Learn the method
Understand Tensor SPC, T², Q, residual energy, and why preserving multiway structure matters.
Open Tensor SPC guide →Statistical Engineering Platform
Structure-aware process monitoring for modern manufacturing data.
Stat-Forge connects classical SPC, multivariate monitoring, tensor methods, and applied Python analytics for high-dimensional quality and manufacturing systems.
Featured monograph: Tensor-Structured Statistical Process Control, Version 4. Zenodo DOI: 10.5281/zenodo.19985232.
Recommended Path
Explore the work from concept to application
The site is now organized as a learning and research hub: start with the plain-language explanation, walk through the worked example, compare PCA and Tensor SPC, then open the monograph or Python app resources.
See a worked example
Follow a sensor × time × condition monitoring workflow using tensor decomposition and T²-Q interpretation.
Open worked example →Compare against PCA
See why vectorized PCA is a useful baseline but can hide mode-specific structure.
Open comparison →Run an interactive demo
Use browser-based Python to adjust anomaly strength and see how residual Q responds.
Open interactive demo →Publication
Tensor-Structured Statistical Process Control
Download the latest monograph or view the Zenodo record for citation and version history.
Software Direction
Stat-Forge Python App
The site now includes a Python app landing page designed for Streamlit/Railway deployment, demo screenshots, notebooks, and downloadable analytics resources.
Open tools/app page