Research
My research experience spans mechanical characterization, additive manufacturing, and data-driven process optimization. Through projects—ranging from thermal simulation and topology optimization to microfabrication reviews—I’ve built a strong foundation in experimental and computational methods.
Mechanical Modeling and Experimental Characterization of Additively Manufactured Lattice Structures
Publication
Analysis of Compression and Energy Absorption Behavior of SLM printed AlSi10Mg Triply Periodic Minimal Surface Lattice Structures. Structures, Elsevier 15(3), 546. https://doi.org/10.1016/j.istruc.2024.106580
Objective & Motivation
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To evaluate the compressive response and energy absorption behavior of triply periodic minimal surfaces (TPMS) lattice structures manufactured via Powder Bed Fusion (PBF).
Approach
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Four distinct TPMS topologies were designed using AlSi10Mg and fabricated at two relative densities (30% and 50%).
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Stress-strain behavior was analyzed to determine the onset of densification, yield strength, and plateau stress.
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Energy absorption metrics such as specific energy absorption and efficiency were computed.
Outcome
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The study demonstrated a strong dependence of mechanical performance on both the lattice topology and relative density.
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The diamond and gyroid topologies showed promising energy absorption capabilities, suggesting suitability for impact mitigation applications.
Tools & Techniques:
PBF· Quasi-static Compression · Stress-Strain Analysis · Gibson–Ashby Model
Part Quality Improvement in Polymer Additive Manufacturing using Statistical Techniques and Machine Learning Models
Publication
Parametric Modeling and Optimization of Dimensional Error and Surface Roughness of Fused Deposition Modeling Printed Polyethylene Terephthalate Glycol Parts, MDPI, Polymers. January 2023. doi.org/10.3390/polym15030546
Objective & Motivation
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To improve the quality of parts fabricated using Fused Deposition Modeling (FDM), focusing on reducing dimensional inaccuracies and surface roughness—two key limitations of the technique.
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The goal was to optimize process parameters to enhance print fidelity across multiple Cartesian directions and surface profiles.
Approach
A combination of statistical and machine learning (ML) techniques was employed to model, predict, and optimize the FDM process:
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Response Surface Methodology (RSM) was used to develop regression models capturing the relationship between process parameters and output metrics.
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Analysis of Variance (ANOVA) verified the statistical significance and adequacy of the developed models.
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Adaptive Neuro-Fuzzy Inference System (ANFIS) models were built to predict dimensional and surface quality using data-driven learning approaches.
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A hybrid multi-objective optimization framework combining RSM and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was implemented to identify optimal process settings.
Outcome
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The study highlighted the strengths and trade-offs of statistical and machine learning methods in predicting FDM outcomes.
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It established a robust, hybrid approach for quality enhancement in polymer additive manufacturing, offering a framework adaptable to other materials and geometries.
Tools & Techniques:
RSM · ANOVA · ANFIS · NSGA-II · Design Expert · MATLAB
Statistical Modeling and Process Optimization of Metal Inert Gas Welding
Publications
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Prediction and Optimization of Weld Bead Geometry of MIG Welded Stainless Steel 202 Plates. Lecture Notes of Mechanical Engineering (LNME), Springer. https://doi.org/10.1007/978-981-16-2794-1_64
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Development of Mathematical Model for Prediction and Control of Weld Dilution in MIG Welded Stainless Steel 202 Plates. Lecture Notes of Mechanical Engineering (LNME), Springer. https://doi.org/10.1007/978-981-16-2794-1_39
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Mathematical Analysis of the Effect of Process Parameters on Angular Distortion of MIG Welded Stainless Steel 202 Plates by using the technique of Response Surface Methodology. Materials Today: Proceedings, Elsevier. https://doi.org/10.1016/j.matpr.2020.06.570
Objective & Motivation
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To develop empirical models correlating MIG welding parameters with key output metrics such as angular distortion, bead geometry, and dilution, with the aim of improving weld quality through parametric optimization.
Approach
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A structured Design of Experiments (DOE) approach was implemented.
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RSM was used to build mathematical models (validated using ANOVA) linking input welding parameters to output responses
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Main effect and interaction plots were analyzed to identify significant factors.
Outcome
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The study offered insights into the influence of individual welding parameters and their interactions on weld quality.
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The developed models enable predictive tuning of parameters for controlled distortion and optimal bead characteristics.
Tools & Techniques:
DOE · RSM · ANOVA · MIG Welding · Statistical Modeling
Review on Micro-Additive Manufacturing Technologies
Publication
Additive Manufacturing: A Review on the Microfabrication Methods. International Journal for Research in Applied Science & Engineering Technology. http://doi.org/10.22214/ijraset.2020.4160
Objective & Motivation
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To survey the landscape of micro-scale 3D printing technologies, classifying and comparing their fabrication principles, capabilities, and application areas.
Approach
The reviewed technologies were grouped into:
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Scalable AM: Techniques adapted from macro-AM processes to microfabrication.
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Direct-write (DW): Originally 2D deposition methods adapted for high-resolution 3D printing.
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Hybrid AM: Integrated additive-subtractive processes, typically combining multilayer electrodeposition and planarization.
Outcome
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The review clarified the trade-offs between throughput, resolution, and material choices across different micro-AM techniques.
Post Processing Methods in Additive Manufacturing
Publication
Additive Manufacturing: Post Processing Methods and Challenges. Advanced Engineering Forum (AEF), Trans Tech Publications.
Objective & Motivation
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To provide a comprehensive overview of post-processing techniques used in additive manufacturing to meet design and performance specifications.
Approach
The review categorized post-processing methods into four key areas:
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Support material removal
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Surface Finish Improvement
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Property enhancement using thermal techniques
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Property enhancement using non-thermal techniques
Each category was discussed in terms of operation principles, relevant AM technologies, and their impact on final part properties.
Outcome
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The study emphasized the crucial role of post-processing in enabling AM parts to meet mechanical and surface requirements, highlighting current challenges and best practices.
Thermal Analysis and Manufacturing Simulation of Heat Sinks produced via Additive Manufacturing
Objective & Motivation
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To design TPMS-based heat sinks using additively manufactured AlSi10Mg structures and evaluate their thermal management performance.
Approach
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Gyroid, Diamond, and Schwartz TPMS topologies were constructed using nTopology and simulated for heat dissipation behavior.
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Thermal performance was evaluated under steady-state loading conditions.
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The build process was simulated in Autodesk Netfabb to identify regions prone to residual stress during Powder Bed Fusion
Outcome
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The diamond topology demonstrated superior cooling performance due to its high surface-area-to-volume ratio.
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Netfabb simulation provided actionable insights for reducing internal stresses during fabrication.
Tools & Techniques
nTopology · SLM · Thermal Simulation · Netfabb · AlSi10Mg