Guides

Functional testing validates that hardware behaves correctly as a complete system. Learn the difference from ICT, how to build functional tests in Python.

Predictive quality uses production data to catch defects before they happen. Learn how it works, what data it needs, and how test results feed prediction.

Compare open source test executives for manufacturing: OpenHTF, OpenTAP, pytest, and HardPy. Features, operator UI, cost, and when to use each.

Connect a Keysight 34461A or 34465A DMM to Python using PyVISA, measure voltage, current, and resistance, and log results to TofuPilot via an OpenHTF plug.

An AI-native test station is built around data and inference from the start, not bolted on after. Learn what it means and how it changes manufacturing test.

A comparison of manufacturing test frameworks (OpenHTF, pytest, OpenTAP, TestStand) with code examples, feature matrices, cost analysis, and guidance on.

Overall equipment effectiveness (OEE) measures manufacturing productivity. Learn how to calculate OEE for test stations and track it with TofuPilot.

A continuous test stack connects test development, execution, data collection, and analytics into one integrated workflow. Learn what it includes and how.

Learn how to use TofuPilot's test data to trace hardware failures back to their root cause using measurement trends and run comparisons.

Structure your test procedures across EVT, DVT, and PVT phases. Refine measurement limits using early data and track procedure versions as your product matures.

Map AS9100 quality management requirements to TofuPilot features for test records, serial tracking, measurement history, and audit-ready exports.

Learn how to build a test sequencer with OpenHTF using phase ordering, skip logic, PhaseGroups, multi-SKU sequences, and TofuPilot result logging.