Machine learning detect changes in components
This is an interesting question about how machine learning can be used to changes in components. In this case, I am going to infer that is maybe talking about detecting defect components, which of course should not be put into a new vehicle.
In this case, you might use images (maybe taken from video of the assembly floor) to the detect potentially defect components. So, hard-coding this would be extremely hard, because:

Firstly, you would need to the code to detect the component and separate this from the background (this task is known as semantic segmentation).
You then have to compare the component with images of a &quot;good&quot; component and a &quot;bad&quot; component (preferably multiple examples of each). This also isn't an easy task to hard-code, because you need might observe the component in different angles (bird's eye view or front on, or example), which are not discrete. Therefore, some level of generalisation is required to do this (hence machine learning)

I look forward to other's interpretations and answers to this question.