Guides

Learn why test data jurisdiction matters for manufacturing companies and how to protect process parameters and yield data from foreign surveillance laws.

Pp measures long-term process spread relative to spec width using overall standard deviation. Learn the formula, compare Pp to Cp, and track it in TofuPilot.

Ppk measures long-term process performance using overall standard deviation. Learn how it differs from Cpk and how TofuPilot tracks both.

Cp measures how much of the spec window your process variation uses, ignoring centering. Learn the formula, how Cp relates to Cpk, and read it in TofuPilot.

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.