Insights on green sand management, foundry optimization, and predictive analytics.
16 min read · Vijay Gurunath · 2026-04-03
Your lab tests seven parameters at 25°C. Defects form at 1,300°C. Four structural blind spots explain why good numbers still produce bad castings — and why statistical tools can’t help.
Read article →24 min read · Vijay Gurunath · 2026-03-26
Correlation-based sand analytics pattern-matches to your history; the model breaks the moment conditions shift. Physics-based prediction calculates from first principles and adapts. Methodology decides what still works when your supplier or season changes.
Read article →24 min read · Vijay Gurunath · 2026-03-25
Correlation-based vs physics-based sand analytics. Learn the key differences between statistical ML and physics-based prediction, and which works better for your foundry.
Read article →9 min read · Vijay Gurunath · 2026-03-24
Every parameter on your SPC chart is in range. Your rejection rate isn't. Here's why monitoring parameters one at a time can't see what your sand is doing.
Read article →22 min read · Vijay Gurunath · 2026-03-22
Why correlation-based foundry sand analytics fails. How physics-based prediction calculates actual sand behavior. The simulation analogy explained.
Read article →12 min read · Vijay Gurunath · 2026-03-19
What if you could see tomorrow’s Active Clay, LOI, and GCS before you pour? Not a guess. A physics-based calculation you can verify with your own data.
Read article →18 min read · Vijay Gurunath · 2026-03-18
Your sand was perfect yesterday. Today it’s off. You didn’t change anything. Here’s the physics of what actually happens inside your sand system overnight.
Read article →12 min read · Vijay Gurunath · 2026-03-16
Your lab tests measure seven properties. But the forces that actually drive defects — thermal degradation rates, transport rates, system states — are invisible.
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