Dr Aurik Andreu

Senior Technologist

Advanced Forming Research Centre

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Personal statement

Dr Aurik Andreu is a senior manufacturing engineer at the Advanced Forming Research Centre Forging team within the Forging and Incremental Technologies Team. Aurik is also leading the Heating Technology theme within the centre which is a very relevant area for most of the Tiers 1 and Tiers 2 members of the AFRC.

Aurik’s background consists of a mixture of industrial and commercial research with particular areas of expertise around material science (ceramics, carbon and metals), Gas to liquid (GTL), heating technologies / processes and modelling.

Aurik has led research activities at the AFRC on programmes such as the commissioning of an industrial gas furnace and Emissivity Calibration furnace for validating thermal process FEA modelling, testing new sensors and instruments, characterising furnace behaviour under different loading conditions and informing part-specific heating for the centre’s industrial partners.

 

Research Gate profile:

https://www.researchgate.net/profile/Aurik_Andreu

 

Linkedin page:

https://www.linkedin.com/in/aurik-andreu-a5a0b1aa

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Professional Activities

2026 AFRC Intern project - Induction Heating project - Recommissioning of Lab scale equipment and FEA modelling
Recipient
1/6/2026
2026 AFRC Intern Project - Physics-Informed Furnace Intelligence 
Recipient
1/6/2026
2023 AFRC Intern project - Preliminary development of CFD models for combustion system
Lecturer
1/6/2023
EMPRESS 2 - Temperature Metrology for Process Efficiency
Organiser
7/10/2021
2019 AFRC Intern project - Induction heating project / Real-Time Signal Processing, Control, and Simulation
Lecturer
1/6/2019
Thermal Modelling and Temperature Measurement Workshop
Organiser
28/6/2018

More professional activities

Projects

AFRC-CORE-07370-Continuation of CORE-06951 - induction heating line (NN and FE) and gas furnace CFD modelling with Qobeo (CORE funding: £109,738)
Huang, Jianglin (Principal Investigator) Andreu, Aurik (Co-investigator) Krishnamurthy, Bhaskaran (Researcher) Azim, Safi Sohail (Researcher)
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 (CORE Funding: £120,542)
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_CRAD_1716_Doing More with Less – Cogging Automation
Andreu, Aurik (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_CORE_06598_Sustainable Manufacturing of Titanium Alloy Components for Aerospace Applications (CORE funding: £49,095)
Andreu, Aurik (Co-investigator)
High-strength beta-titanium alloy, Ti-5553, is frequently used in aerospace applications such as landing gear components due to its excellent strength to density ratio and corrosion resistance. The traditional manufacture route of Ti-5553 parts for landing gear application consists of complex operations comprising sequential hot working and heat treatments to produce a bi-modal microstructure which is attributed with an excellent combination of strength, ductility, and fracture toughness. Understanding of the effect of processing routes and heat treatments on the mechanisms and kinetics of beta grains refinement is essential to achieve the desired microstructure and mechanical properties. Work Package 1 will focus on identifying the most effective thermo-mechanical processing route for beta grains refinement during ingot to billet conversion. Additive manufacturing route is also used by aerospace industry to manufacture some near-net shaped parts from dual phase Titanium alloys. Particularly, wire arc additive manufacturing (WAAM) which is also known as directed energy deposition-arc (DED-arc) technique is widely used. Due to both high-temperature gradients and high cooling rates which are induced in the depositing process, a basket weave microstructure having fine lamellar α and small α colony size in a β matrix is produced. Transformation of lamellar α into equiaxed α is critical for achieving the better combination of strength and ductility; and this is traditionally accomplished by applying mechanical load in the case of alloys from ingot metallurgy route. WP2 of this project will explore the possibility of using novel cyclic thermal treatment to globularise primary α phase in a Ti-6Al-4V alloy part manufactured by the WAAM . The temperature range for CTT is below β-transus and both furnace and induction-based approaches will be explored.
18-Jan-2024 - 30-Jan-2025

More projects

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Contact

Dr Aurik Andreu
Senior Technologist
Advanced Forming Research Centre

Email: aurik.andreu@strath.ac.uk
Tel: 534 5572