Do you have target labels for how much defect do you have against the flow?

The data consists of flowrate and defect column, if there is no defect for eg. flow doesn't go above 8 lpm, then there is no default. If it does exceed then "Porosity" is mentioned with that flow rate.

There can be a two way to achieve it. One by image classification and another based on gas flow rate like time series data or combination of both. If your data is not big enough I would refrain from making square wave assumptions. You can look into methods based on RNN or time series Data based prediction method. But be aware if you are going for this way then you should be reasonably sure that gas flow is the only or most important parameter in welding defect.

it certainly does not only depend on gas flow rate but also current and arc length, so there are 3-4 variables. I guess I'm looking towards multi variate time series prediction method then

Now it makes more sense and if you can gather some data of defect image and non defect image of welding then it can learn from 2 modalities (time series and image) and might generate better results. One of my friend used this method for similar defect analysis. You might need to do some feature analysis to find some pattern as non useful or outlier feature can produce unnecessary noise and pose difficulties for optimization.

Does the data include the type of material being welded (and possibly thickness)? I think certain metals e.g. titanium, stainless, etc. may need different torch cups and therefore different flow rates