Difference between revisions of "ML-TN-003 — AI at the edge: visual inspection of assembled PCBs for defect detection — Part 3"
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===Class subdivision and labelling=== | ===Class subdivision and labelling=== | ||
| + | In order to build a dataset for training a ML model for a classification application, a set of classes has to be defined. By looking at the standard IPC-A-610E-2010 developed by IPC for the Acceptability of Electronic Assemblies, and at the features of collected images, the classes identified are the following ones: | ||
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===Soldering regions extraction=== | ===Soldering regions extraction=== | ||
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|[[File:Missing upper lower 256x256.png|thumb|350x350px|256 × 256 resolution ''upper'' and ''lower'' region images]] | |[[File:Missing upper lower 256x256.png|thumb|350x350px|256 × 256 resolution ''upper'' and ''lower'' region images]] | ||
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{| style="background:transparent; color:black" border="0" align="center" cellpadding="10px" cellspacing="0px" height="550" valign="bottom" | {| style="background:transparent; color:black" border="0" align="center" cellpadding="10px" cellspacing="0px" height="550" valign="bottom" | ||
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|[[File:Tombstoning upper lower 256x256.png|thumb|350x350px|256 × 256 resolution ''upper'' and ''lower'' region images]] | |[[File:Tombstoning upper lower 256x256.png|thumb|350x350px|256 × 256 resolution ''upper'' and ''lower'' region images]] | ||
|} | |} | ||
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==Useful links== | ==Useful links== | ||
Revision as of 13:21, 14 April 2021
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Contents
History[edit | edit source]
| Version | Date | Notes |
|---|---|---|
| 1.0.0 | March 2021 | First public release |
Introduction[edit | edit source]
Building the dataset[edit | edit source]
Defects generation and acquisition[edit | edit source]
| Anomaly | P&P | Serigraphy | Manual | Numerosity |
|---|---|---|---|---|
| Missing | ✓ | 21 | ||
| Manhattan | ✓ | 4 | ||
| Shift x-axis | ✓ | 51 | ||
| Shift y-axis | ✓ | 58 | ||
| Shift&Rotation (shift x+z-axes) |
✓ | 57 | ||
| Rotation (z-axis) |
✓ |
55 | ||
| Under soldering | ✓ | All | ||
| Over soldering | ✓ | All |
Class subdivision and labelling[edit | edit source]
In order to build a dataset for training a ML model for a classification application, a set of classes has to be defined. By looking at the standard IPC-A-610E-2010 developed by IPC for the Acceptability of Electronic Assemblies, and at the features of collected images, the classes identified are the following ones:
Soldering regions extraction[edit | edit source]
Data augmentation with image synthesis[edit | edit source]
Generative adversarial networks[edit | edit source]
Progressive GAN implementation[edit | edit source]
Results validation[edit | edit source]
| Class | Synth image | Resolution (pixel) |
Google Colab (min) |
AWS SageMaker (min) |
|---|---|---|---|---|
missing |
full | 512 × 512 | ~480 | ~410 |
| upper/lower | 256 × 256 | ~435 | ~310 | |
tombstoning |
full | 512 × 512 | ~460 | ~390 |
| upper/lower | 256 × 256 | ~420 | ~300 |
Useful links[edit | edit source]
- Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen, Progressive Growing of GANs for Improved Quality, Stability, and Variation, February 2018.
- Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville, Improved Training of Wasserstein GANs, December 2017.
- IPC-A-610E: Acceptability of Electronic Assemblies, a standard developed by IPC, April 2010.
