (AM). Hunor and the team help customers with the simulation of AM processes, the prediction of resulting microstructures, defects, and related material properties, and their impact on part scale performance; such as strength & durability.
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