Deep Learning-Based Defect Detection for Sustainable Smart Manufacturing
Problem
Product defects arise at several stages of manufacturing, and small-scale producers rely mainly on manual visual inspection to catch them.
Why it matters
Defects and manual inspection costs threaten sustainable growth for small-scale manufacturers.
What the AI did
A deep learning-based automatic defect detection model trained on product characteristics using open sources was applied to detect liquefied gas volume defects in disposable gas lighter manufacturing.
Results
Detection accuracy and processing time confirmed sufficient for the manufacturing process; concrete numbers not stated in source
Impact
Evidence supports viability of deep learning defect detection in one lighter manufacturing process.
Record
| Venue | Sustainability |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Sang-Hyun Park, Kang-Hee Lee, Ji Su Park, Youn-Soon Shin |
| Citations | 29 (OpenAlex, at import time) |
| References | 18 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/su14052697 |
| Open access | gold |
| Keywords | deep learning, defect detection, smart manufacturing, visual inspection |
Importance rating
incremental / niche
Abstract claims defect reduction and lower human costs aid sustainable growth for small-scale producers, but evidence is one gas lighter process; narrow niche application.
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