I have a research background in thermomechanical processing, process-microstructure-property correlations, numerical modelling of manufacturing processes, in situ process monitoring, development of digital twins, design and development of CNC machines, smart hybrid manufacturing and digital image corelation.
With this background, my research will aim to integrate process physics with intelligent control for sustainable, high-performance manufacturing. Followings will be my primary thrust areas.
Physics-Informed Smart Manufacturing Framework for Solid-State Processing.
Solid-state processes enable defect-free fabrication and direct recycling of metals without melting, driven by frictional heating, severe plastic deformation, and dynamic recrystallization [1] . Yet, the absence of in-situ process awareness and closed-loop control still results in microstructural variability and limited repeatability.
AI models offer fast predictions but limited generality, while physics-based models are accurate but slow. Building on our earlier work, where we successfully implemented a digital twin for real-time temperature prediction [2] , the Smart Solid-State Processing (Smart-SSP) framework extends this capability by integrating multi-sensor in-situ monitoring, phenomenological process maps, and physics-informed surrogate models into a unified, real-time architecture. This approach enables adaptive control of processing parameters, supporting stable, repeatable, and microstructure aware solid state manufacturing for advanced engineering applications.
2. Intelligent Wire Arc Additive Manufacturing (IWAAM) for Complex Metal Components
Conventional WAAM relies on fixed process settings, making it difficult to maintain consistent bead geometry, adapt to curved toolpaths, and prevent defects such as porosity or lack of fusion. Our earlier development of a portable high-precision 3-axis WAAM repair system further highlighted the need for intelligent, feedback-driven control to ensure stable and reliable deposition in real-world conditions.
Intelligent WAAM (I-WAAM) framework combines ML based bead modelling, geometry aware path planning, and real time melt pool sensing for adaptive, defect-free fabrication. Key elements include ML driven parameter optimization, region-based curved-layer deposition strategies, evaluation of responsive arc transfer modes (STT/CMT), and imaging-based defect prediction. Validation on thin-wall and multi-material components will demonstrate I-WAAM’s ability to deliver accurate, repeatable, and structurally sound builds, enabling next-generation smart and deployable WAAM systems.
3. DIC-Assisted Inherent Strain Framework for Residual Stress Prediction in Multi-Material Structures
We are currently exploring a surface-modification strategy for AM Inconel 718, in which a thin SS316L layer is deposited to promote partial remelting, reduce surface hardness, and improve machinability. In situ DIC during cutting of as-built and modified Inconel will quantify strain localization and chip-tool interaction, establishing the mechanics behind improved finishing performance.
Multi-material AM structures are increasingly used in aerospace, energy, and high-temperature applications where tailored thermal or mechanical gradients are required. Building on our present experiences, a sterio DIC assisted inherent strain framework can be implemented for rapid residual stress prediction in graded multi-material DED components. Layer-wise DIC and thermal imaging capture the evolving strain fields, which are converted into inherent strains for static FE simulations- enabling fast, accurate residual stress estimation without full transient modelling. This approach supports the reliable design of distortion-free, high-performance multi-material AM structures.
4. Development of a foamable precursor and a composite blowing agent for low temperature foaming
In our prior work, we successfully developed a rapid, energy-efficient friction sintering process for fabricating open-cell Cu metal foams [3]. We now aim to extend this capability toward producing aluminium foam filled tubes with controlled pore structures and strong tube foam bonding.
Aluminium foam–filled tubes offer excellent crashworthiness, but are challenging to fabricate with uniform pores and strong tube–foam bonding due to premature blowing agent decomposition and high foaming temperatures. This work develops a friction stir based route combined with low temperature foaming to achieve controlled pore formation while preserving tube integrity. A tailored low temperature precursor alloy will be designed to foam homogeneously below the melting point of aluminium, while a composite blowing agent will be engineered to delay hydrogen release. This can stabilize pore nucleation and growth, thereby enabling uniform porosity and strong tube–foam bonding.
5. Solid-State Upcycling of Functional Materials: Ultra-High Conductors and Rare-Earth-Free Magnetic Alloys
My prior work on SiC reinforced Al–Cu joints using a new friction processing method demonstrated significant enhancement of both mechanical and electrical functionality at dissimilar metal interfaces [4], [5]. This success motivates my broader research on developing energy-efficient solid-state manufacturing routes for advanced functional materials. I investigate deformation-driven consolidation strategies that produce porosity-free, oxidation-resistant magnetic alloys with nanoscale recrystallized grains, as well as ultra-high-conductivity Cu-based composites such as Cu–graphene systems that can surpass the conductivity of pure Cu. These melt-free processes reduce energy use, offer precise microstructural control, and enable sustainable upcycling of recycled feedstock. Ultimately, my goal is to create circular, high-efficiency materials for electrification, mobility, and clean-energy technologies.
