Thermodynamics powered by Machine Learning

A bilingual (EN/ZH) 4-stage research workflow skill for Claude Code and Codex CLI — topic refinement, urgency assessment, route evaluation, and experiment planning with citation verification

Materials that are dependent on conditions

Study of molecular motion of Glycerol using NMR modeling and simulations

Integrated thermal, kinetic & scheduling framework that cuts a steelmaking Ladle Furnace cycle 70 → 37.6 min — coupling queueing theory, transient heat transfer, dephosphorisation kinetics & Monte Carlo. IIT Madras × Amalgam Steel.

Thermo-metallurgical FEM (Abaqus) predicting phase transformation in a quenched S5140 steel gear - JMAK + Koistinen-Marburger kinetics, parametric cooling-rate study. IIT Madras.

Python-based GUI application for Dynamic Mechanical Analysis (DMA) data processing, visualization and automated reporting.

PressureX is an engineering evaluation package for a passive layered structural mitigation concept using shear-thickening fluid behavior to broaden impulsive loads and reduce peak transmitted response in high-vibration aerospace structures. Targets are design-intent until validated.

Microstructure vision-based porosity analysis

Public-source systems-engineering commentary on Starship's stainless-steel route, reuse governance, and safety-boundary thinking.

Brass tensile test analysis using MATLAB: stress–strain visualization, elastic-region fitting, yield determination, and mechanical properties extraction.

A small-scale Extract-Transform-Load framework focused on materials characterization

Material property database for Grade 91 (9Cr-1Mo-V-Nb) steel. Larson-Miller creep rupture, Norton power law, tensile correlations, and Coffin-Manson strain-life from published NIMS/ORNL sources.

Interactive heat treatment simulator for predicting alloy phase transformation and hardness using Python, JavaScript, and CCT-based modeling.

Metal LPBF process documentation from my Uniformity Labs Metal 3D Print Specialist role — AlSi10Mg, Ti-6Al-4V, 316/304 SS, and Inconel 625 + 718, printed on SLM Solutions SLM 125 and SLM 280 machines.

Open-source Arabic assistant for ceramic manufacturing defect diagnosis, quality control, and OpenAlex-powered research summaries.

[11] You don't choose Ridge or Lasso - you let the data decide.

Machine learning based alloy yield strength prediction using Linear Regression, Random Forest, SHAP, and interactive web visualization.

Python-based coating robustness and machining severity decision-support framework