Wenhua Yang
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Wenhua Yang, Ph.D.

Assistant Professor

Department of Mechanical Engineering
Prairie View A&M University

Additive Manufacturing Data-Driven Modeling Battery Modeling

Dr. Wenhua Yang's research lies at the intersection of advanced manufacturing, machine learning, and computational materials science. His work focuses on data-driven process–structure–property relationships in metal additive manufacturing and deep learning models for battery electrode microstructure analysis. He received his Ph.D. in Mechanical Engineering from Mississippi State University (2021) and previously held positions at the University of Michigan-Dearborn and the University of Houston.

Ph.D. Mechanical Engineering, Mississippi State University, 2021
M.S. Mechanical Engineering, Mississippi State University, 2018
B.E. / M.E. Mechanical Engineering, Jiangxi Agricultural University, 2004

News

2024

New position. Joined Prairie View A&M University as an Assistant Professor in the Department of Mechanical Engineering.

2024

New paper. "Data-driven prediction of future melt pool from built parts during metal additive manufacturing" published in Journal of Manufacturing Processes.

2023

New paper. "Time-dependent Deep Learning Predictions of 3D Electrode Particle-resolved Microstructure Effect on Voltage Discharge Curves" published in Journal of Power Sources.

2022

Review published. "Data-driven modeling of process, structure and property in additive manufacturing: a review and future directions" published in Journal of Manufacturing Processes. (120+ citations)

Research

Laboratory for Intelligent Process Modeling and Materials Design (LIPMD)

Our research integrates machine learning with mechanics of materials and advanced manufacturing to accelerate material design and process optimization.

Additive Manufacturing grain evolution

Additive Manufacturing

Process–structure–property relationships in metal AM, including uncertainty quantification, melt pool dynamics, microstructure evolution in Ti-6Al-4V, and bi-continuous piezocomposite fabrication.

Data-driven modeling simulation Data-driven modeling animation

Data-Driven Modeling

Deep learning and physics-informed neural networks for materials and manufacturing — multi-input CNNs for ultrafast field simulation, generative models for microstructure prediction, and AI-integrated process monitoring.

Battery electrode microstructure Battery electrode microstructure Battery electrode microstructure
Battery dendrite growth simulation

Battery Modeling

Phase-field and multiphysics simulation of lithium dendrite growth in Li-metal batteries, coupling ion transport and charge conservation with adaptive mesh refinement to study dendrite-induced short-circuit mechanisms.

People

Principal Investigator

Wenhua Yang

Wenhua Yang, Ph.D.

Assistant Professor

Department of Mechanical Engineering
Prairie View A&M University

weyang@pvamu.edu

Graduate Students

MZ

Md Ehsanul Islam Zafir

M.S. Student

Mechanical Engineering

SJ

Shahriar Jahan

M.S. Student

Mechanical Engineering

Undergraduate Students

KD

Kara Aliyah Daveron

Undergraduate Researcher

Mechanical Engineering

Publications

Full list on Google Scholar.  Bold = presenting / corresponding author.

Journal Articles

  1. W. Yang, et al., "Data-driven prediction of future melt pool from built parts during metal additive manufacturing," Journal of Manufacturing Processes, 2024. 24 citations

  2. W. Yang, X. Yao, Z. Wang, "Time-dependent Deep Learning Predictions of 3D Electrode Particle-resolved Microstructure Effect on Voltage Discharge Curves," Journal of Power Sources, vol. 579, p. 233087, 2023.

  3. Z. Wang, W. Yang, L. Chen, "Multi-input Convolutional Network for Ultrafast Simulation of Field Evolvement," Patterns, 2022.

  4. Z. Wang, W. Yang, L. Chen, "Data-driven modeling of process, structure and property in additive manufacturing: a review and future directions," Journal of Manufacturing Processes, vol. 77, pp. 13–31, 2022. 120+ citations

  5. W. Yang, Z. Wang, T. Yang, L. He, X. Song, Y. Liu, L. Chen, "Exploration of the Underlying Space in Microscopic Images via Deep Learning for Additively Manufactured Piezoceramics," ACS Applied Materials & Interfaces, vol. 13, no. 45, pp. 53439–53453, 2021.

  6. Y. Xiao, M. Cagle, S. Mujahid, P. Liu, Z. Wang, W. Yang, L. Chen, "A Gleeble-assisted study of phase evolution of Ti-6Al-4V induced by thermal cycles during additive manufacturing," Journal of Alloys and Compounds, vol. 860, p. 158409, 2021. 29 citations

  7. Z. Wang, C. Jiang, P. Liu, W. Yang, L. Chen, "Uncertainty Quantification and Reduction in Metal Additive Manufacturing," NPJ Computational Materials, vol. 6, no. 1, p. 175, 2020. 76 citations

  8. X. Song, L. He, W. Yang, L. Chen, "Additive Manufacturing of Bi-Continuous Piezocomposites with Triply Periodic Phase Interfaces for Combined Flexibility and Piezoelectricity," Journal of Manufacturing Science and Engineering, vol. 141, no. 11, p. 111004, 2019. 36 citations

Teaching & Experience

2024 – Present

Assistant Professor

Department of Mechanical Engineering, Prairie View A&M University

2023 – 2024

Lecturer

Cullen College of Engineering, University of Houston

2021 – 2023

Postdoctoral Research Fellow

Department of Mechanical Engineering, University of Michigan-Dearborn

2021

Instructor — Thermodynamics I & Heat Transfer

Bagley College of Engineering, Mississippi State University

Awards & Honors

2021

Summer Bridge Assistantship

Bagley College of Engineering, Mississippi State University

2020

1st Place — ASME-CIE Hackathon

Identifying, Extracting & Analyzing Value from Large Unstructured Data Sets in Mechanical Engineering

2018

Finalist, Best Paper — ASME MSEC

Manufacturing Science and Engineering Conference

2018

Fall Bridge Assistantship

Bagley College of Engineering, Mississippi State University

Contact

Mailing Address
Department of Mechanical Engineering
Prairie View A&M University
Prairie View, TX 77446
Email
weyang@pvamu.edu
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Professional Memberships
ASME · Society of Engineering Science (SES)