Shahadat Hussain
Research Associate · NYU Abu Dhabi · Abu Dhabi, UAE
🏭 What I Do — From Industry's Perspective
I solve hard manufacturing and materials challenges that sit at the boundary of what can be designed and what can be physically built. My work centres on three industrial problem spaces:
| Problem | What I Bring |
|---|---|
| You need complex, lightweight structures that outperform solid or conventionally-latticed parts | Computational design + 3D printing of TPMS metamaterials (Gyroid, Schwarz-P, Diamond) in metal and high-performance polymers |
| Your material behaves unexpectedly after AM processing — cracking, porosity, phase shifts | Hands-on LPBF process optimisation + full SEM/XRD/DSC characterisation pipeline to diagnose and resolve root causes |
| You need smart, adaptive components — actuators, stents, morphing structures — without motors or electronics | Shape Memory Alloy (NiTi, Cu-Al-Mn, Cu-Al-Ni) fabrication, characterisation, and functional testing |
Availability: Open to collaborative research, industrial R&D consultancy, and joint project proposals. → contact@shahadathussain.com
⚙️ Core Engineering Capabilities
🖨️ Additive Manufacturing — Processes & Equipment
I have direct hands-on experience operating the following systems:
Metal AM (LPBF/SLM)
- EOS M400 — Laser Powder Bed Fusion of NiTi, stainless steel, Ti alloys
- Process parameter optimisation: laser power, scan speed, energy density, hatch spacing
- Standards: ISO/ASTM 52904 (metal PBF process characteristics & QC)
Polymer & Composite AM
- Raise3D Pro3 Plus HS · Raise3D Pro2 Plus · Raise3D Forge1
- Stratasys F370 (FDM) · Stratasys J850 (PolyJet, multi-material)
- Ultimaker S1 · Formlabs Form 3 (SLA) · Bambu Lab X1 Carbon
- Materials: CF-PPA, PPA, ABS, ASA, PETG, Resins, VeroWhite
- Standards: ISO/ASTM 52903-1 (material extrusion, high-performance polymers)
Slicing & Build Preparation
- Materialise Magics · IdeaMaker · Cura · GrabCAD Print · PreForm · Bambu Studio
🔬 Materials Characterisation Pipeline
Full in-house characterisation workflow from sample prep to data export:
Raw Sample → Metallography → Microscopy → Diffraction → Thermal Analysis → Mechanical Testing → Data Processing| Technique | Equipment | What It Tells You |
|---|---|---|
| Scanning Electron Microscopy | JEOL 7610F · FEI Nova NanoSEM · FEI Quanta 3D FIB-SEM | Microstructure, porosity, grain boundaries, fracture surfaces |
| X-ray Diffraction | Bruker D2 Phasor · Rigaku MiniFlex II + Xpert HighScore Plus | Phase identification, crystallography, residual stress |
| Differential Scanning Calorimetry | Setaram Instruments · Mettler Toledo DSC 1 | Phase transformation temps (A_s, A_f, M_s, M_f) in SMAs |
| Optical Microscopy | Leica Optical Microscope | Grain structure, surface quality, etch contrast |
| Optical Emission Spectroscopy | Bruker Q4 Tasman | Elemental/chemical composition verification |
| Metallography | Struers & Buehler grinder/polisher + chemical etching + Carbolite furnaces | Surface prep, heat treatment, sectioning |
🧪 Mechanical Testing & Validation
Testing to international standards across static, dynamic, and fatigue regimes:
| Test Type | Equipment | Standard |
|---|---|---|
| Tensile (metals) | Instron 5969 (50 kN) | ASTM E8/E8M |
| Compression | Controls Simplex 350 + Automax / MTS 100 kN | ASTM E9 / ASTM D695 |
| Tensile (polymers/composites) | Instron 5969 | ASTM D638 |
| Fatigue (NiTi) | Instron 8872 (25 kN) + WaveMatrix3 | ASTM E606 · ISO 12106 |
| 4-Point Bending | MTS 100 kN | ASTM E290 |
| SMA Phase Characterisation | Mettler Toledo DSC 1 | ASTM F2004 · ASTM F2082 |
🧲 Shape Memory Alloys — Fabrication to Function
End-to-end SMA capability: alloy design → fabrication → processing → characterisation → testing.
Alloy Systems Worked With:
- NiTi (Nitinol) — LPBF-fabricated TPMS lattices; phase transformation, superelasticity
- Cu-Al-Mn — vacuum induction melting + rolling; high-damping structural applications
- Cu-Al-Ni — sand casting + thermomechanical processing; high-temperature actuation
Fabrication Routes:
Vacuum Induction Melting · Sand Casting · Two-Roll Rolling · Laser Powder Bed Fusion · Heat Treatment
Industrial Applications This Enables: Biomedical implants · Self-expanding stents · Morphing aerospace structures · Vibration dampers · Thermal actuators · Lightweight energy-absorbing lattices
🏗️ Design & Simulation
| Category | Tools |
|---|---|
| CAD | SolidWorks · FreeCAD · Tinkercad |
| Lattice / TPMS Generation | MSLattice · Materialise Magics |
| FEA / Simulation | ANSYS · FreeCAD FEM |
| HPC / Scientific Computing | MATLAB · Python · Linux · Shell Scripting |
| Image Processing | OpenCV · Scikit-image · ImageJ/Fiji · MATLAB |
📊 Scientific Computing & Data Science
I write code to process, analyse, and model experimental data — not as a separate discipline, but as an integral part of the research workflow.
Stack:
Applied to: stress–strain curve processing · SEM image segmentation & feature extraction · process parameter–property correlation modelling · predictive ML for materials behaviour · DSC transformation temperature extraction · experimental data dashboards
📚 Selected Publications
Peer-Reviewed Journals
-
Imperfections in NiTi TPMS Lattice Layers Fabricated via LPBF
Materials, 2022, 15(22), 7950 — Balling, intergranular cracking, melt pool spattering in Schwarz-P NiTi layers at varying relative densities and scan strategies -
Microstructural & Surface Analysis of NiTi TPMS Lattice Sections (LPBF)
Journal of Manufacturing Processes, 2023, Vol. 102, pp. 375–386 — Primitive & Gyroid topologies; Ni evaporation, oxide/Ti-rich phase formation, effect of LPBF parameters on phase distribution
Conference
- Inhomogeneous Microstructure in NiTi TPMS Structures via LPBF
ASME IMECE 2022, Columbus, Ohio, USA — Non-uniform solidification rates; influence of TPMS geometry on melt pool dynamics
🎓 Credentials
| Certification | Issuer | Focus |
|---|---|---|
| Additive Manufacturing Specialization | Arizona State University | DfAM, process execution, parameter optimisation |
| IBM Data Science Professional Certificate | IBM | Full DS pipeline, Python, ML, SQL |
| Machine Learning Specialization | Stanford / DeepLearning.AI | Supervised, unsupervised, reinforcement learning |
| Deep Learning Specialization (in progress) | DeepLearning.AI | Neural networks, CNNs, sequence models |
| Image Processing for Science & Engineering | MathWorks | Segmentation, object detection, batch workflows |
| Practical Data Science with MATLAB | MathWorks | EDA, statistical modelling, predictive analytics |
| MATLAB Skills for AI Era | MathWorks | AI integration, workflow automation, app design |
| Google AI Essentials | Applied AI in engineering workflows | |
| Python for Everybody | University of Michigan | Python, databases, APIs |
🤝 How to Engage
I collaborate with industry partners, R&D teams, and researchers on:
- Custom TPMS/metamaterial design and 3D printing for structural, biomedical, or thermal applications
- LPBF process development and defect troubleshooting for new alloy systems
- Shape memory alloy component fabrication and characterisation
- Data-driven materials property prediction and experimental data analysis
- Peer review for journals in materials science and additive manufacturing
ORCID: 0000-0002-4355-2169 · Scopus: 56380929800 · ResearcherID: I-3091-2017 · ISNI: 0000-0005-3020-7229
Personal projects
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