About
Shamsuddin Piash
Mechanical Engineering Graduate
Bangladesh University of Engineering and Technology (BUET)
Class of 2025 (Graduated March 2025)
Academic & Research Overview
I am a Mechanical Engineering graduate from BUET with deep interests in Computational Solid Mechanics, Functionally Graded Materials (FGM), Advanced Composites, and Physics-Informed Machine Learning (PIML). My research focuses on leveraging finite element simulations (ANSYS) and multi-objective computational optimization algorithms to design next-generation lightweight, fatigue-resistant mechanical components.
Alongside engineering research, I have spent over 5 years as a Senior Physics Instructor and Academic Content Contributor at Udvash-Unmesh, teaching and mentoring over 5,000 students and leading conceptual physics demonstrations (including the Golpe Golpe Physics curriculum).
Research Domains & Core Competencies
- Structural & Solid Mechanics: Finite Element Analysis (FEA), Fatigue & Fracture Mechanics, Stress Concentration Mitigation, Composite & Bimaterial Interfaces.
- Functionally Graded Materials: Continuous gradation laws (Linear, Exponential, Sigmoid, Logarithmic), localized stiffness mismatch analysis, interfacial shear minimization.
- Computational Engineering & CAD: ANSYS Workbench (Static Structural, Steady-State Thermal, Fatigue Tool, Mesh Convergence), SolidWorks (Parametric 3D CAD, Motion Analysis).
- Scientific Computing & Programming: Python (NumPy, SciPy, Matplotlib), MATLAB, LaTeX, Git/GitHub, Linux/Bash.
Publications & Scholarly Works
Multi-Objective Structural and Material Optimization of Bimaterial Mechanical Components
Under Review at ASME Journal of Mechanical Design (2025)
Authors: Shamsuddin Piash, et al.
Focus: Multi-physics finite element modeling and SSE optimization schemes for bimaterial and composite structural elements.State-of-the-Art in Functionally Graded Material Gradation Laws and Computational Optimization
Target: Q1 Materials/Mechanics Journal (Manuscript in Preparation)
Focus: Comprehensive review synthesizing analytical gradation functions, homogenization models, and optimization algorithms.