The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately predict the melt pool morphology during SLM. Its superior per (IMAGE)

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The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately predict the melt pool morphology during SLM. Its superior per

Caption

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately predict the melt pool morphology during SLM. Its superior performance under different laser scanning speeds is validated by experiment and simulation, showing a broad potential for industrial applications.

Credit

Qingyun Zhu/ Wuhan University, Zhengxin Lu/ Wuhan University, Yaowu Hu/ Wuhan University

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CC BY

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