Dr Jianglin Huang

F I T Process Modelling Theme Lead

Advanced Forming Research Centre

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Publications

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Publications

Achieving superior tensile and fatigue properties than conventional wrought state via hybrid additive-forging manufacturing: Wang Wei, Tan Zinong, Wang Yaping, Zhang Ruiqiang, Huang Jianglin, Patawee Jintana, Allen Michael, Meredith Katie, Lin Jianguo, Hopper Christopher, Jiang Jun; Materials and Design Vol 262 (2026); https://doi.org/10.1016/j.matdes.2026.115485
Transvalor/Bifrangi/AFRC - case study - Simulation of the induction hardening process applied to crankshaft manufacturing: Andreu Aurik, Huang Jianglin, Coulbeck Teig, Perchat Etienne; (2025)
AFRC-PRS03075-DIRF07316-D1-Calibration and verification report with tuned simulation inputs: Barbera Daniele, Falsafi Javad, Parolin Paolo, Huang Jianglin; (2025)
AFRC-PRS03014-CORE06951-D7.2 - Technical notes on the implementation and results of the neural network models: Andreu Aurik, Huang Jianglin, Azim Safi Sohail; (2025)
AFRC-PRS02990-CRAD1716-D5 - Development of an Integrated Platform for Fully Automated Cogging Processes Design and Digitalisation: Huang Jianglin; (2025)
AFRC-TRP02932-CORE06951-D6.1b: Huang Jianglin, Andreu Aurik; (2025)

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Research

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Projects

AFRC-DIRF-07316 - Cosworth forging modelling project for Aerospace: Huang, Jianglin (Principal Investigator) Parolin, Paolo (Co-investigator) Barbera, Daniele (Project Lead) Falsafi, Javad (Researcher); 07-Jan-2025 - 14-Jan-2025
AFRC-CORE-07370-Continuation of CORE-06951 - induction heating line (NN and FE) and gas furnace CFD modelling with Qobeo: Huang, Jianglin (Principal Investigator); This follow-on project builds on the outcomes of CORE-06951, advancing neural network approaches to improve prediction, efficiency, and scalability for large industrial induction heating lines. The work includes characterising an industrial-scale induction heating line, refining and integrating neural network models with FE simulation data, and validating predictions against real-world measurements. In parallel, the project extends CFD modelling of industrial furnaces using Qobeo software, focusing on process aspects such as door-opening effects and low-temperature fan-assisted heating. The combined NN–FE–CFD approach aims to deliver faster, more accurate process simulations, enhancing digital twin capabilities for heating and hardening operations.; 11-Jan-2025 - 31-Jan-2026
Induction Heat Treatment of White Cast Iron: Espinoza, Ashlee (Principal Investigator) Andreu, Aurik (Co-investigator) Huang, Jianglin (Co-investigator); 01-Jan-2025 - 15-Jan-2026
AFRC_CORE_06951_Heating Technologies: FF furnaces characterisation and induction hardening optimisation using Neural Network: Andreu, Aurik (Principal Investigator) Chalkley, Eleanor (Co-investigator) Huang, Jianglin (Co-investigator) Azim, Safi Sohail (Co-investigator); FF furnaces characterisation
Investigating the thermal characteristics of the FutureForge furnaces by implementing the previously
developed FutureForge data handling and analysis tools and completing heating trials with an
instrumented part. This data can be used to compare the efficiency, carbon cost, temperature
uniformity and stability of these two furnaces and provide process feedback to the operators of the
FutureForge cell.
This work package will also include the instrumented part trials planned for CORE 06601 as soon as
the furnaces are available.

Induction hardening optimisation using Neural Network
In the previous project "AFRC-CORD-06093", we successfully developed and validated induction
hardening process models in both DEFORM and FORGE for the rack bar induction hardening
process (tooth and rack side) at Bifrangi. These models accurately predicted the hardened layer
thickness and hardness profile, closely aligning with experimental results. This outcome underscores
our advanced capabilities in physics-based modelling of induction processes, developed through
years of research in various CORD/CORE projects.
However, while these traditional FEM-based models are highly accurate, they come with significant
computational costs, particularly when scaling to more complex or larger systems. The iterative
nature of FEM simulations and the high fidelity required for accurate predictions often result in long
processing times, which can limit their practicality for real-time process optimization and the
exploration of a vast design space.
To address these challenges, we propose to introduce a neural network approach to enhance our
existing induction hardening models. Neural networks offer the potential to significantly accelerate
simulation times while maintaining accuracy, enabling more efficient process optimization and
reducing the dependency on extensive physical trials.; 30-Jan-2024
AFRC - CRAD - 1716 Doing More with Less – Cogging Automation: Krishnamurthy, Bhaskaran (Researcher) Huang, Jianglin (Project Lead) Barbera, Daniele (Researcher) Andreu, Aurik (Researcher) Parolin, Paolo (Researcher) Falsafi, Javad (Researcher) Chalkley, Eleanor (Researcher); This project aims to create an integrated platform for the fully automated design, simulation, and digitalization of the cogging process, using advanced tools such as easy2forge, FORGE, and qobeo. The platform will provide engineers with easy access to cogging process design and optimisation. By automating and digitizing the cogging process design, the platform will facilitate better data collection, storage and management, providing fundamental support to the digital transformation of cogging operations.; 20-Jan-2024 - 31-Jan-2025
AFRC-CATP-07065 - Modelling capability development (defence): Barbera, Daniele (Project Lead) Parolin, Paolo (Co-investigator) Krishnamurthy, Bhaskaran (Researcher) Andreu, Aurik (Researcher) Huang, Jianglin (Principal Investigator) Falsafi, Javad (Researcher); Modelling capability development project to embed core modelling expertise into AFRC’s defence portfolio. The focus was on knowledge transfer, staff upskilling, and the reuse of strategic modelling IP developed through previous defence collaborations. Value: £ 35,000; 02-Jan-2024 - 31-Jan-2025