AOI defect report optimization
Python reporting for MIRTEC AOI that cut false positives and tied defects to accurate BOM lines for faster analysis.
- Role
- Process Engineer / Software Developer — Integrated Test Corporation (ITC), 2023–2025
- Stack
- Python, pandas, AOI machine exports, BOM cross-reference
Highlights
- Filtered noisy multi-failure reports so engineers saw real defects first.
- Cross-referenced data to append correct BOM line numbers to each finding.
- Persisted only substantive errors to support deeper failure analysis.
Problem
AOI machines produced dense, multi-failure reports where false positives and unclear line attribution slowed triage and obscured real defects.
Approach
Implemented Python pipelines that classified and filtered report noise, then joined AOI output to BOM structure so each remaining defect pointed to the correct assembly context.
Outcome
Triage became faster and more trustworthy; stored errors supported trend analysis without drowning analysts in duplicate or phantom hits.
Technical notes
- Rule layers tuned with floor feedback to avoid over-pruning edge cases.
- Stable identifiers for correlating repeated failures across batches.