Submitted by hotspicynoodles t3_104ldbr in MachineLearning

I am responsible for building a defect detection system for TIG welding. If gas flow gets too high, there is a fair chance that welded piece might have porosity defect. The project aims to predict % of defect by predicting gas flow.

Attached is the link of how the flow pattern looks like over time, like square waves.

Flow rate fluctuates between 0-8 liters per minute over a given time

I have data from various workstations on after welding, if the piece had a defect or not. Please help me solve this problem or give rough steps to follow.

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KBM_KBM t1_j35r12q wrote

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

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hotspicynoodles OP t1_j35txz9 wrote

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.

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projekt_treadstone t1_j35wtvt wrote

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.

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projekt_treadstone t1_j35yy00 wrote

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.

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Anjum48 t1_j38bc0o wrote

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

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