Materials used in manufacturing or construction can have all sorts of defects ranging from physical anomalies like breaks to chemical issues like oily surfaces. These issues can occur during manufacturing, installation, or post-installation (e.g., due to wear, environmental exposure, etc.). Such issues often need to be detected as soon as possible to avoid subsequent problems or to be flagged for repair or reclassified as a lower grade of quality, depending on the situation.
In cases involving high-precision components, the detection of such defects becomes even more important to avoid subsequent problems. One such example is in the manufacturing of pistons for air conditioner (AC) units which must be built to within tight tolerances so that the units operate reliably in the field.
With the growing use of computer vision in Industrial IoT (IIoT) and Industry 4.0 to analyze and detect defects, we were inspired to build an image recognition model that can classify images of AC pistons as either normal (i.e., no defects), oily/greasy, or defective (i.e., broken, out of shape, or dropped). This involved preparing and wrangling the training data, building a .csv file to map that data to the classifications, and iterating with the model in PerceptiLabs.
Our model was built with just three Components:
Component 1: Convolution Path_size=3, stride=2, feature_maps=16
Component 2: Dense Activation=ReLU, Neurons=128
Component 3: Dense Activation=Softmax, Neurons=3
Check out our GitHub for this case study!