Dr. Xianglin Huang is a Visiting Research Professor at Dongguk University–Seoul and a Postdoctoral Researcher at the InnoCORE PRISM-AI Center, Korea Advanced Institute of Science and Technology (KAIST). He received his Ph.D. in Mechanical Engineering from the University of Manchester (2024) under the supervision of Prof. Qingming Li, one of the leading authorities in impact engineering.
His doctoral research established a mathematically rigorous, data-driven methodology for characterizing the dynamic flow stress of metals — combining SVD/CP tensor decomposition with artificial neural networks to determine constitutive equations directly from discrete SHPB experimental data. This work addressed a long-standing gap in the field: a systematic framework for verifying and relaxing the assumptions underlying the widely-used Johnson-Cook equation. The resulting trilogy of papers in the International Journal of Impact Engineering (2023, 2025 ×2) forms the core of his academic identity in impact dynamics.
As a postdoctoral researcher, Dr. Huang is extending this constitutive modeling expertise toward the impact safety and health management of lithium-ion batteries (LIBs). His current work encompasses: (i) experimental characterization of LIB failure modes and energy thresholds under quasi-static, drop-weight, and ballistic loading; (ii) deep learning frameworks using acoustic emission signals for real-time battery damage assessment; and (iii) multiphysics finite element models coupling structural impact with electrochemical degradation.
His long-term vision is to leverage his impact dynamics foundation to address open challenges in battery mechanics — developing constitutive models for electrode and separator materials under extreme loading, enabling physics-informed numerical simulations of battery failure, and ultimately contributing to the safety standards for next-generation energy storage systems.
Graduate and undergraduate courses bridging modern machine learning methods with mechanical and PHM engineering practice.