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===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:
*'''Acceptable''': the AOI machine signals the component as a possible anomaly because the component image doesn’t respect the color constraints specified by the software of the machine but in truth, there is no defect. Generally, this occurs when the component is correctly soldered on both pads, but the amount of red and green color is too high with respect to the amount of blue. In this case, quality is not the target one but still is acceptable.
*'''Missing''': the component is not in place, hence only the pads with applied solder are visible.
*'''Tombstoning''': the component is lifted from a pad of the PCB; this class also comprehends all the cases for which the component is lifted and rotated by a certain amount.
*'''Under soldering''': the component is soldered on both pads, but the amount of solder is too low. By looking at picture of the reported component, this is clearly visible when on a pad or both there is a higher amount of red with respect to the blue one.
To simplify the problem, all the classes into which the images are divided are mutually exclusive. ''manhattan'' and ''over soldering'' defect typologies are no longer included among the possible classes because their generation is too difficult hence the quantity of examples obtained is too low for training a model.
[[File:Samples of defects divided by classes.png|center|thumb|500x500px|Examples of four types of defects]]
For labelling the images [https://www.makesense.ai/ ''makesense−ai''] tool was used.
{| class="wikitable" style="font-weight:bold; text-align:center;"
|-
! Acceptable
! Missing
! Tombstoning
! Under soldering
|- style="font-weight:normal;"
| 17
| 408
| 376
| 24
|}
It is evident that this dataset is unbalanced because it has a relatively low number of acceptable and under soldering defect images. Nevertheless, taking into account the overall results achieved, this first attempt can be considered a successful one.
===Soldering regions extraction===
