Guides

Cpk measures whether your process produces units within spec limits, accounting for centering. Learn the formula, thresholds, and how TofuPilot tracks it.

SPC uses production test data to separate normal variation from real problems. Learn the core tools, how they connect, and how TofuPilot automates SPC.

A hardware test plan defines what to test, when, and how. Learn how to structure a test plan and implement it in Python with TofuPilot.

IQC, IPQC, FQC, and OQC are quality control stages in manufacturing. Learn what each covers and how to track quality data with TofuPilot.

Validate instrument calibration status before every test run. Track calibration dates as metadata and catch expired calibrations before they corrupt your data.

An operator interface lets production floor workers run tests without writing code. Learn what it includes, how it compares across frameworks, and how to.

Control a Rigol DP800 series power supply from Python using PyVISA, with multi-channel control, OVP/OCP protection, and TofuPilot integration via OpenHTF.

A complete reference for OpenHTF measurement types, validators, and units, with code examples for numeric ranges, exact matches, percentages, and marginal.

Learn how to use production test data trends in TofuPilot to predict equipment failures and schedule maintenance before downtime occurs.

Learn how to monitor test station health, track uptime metrics, and detect performance degradation using TofuPilot analytics.

Test observability gives full visibility into what your test systems are doing, why they fail, and how they perform. Learn how it applies to manufacturing test.

Design validation testing (DVT) confirms a product meets its design requirements. Learn how to structure DVT in Python and track results with TofuPilot.