AI

A Pharmaceutical Research study trained Random Forest and XGBoost classifiers on particle size, shape, surface energy, and silica dry-coating data to predict powder blend flowability category. The accuracy holds up as a screening tool and breaks down in specific, predictable places.

Process analytical technology combined with machine learning is changing residual moisture control and yield prediction in pharmaceutical and food spray drying. The gains are real but bounded, and the off-line test panel has not been replaced.

Real-time particle characterization is moving powder processing from delayed laboratory checks toward [...]

Smart powder processing in additive manufacturing links engineered powders, real-time sensor data, [...]

Digital twins in powder processing only work when you give them one [...]

AI-assisted material characterization speeds testing, strengthens models, and improves inline control. Labs [...]

AI monitoring in additive manufacturing makes affordable powders viable in metal AM. [...]

Machine learning now allows robots to adjust dosing motions based on real-time [...]

AI-assisted direct imaging can turn difficult process images into usable particle-size information [...]

Quantum thinking in powder technology isn’t about physics. It’s a mindset shift. [...]

Agglomeration in powder technology transforms fine powders into larger, manageable structures, improving [...]

Table of Contents: AI-driven powder scaling Particles gradually transition [...]