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.
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.