What Is FMEA for Manufacturing Test

What Is FMEA for Manufacturing Test with TofuPilot

Failure Mode and Effects Analysis (FMEA) is a structured method for identifying what can go wrong, how severe the consequences are, and what controls exist to catch it. Applied to manufacturing test, FMEA helps you decide which test steps matter most and where to invest in detection. This guide covers how FMEA works, the difference between DFMEA and PFMEA, and how TofuPilot data feeds your FMEA process.

How FMEA Works

FMEA scores each potential failure mode on three dimensions:

Factor	What It Measures	Scale
Severity (S)	How bad is it if this failure reaches the customer?	1-10
Occurrence (O)	How often does this failure mode happen?	1-10
Detection (D)	How likely is your current test to catch it?	1-10

The Risk Priority Number (RPN) is S x O x D. Higher RPN means higher priority. A failure that's severe, frequent, and hard to detect gets the highest score.

Example FMEA for a Power Supply

Failure Mode	Effect	S	O	D	RPN	Action
Solder bridge on output FET	Short circuit, potential fire	10	3	2	60	AOI + hipot test covers this
Wrong capacitor value	Output ripple out of spec	6	2	4	48	Add ripple measurement to FCT
Cold solder on connector	Intermittent connection in field	8	4	7	224	Add pull test or thermal cycling screen
Firmware flash failure	Unit DOA	9	2	1	18	Self-test catches this

The cold solder on the connector has the highest RPN because it's hard to detect (D=7). That's where you invest in better testing.

DFMEA vs PFMEA

Type	Scope	Who Leads	When
DFMEA (Design FMEA)	Product design failures	Design engineering	EVT/DVT
PFMEA (Process FMEA)	Manufacturing process failures	Process/test engineering	PVT/production

DFMEA asks: what can fail in the design? PFMEA asks: what can go wrong during manufacturing? For test engineering, PFMEA is where you define which tests are needed and why.

How TofuPilot Data Feeds FMEA

FMEA requires real data for the Occurrence and Detection scores. Guessing these numbers makes the analysis unreliable. TofuPilot provides the data you need:

FMEA Input	TofuPilot Source
Occurrence rate	Failure Pareto by test step
Detection effectiveness	Compare production test yield to field return rate
Failure mode distribution	Measurement distributions showing which parameters drift
Trend data	Control charts showing whether a failure mode is increasing

Prerequisites

Python 3.10+
OpenHTF installed (pip install openhtf)
TofuPilot Python SDK installed (pip install tofupilot)

Step 1: Map FMEA Actions to Test Phases

Each FMEA action item that requires a test becomes an OpenHTF phase. Include the FMEA reference in the phase docstring for traceability.

fmea_driven_test.py

import openhtf as htf
from openhtf.util import units


@htf.measures(
    htf.Measurement("output_ripple_mV")
    .in_range(maximum=50)
    .with_units(units.MILLIVOLT),
)
def phase_ripple_check(test):
    """PFMEA item #2: Detect wrong capacitor via ripple measurement."""
    test.measurements.output_ripple_mV = 28.5


@htf.measures(
    htf.Measurement("connector_resistance_mOhm")
    .in_range(maximum=100)
    .with_units(units.OHM),
)
def phase_connector_check(test):
    """PFMEA item #3: Detect cold solder on output connector."""
    test.measurements.connector_resistance_mOhm = 42.0


@htf.measures(
    htf.Measurement("hipot_result").equals("PASS"),
)
def phase_hipot(test):
    """PFMEA item #1: Verify no shorts on output stage."""
    test.measurements.hipot_result = "PASS"

Step 2: Log Results and Update FMEA

Connect the test to TofuPilot. As production data accumulates, update your FMEA occurrence and detection scores with real numbers.

fmea_driven_test.py

from tofupilot.openhtf import TofuPilot

test = htf.Test(
    phase_hipot,
    phase_ripple_check,
    phase_connector_check,
)

with TofuPilot(test):
    test.execute(test_start=lambda: input("Scan serial: "))

Step 3: Review and Iterate

TofuPilot tracks results per test step. Open the Analytics tab to review:

Failure Pareto shows which failure modes actually occur most often (updates your O score)
Measurement distributions show whether your limits catch marginal units (validates your D score)
Yield trends show whether corrective actions are working (RPN should decrease over time)

FMEA is a living document. Review it quarterly using TofuPilot data to update scores and reprioritize actions. A failure mode that was rare six months ago might be common now due to a supplier change.

What Is FMEA for Manufacturing Test