Thermodynamics powered by Machine Learning
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Updated
May 7, 2021 - Python
Thermodynamics powered by Machine Learning
Materials that are dependent on conditions
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.
Study of molecular motion of Glycerol using NMR modeling and simulations
Microstructure vision-based porosity analysis
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.
Brass tensile test analysis using MATLAB: stress–strain visualization, elastic-region fitting, yield determination, and mechanical properties extraction.
Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world.
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.
[11] You don't choose Ridge or Lasso - you let the data decide.
A small-scale Extract-Transform-Load framework focused on materials characterization
Open-source Arabic assistant for ceramic manufacturing defect diagnosis, quality control, and OpenAlex-powered research summaries.
Python-based coating robustness and machining severity decision-support framework
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