Abstract: Systems and methods are described for training a model for detecting manufacturing anomalies. A test response parameter is identified at a computing device, and a first plurality of component waveforms associated with the test response parameter are received at the computing device. Each waveform of the plurality of waveforms comprises a plurality of datapoints. A model is generated at the computing device, and the model is trained at the computing device and on the first plurality of component waveforms, thereby generating one or more parameters associated with the model. A second plurality of component waveforms associated with the test response parameter is received, and the trained model is accessed. It is indicated using the trained model, whether any of the second plurality of component waveforms comprises an anomaly. For each indicated waveform, the indicated waveform is reviewed and, for each reviewed waveform not comprising an anomaly, the waveform is labelled.