FMEA-AI™ is an AI-Powered PFMEA Software developed by TMIC (Trade Master International) that automates Process Failure Mode and Effects Analysis according to IATF 16949:2016 and AIAG-VDA FMEA 2019 Harmonized. It uses a Factory Knowledge Base built from your actual engineering data — process flow charts, machine lists, defect history, CAR/NCR records — to generate comprehensive PFMEA, Control Plans, and SPC linkage up to 10 times faster than traditional methods.

Can FMEA-AI™ generate PFMEA automatically?

Yes. FMEA-AI™ automatically generates PFMEA by learning from your Factory Knowledge Base, including engineering specifications, process flow charts, machine and equipment lists, defect history (PPM/DPPM), CAR/NCR records, and Lessons Learned. The output includes failure modes, effects, causes, prevention controls (PC), detection controls (DC), and Action Priority (AP) ratings per AIAG-VDA 2019.

Does FMEA-AI™ support AIAG-VDA FMEA 2019 Harmonized?

Yes. FMEA-AI™ fully supports the AIAG-VDA FMEA Harmonized 2019 format including the 7-Step Approach, Action Priority (AP) system (H/M/L) replacing traditional RPN, separation of Prevention Controls (PC) and Detection Controls (DC), Structure Analysis, Function Analysis, Failure Analysis, Risk Analysis, and Optimization steps — all aligned with AIAG Core Tools requirements.

Can FMEA-AI™ generate Control Plans?

Yes. FMEA-AI™ automatically generates Control Plans in AIAG format linked directly to PFMEA results. Generated Control Plans include process steps, product and process characteristics, special characteristic classifications (CC/SC), measurement techniques, sample sizes, control methods, and reaction plans — fully aligned with IATF 16949 clause 8.5.1.1.

Is FMEA-AI™ suitable for IATF 16949 compliance?

Yes. FMEA-AI™ is designed specifically for IATF 16949:2016 compliance. It supports PFMEA (clause 8.3.3.3), Control Plan (clause 8.5.1.1), and SPC linkage (clause 10.2.3). The system follows AIAG Core Tools methodology — APQP, PPAP, FMEA, Control Plan, MSA, and SPC — making it suitable for Tier 1 and Tier 2 automotive suppliers preparing for customer audits or PPAP submissions.

FMEA-AI™ — AI-Powered PFMEA Analysis in Minutes, Not Days

AI Output Preview (PFMEA Example)

PFMEA

Control Plan

Action Plan

SPC Linkage

Summary

PROCESS FMEA AIAG-VDA Harmonized 2019 — 7-Step Approach

Part NameHousing Cover (Injection Molded)

Part Number / Rev.HCS-2024-001 / Rev.B

PFMEA NumberPFMEA-IM-2026-001 Rev.A

Model Year / ProgramMY2026 — Program HCS-A

Process / PlantInjection Molding — Op.10 · TMIC Manufacturing

CustomerAutomotive Tier 1 Assembly

Core TeamJ.Tanaka (QA) · S.Panya (Process) · P.Krit (Prod.) · A.Maneerat (Mat.)

Revision Date / Approved By2026-01-15 · QMR Sign-off

① Structure & Function Analysis			② Failure Analysis			③ Risk Analysis				④ Optimization & Actions
Process Step Function	Failure Mode FM ID	Failure Effects Next Op · Customer	S	SC Class	Failure Cause Prevention Controls (PC)	O	Detection Controls (DC)	D	AP	Recommended Action	Responsible Target Date	New S · O · D · AP
Op. 10 Injection Molding Fill mold cavity at 80–120 bar · 45.0±0.5g (PA66-GF30, dwg A1-204)	Incomplete Cavity Fill (Short Shot) FM-001	Next Operation Assembly misfit — missing material causes bracket attachment failure; downstream rework required End Customer Structural integrity failure under vibration load · Loss of primary function · Customer complaint / warranty return	7	◆ CC	Injection pressure below setpoint Set point 100 bar · Actual <80 bar (pressure drift, valve wear) PC — Prevention Controls Machine parameter standard SOP-IM-05 Rev.3 Alarm interlock: ±10% pressure deviation triggers stop	5	DC — Detection Controls Cavity pressure sensor (PLC real-time) SPC-01 X̄-R SPC Chart — continuous monitoring 1st-off dimensional check per batch WI-QC-07	6	H	Increase setpoint: 95 → 115 bar Validate via DOE Taguchi L9 (n=3 replicates) · Confirm Cpk ≥ 1.67 on SPC-01 · Update SOP-IM-05 Rev.4 S. Panya (Process Eng.) 2026-03-15	S. Panya 2026-03-15	7 · 2 · 4 AP: L
					Gate blockage — carbon deposit / PA degradation Crystallized PA66 residue narrows gate bore; runner restriction; ~2× per quarter (NCR-2025-047, NCR-2025-103) PC — Prevention Controls PM schedule: gate cleaning every 200 shots Form-PM-04 Mold condition log per maintenance event	4	DC — Detection Controls 100% visual at ejection (light box) WI-QC-07 CMM dimensional check every 2 hrs (±0.05mm) Shift inspection report Form-QC-10	5	M	Reduce PM interval: 200 → 150 shots Update Form-PM-04 · Add borescope inspection step to gate cleaning SOP · Review PA drying to prevent degradation at barrel P. Krit (Prod. Sup.) 2026-03-10 ✓ Done	P. Krit 2026-03-10	7 · 2 · 3 AP: L
					PA66-GF30 moisture content > 0.2% Insufficient drying (70°C/2h instead of specified 80°C/4h) → steam voids and incomplete fill; splay/silver streak visible PC — Prevention Controls Drying parameter std per material spec SOP-MAT-02 Incoming material inspection Form-IQC-03 Operator training WI-MAT-02 (annual)	3	DC — Detection Controls Drying temp/time log Form-DRY-01 (per batch) Moisture meter — Rotronic HygroPalm (per lot, target <0.2%) MFI test on sampling basis (monthly)	7	M	Revise drying SOP: 70°C/2h → 80°C/4h Per BASF Ultramid datasheet · Add moisture meter go/no-go gate before hopper loading · Interlock dryer → machine: block start if moisture >0.2% A. Maneerat (Mat. Eng.) 2026-03-20	A. Maneerat 2026-03-20	7 · 1 · 4 AP: L

Op. 10 Injection Molding Hold pressure & gate freeze stage (cosmetic surface)	Sink Mark on Class-A Surface FM-002	Next Operation Visual fail at incoming inspection; returned to molding for 100% sort/rework End Customer Cosmetic defect visible on exterior surface · Customer complaint · Risk of warranty return (appearance claim)	5	★ SC	Insufficient holding pressure → premature gate freeze Holding pressure too low (<60% of fill pressure) and/or hold time too short → insufficient material compensation in thick wall section (4.5mm rib) PC — Prevention Controls Holding pressure param std SOP-IM-05 DFM wall-thickness guideline (max. ratio 2.5:1) per Moldex3D simulation MD-2025-11	6	DC — Detection Controls 100% visual inspection — light box + golden sample WI-QC-07 Cosmetic AQL 1.0 per lot Shift patrol inspection (every 30 min)	5	M	Optimize holding pressure & time via DOE Taguchi L9 array: holding pressure (3 levels) × hold time (3 levels) × melt temp (3 levels) · Verify gate freeze time via Moldex3D re-simulation S. Panya (Process Eng.) 2026-04-01	S. Panya 2026-04-01	5 · 3 · 4 AP: L

Op. 10 Injection Molding Clamp unit & parting line integrity	Flash / Burr Beyond Parting Line FM-003	Next Operation Sharp burr causes operator safety concern during assembly; part interference blocks clip insertion End Customer Assembly difficulty / dimensional mismatch at mating interface · Cosmetic complaint if flash visible externally	6	—	Worn mold parting line — gap > 0.05 mm Extended mold service (>80,000 shots) causes parting-line wear; combined with excessive injection pressure, material enters gap; also observed after mold crash event PC — Prevention Controls Mold PM schedule: parting-line inspection every 5,000 shots Form-PM-02 Clamp force spec: 800 kN minimum SOP-IM-05 Mold life tracking log (current: 62,000 shots)	5	DC — Detection Controls 100% visual at ejection (standard WI-QC-07) Parting-line gap gauge — go/no-go 0.03mm (every 500 shots) 1st-off flash check after every mold open/close cycle	3	M	Inspect & regrind parting line; update PM frequency Inspect mold parting line with 10× loupe · Schedule regrind if gap >0.03mm · Reduce PM interval to 3,000 shots until regrind complete · Add mold crash protocol to operator checklist J. Tanaka (QA Eng.) 2026-03-30	J. Tanaka 2026-03-30	6 · 2 · 2 AP: L

◆ CC = Critical Characteristic (Safety / Regulatory) ★ SC = Significant Characteristic (Customer / Function) AP: H High — Immediate action required AP: M Medium — Team decides on action AP: L Low — Optional improvement PFMEA-IM-2026-001 · IATF 16949:2016 §8.3.3.3 · AIAG-VDA FMEA 2019

☐ Prototype ☐ Pre-Launch ☑ Production CP-IM-2026-001 Rev.A

Part NameHousing Cover (Injection Molded)

Part NumberHCS-2024-001

PPAP LevelLevel 3

Supplier / PlantTMIC Manufacturing Co., Ltd.

CustomerAutomotive Tier 1 — Assembly Line

Engineering Change LevelRev.A — 2026-01-15

Core TeamJ.Tanaka (QA) · S.Panya (Process) · P.Krit (Prod.)

Supplier Approval Date2026-01-20

Customer Engineering Approval2026-02-05

No	Process Name	Op.	Product Characteristic	Process Characteristic	S.C.	Specification / Tolerance	Measurement Technique	Sample Size	Frequency	Control Method	Reaction Plan
05	Material Preparation Drying Stage	05	Melt Flow Index (MFI)	Drying Temperature	◆ CC	80°C × 4 hrs Tolerance: ±3°C	K-Type Thermocouple Cal. due: 2026-12-01	1 batch	Per material lot	Temperature Log Sheet + Alarm interlock	Quarantine batch → Re-dry or Reject → NCR issue
10	Injection Molding Gate Fill	10	Cavity Completeness (Short Shot prevention)	Injection Pressure	◆ CC	80–120 bar Nominal: 100 bar	Cavity Pressure Sensor PLC data logging	1 shot	100% Continuous	SPC X̄-R Chart → SPC-01 Linkage	Stop → Alert QE → Adjust pressure → Verify 5 shots OK
20	Injection Molding Cooling Stage	20	Dimensional Stability	Cooling Time	★ SC	20 ± 2 sec LSL 18s / USL 22s	PLC Timer ±0.1 sec resolution	1 shot	100% Continuous	Process Parameter Log + PLC alarm	Adjust → Record in logbook → If repeat: engineering review
30	Injection Molding Ejection / Weighing	30	Part Weight (Shot Size)	Shot Size Volume	—	45.0 ± 0.5 g LSL 44.5 / USL 45.5	Analytical Scale Resolution ±0.01g	5 pcs	Every 1 hour	SPC X̄-R Chart → SPC-03 Linkage	Stop line → 100% inspect last hour → Notify QE → Cpk study
40	Mold Maintenance Gate / Runner Check	40	Mold Cavity Condition	Gate & Runner Inspection	—	Free from debris / flash Visual std. per photo	Visual + Borescope SOP-IM-07	Full mold	Every 200 shots	Maint. Check Sheet (Form-PM-04)	Clean gate → Record downtime → Restart → Verify 1st shot OK

◆ CC = Critical Characteristic (Safety/Regulatory) ★ SC = Significant Characteristic (Customer Impact) Generated by FMEA-AI™ — linked to PFMEA FM-001 Short Shot

PFMEA Recommended Actions — Failure Mode: Short Shot (FM-001) | Initial RPN: 210 | Part: Housing Cover | Process: Injection Molding

AP #	Recommended Action	Responsible	Target Date	Priority	Status	Verification Method	Old RPN	New RPN	Cut
AP-001	Increase Injection Pressure setpoint 95 bar → 115 bar · Confirm with DOE trial (n=9 runs)	Process Engineer S. Panya	2026-03-15	HIGH	⏳ In Progress	Production Trial + SPC-01 Cpk ≥ 1.67	210	84	−60%
AP-002	Implement Gate Cleaning Procedure (SOP-IM-07) Clean every 200 shots · Assign dedicated operator	Production Sup. P. Krit	2026-03-10	HIGH	✓ Completed	Audit Checklist — 100% conformance verified	120	40	−67%
AP-003	Update PA Material Drying Parameters 70°C × 2h → 80°C × 4h per material datasheet	Material Engineer A. Maneerat	2026-03-20	MED	◯ Open	FAI + Dimensional Check (5 pcs) + MFI test	108	54	−50%
AP-004	Optimize Cooling Time via DOE Taguchi L9 array · Factors: temp, time, coolant flow · 3 replicates	Process Engineer S. Panya	2026-04-01	MED	◯ Open	Cpk study after DOE · Target Cpk ≥ 1.33	150	90	−40%
AP-005	Validate Shot Size via Cavity Pressure Monitoring Install in-mold pressure sensor · Real-time feedback loop	QA Engineer J. Tanaka	2026-03-25	LOW	◯ Open	MSA Gauge R&R ≤ 10% + FAI sign-off	105	70	−33%

5 Actions · 1 Completed · 1 In Progress · 3 Open Projected Max RPN after completion: 84 (from 210 — 60% reduction)

SPC Linkage Map — Process: Injection Molding | Part: Housing Cover | Linked FMEA: FM-001 (Short Shot) ● Stable ● Watch ● OOC — Action Required

SPC ID	Characteristic	Process Step	Specification (LSL / Nom. / USL)	Chart Type	n	Frequency	Cpk Target	Est. Cpk	Gauge / Instrument	Status & Action
SPC-01	Injection Pressure ◆ CC	Gate Fill	78 / 100 / 122 bar	X̄-R	5	Continuous (PLC stream)	≥ 1.67 (CC spec)	1.43 ⚠	Cavity Pressure Sensor Cal. due: 2026-12-01	● Watch Verify sensor calibration · Tighten setpoint
SPC-02	Cooling Time ★ SC	Cooling Stage	17.5 / 20.0 / 22.5 sec	X̄-R	5	Continuous (PLC stream)	≥ 1.33	1.58 ✓	PLC Timer Resolution ±0.1 sec	● Stable Continue monitoring · No action
SPC-03	Part Weight (Shot Size)	Ejection	44.5 / 45.0 / 45.5 g	X̄-R	5	Every 1 hour	≥ 1.33	0.89 ✗	Digital Scale ±0.01g Cal. due: 2026-09-01	● OOC — ACT NOW Stop line → 100% check → AP-001 / AP-005
SPC-04	Drying Temperature ◆ CC	Material Prep	228 / 240 / 252°C	X̄-S	3	Per material lot	≥ 1.33	1.71 ✓	K-Type Thermocouple Cal. due: 2026-12-01	● Stable Continue · Check AP-003 update
SPC-05	Gate Temperature	Pre-Injection	69 / 75 / 81°C	I-MR	1	Per shot (100%)	≥ 1.33	1.24 ⚠	IR Thermogun Resolution ±0.5°C	● Watch Consider upgrading to SPC X̄-R (n=5)

5 SPC Characteristics · 2 Stable · 2 Watch · 1 OOC (Immediate Action: SPC-03) All charts linked to PFMEA FM-001 — Short Shot | IATF 16949 §10.2.3

Total Failure
Modes Analyzed

High Risk
(RPN ≥ 150)

Medium Risk
(RPN 80–149)

Low Risk
(RPN < 80)

Top Failure Modes by RPN

FM-001 Short Shot

210

FM-002 Sink Mark

150

FM-003 Flash

126

FM-004 Weld Line

FM-005 Warpage

⚠ Priority Focus: FM-001 (Short Shot) is the only High-Risk failure mode. 5 recommended actions generated. SPC-03 (Part Weight) is currently Out of Control — immediate action required.

Action Plan Progress

■ Completed: 1 (20%) ■ In Progress: 1 (20%) ■ Open: 3 (60%)

Key Quality Metrics

Current Max RPN (FM-001)210 — High Risk

Projected Max RPN (post-action)84 — Medium

SPC Characteristics: Stable2 / 5

SPC Out-of-Control1 (SPC-03 Part Weight)

Control Plan Characteristics5 (2 CC · 1 SC · 2 General)

PPAP StatusLevel 3 — Submitted

60%

Expected RPN Reduction
after all 5 actions complete

FMEA-AI™ Analysis — Part: Housing Cover HCS-2024-001 | Process: Injection Molding | IATF 16949:2016 §8.3.3.3 Generated: 2026-06-06 | Ref: PFMEA-IM-2026-001 Rev.A

AI-Powered PFMEA Software for IATF 16949

FMEA-AI™ by TMIC · AIAG-VDA FMEA 2019 Harmonized · Smart Quality System · FMEA Software Thailand

PFMEA Software — Process Failure Mode and Effects Analysis

PFMEA (Process Failure Mode and Effects Analysis) คือเครื่องมือหลักในการระบุ วิเคราะห์ และจัดการความเสี่ยงในกระบวนการผลิต ตามมาตรฐาน IATF 16949 ข้อกำหนด 8.3.3.3 FMEA-AI™ ในฐานะ AI FMEA Software ช่วยลดเวลาจัดทำ PFMEA จากหลายวัน เหลือเพียงไม่กี่ชั่วโมง โดยอาศัย Factory Knowledge Base ที่เรียนรู้จากข้อมูลจริงของโรงงาน ไม่ว่าจะเป็น Process Flow Chart, Machine & Equipment List, Defect History (PPM/DPPM) และ CAR/NCR Records

ผลลัพธ์ที่ได้คือ PFMEA ที่ระบุ Failure Mode, Failure Effects, Failure Causes พร้อม Prevention Controls (PC), Detection Controls (DC) และ Action Priority (AP: H/M/L) ตามมาตรฐาน AIAG-VDA 2019 ครบถ้วน พร้อมสำหรับ PPAP Level 3 และ Customer Audit

DFMEA — Design Failure Mode and Effects Analysis

นอกจาก PFMEA Software แล้ว FMEA-AI™ ยังรองรับ DFMEA (Design Failure Mode and Effects Analysis) สำหรับการวิเคราะห์ความเสี่ยงในระดับการออกแบบผลิตภัณฑ์ เหมาะสำหรับทีม Design Engineer ที่ต้องการระบุ Failure Mode ในช่วง APQP (Advanced Product Quality Planning) ก่อนเข้าสู่กระบวนการผลิตจริง

ระบบ AI จะวิเคราะห์ Design Function, Interface และ Requirements เพื่อ generate DFMEA ที่ครอบคลุม Potential Design Failure Modes ที่อาจส่งผลต่อ Safety, Regulation และ Customer Satisfaction ก่อนที่จะเข้าสู่ Process Risk Assessment ในขั้นตอนถัดไป

AIAG VDA FMEA 2019 Harmonized

FMEA-AI™ รองรับ AIAG-VDA FMEA Handbook 2019 Harmonized อย่างสมบูรณ์ รวมถึง 7-Step Approach, Action Priority (AP) system (High/Medium/Low) ที่แทนที่การใช้ RPN เพียงอย่างเดียว และโครงสร้างคอลัมน์ที่สอดคล้องกับ AIAG VDA FMEA Form มาตรฐาน

ระบบ AI generate PFMEA ที่ผ่านการ validate ตาม AIAG-VDA FMEA requirements ทุกข้อ ทั้ง Structure Analysis, Function Analysis, Failure Analysis, Risk Analysis และ Optimization Steps ทำให้โรงงานพร้อมสำหรับ Customer Audit และ PPAP Submission ได้ทันที

Control Plan Generation & Process Risk Assessment

หนึ่งในคุณสมบัติเด่นของ FMEA-AI™ ในฐานะ Smart Quality System คือ Control Plan Generation อัตโนมัติจากผล PFMEA โดยตรง ในรูปแบบ AIAG Format พร้อม Special Characteristic Classification (◆CC / ★SC), Measurement Technique, Sample Size, Control Method และ Reaction Plan สอดคล้องกับ IATF 16949 ข้อกำหนด 8.5.1.1

Process Risk Assessment ครอบคลุมทุก Failure Mode โดยพิจารณาจาก Severity, Occurrence และ Detection พร้อม generate Action Plan ที่ระบุ Responsible Person, Target Date และ Verification Method อย่างชัดเจน ช่วยลดความเสี่ยงในการ Audit Failure

Factory Knowledge Based AI for PFMEA and Root Cause Analysis

DOE Software · Machine Learning for Quality · Root Cause Analysis · FMEA Software Thailand

Root Cause Analysis with DOE and Machine Learning

FMEA-AI™ ไม่เพียงระบุ Failure Cause เท่านั้น แต่ยังพิสูจน์ Root Cause ด้วย DOE Software (Design of Experiments) และ Machine Learning for Quality ระบบ AI generate DOE Design แบบ Taguchi L9 หรือ Full Factorial และวิเคราะห์ผลเพื่อยืนยัน Root Cause อย่างมีหลักฐานทางสถิติก่อน generate Recommended Actions ที่มีประสิทธิภาพและวัดผลได้จริง

การใช้ Machine Learning ร่วมกับ Root Cause Analysis ทำให้ FMEA-AI™ สามารถเรียนรู้จาก Pattern ของ Defect ในอดีต และ Predict Failure ที่มีโอกาสเกิดขึ้นในอนาคต เพิ่มความแม่นยำของ PFMEA ได้อย่างมีนัยสำคัญ

FMEA Software Thailand — สำหรับโรงงานในไทยและ ASEAN

FMEA Software Thailand ที่เข้าใจบริบทอุตสาหกรรมไทยและ ASEAN ทั้ง Automotive Tier 1 / Tier 2, Electronics, PCB / Semiconductor, Industrial Manufacturing และ Medical Device High Reliability FMEA-AI™ โดย TMIC พร้อมให้การสนับสนุน Implementation เพื่อให้โรงงานบรรลุมาตรฐาน IATF 16949 FMEA และผ่าน Customer Audit ได้อย่างมั่นใจ

ความแตกต่างหลักของ FMEA-AI™ คือการใช้ Factory Knowledge Base แทน Generic AI Prompt ทำให้ผลลัพธ์สอดคล้องกับ Process จริงของโรงงานคุณ ไม่ใช่ข้อมูลทั่วไป — นี่คือ AI FMEA ที่รู้จักโรงงานของคุณอย่างแท้จริง

PFMEA Software AI FMEA FMEA Software Thailand AIAG VDA FMEA IATF 16949 FMEA Process Risk Assessment Control Plan Generation Root Cause Analysis DOE Software Machine Learning for Quality Smart Quality System DFMEA

Why Traditional FMEA Takes Too Long?

How FMEA-AI Works

AI Output Preview (PFMEA Example)

AI-Powered PFMEA Software for IATF 16949

PFMEA Software — Process Failure Mode and Effects Analysis

DFMEA — Design Failure Mode and Effects Analysis

AIAG VDA FMEA 2019 Harmonized

Control Plan Generation & Process Risk Assessment

Factory Knowledge Based AI for PFMEA and Root Cause Analysis

Root Cause Analysis with DOE and Machine Learning

FMEA Software Thailand — สำหรับโรงงานในไทยและ ASEAN

Transform Your FMEA Process
with Artificial Intelligence

Why Traditional FMEA Takes Too Long?

How FMEA-AI Works

AI Output Preview (PFMEA Example)

AI-Powered PFMEA Software for IATF 16949

PFMEA Software — Process Failure Mode and Effects Analysis

DFMEA — Design Failure Mode and Effects Analysis

AIAG VDA FMEA 2019 Harmonized

Control Plan Generation & Process Risk Assessment

Factory Knowledge Based AI for PFMEA and Root Cause Analysis

Root Cause Analysis with DOE and Machine Learning

FMEA Software Thailand — สำหรับโรงงานในไทยและ ASEAN

Transform Your FMEA Processwith Artificial Intelligence

Transform Your FMEA Process
with Artificial Intelligence