Automate QC inspection reports, defect tracking, and pass/fail analysis.
AI AgentLLM ServerManufacturingQCDetached System
0.5×← Perlahan · Cepat →
0%Stage 0 / 7 · Idle
💬Tekan Play Demo untuk lihat bagaimana QC Report Agent mengautomasi laporan kualiti.
Demo Complete — All Stages Generated
01 WhatsApp Command
👷
OpenClaw — AINNA Agent
● Neural Link Active
02 Requirement Analysis
Batches
5
Samples
500
Defect Rate
4.2%
Pass %
95.8
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03 Python Script Generation
1
🔬
Ambil sampelFetch QC inspection records
2
📊
Analisis cacatClassify defect types & counts
3
✅
KeputusanPass/fail determination per batch
4
📋
Laporan QCGenerate QC report with charts
import pandas as pd
from datetime import datetime
def classify_defects(samples, defect_types):
counts = {d: 0 for d in defect_types}
for s in samples:
if s['defect'] in counts:
counts[s['defect']] += 1
return counts
def pass_fail(batch_id, defect_count, sample_size, threshold=0.05):
rate = defect_count / sample_size
return 'PASS' if rate <= threshold else 'FAIL'
# Generate QC report
04 Block Diagram
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05 System Schematic
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⚠️ Cross-check QC results with production supervisor before batch disposition.
06 Simulasi QC Inspection
Mula QC
Batches
0
Samples
0
Defects
0
Fails
0
07 QC Report
Demo
QC Report
Batches
0
Samples
0
Defects
0
Status
Standby
Terminal Log
[SISTEM] Manufacturing Quality Control Report Agent sedia
System Architecture
📱 Command Layer
WhatsApp instruction
🤖 Agent Layer
Defect classifier
Pass/fail engine
Report generator
🏭 Manufacturing Layer
QC DB
Inspection log
Batch records
Reports
Safety Notes
Cross-check QC results with production supervisor before batch disposition.