  Electroplated diamond abrasive tools require nickel coating on a metal
surface for abrasive bonding and part functionality. The electroplated
nickel-coated abrasive tool is expected to have a high-quality part performance
by having a nickel coating thickness of between 50% to 60% of the abrasive
median diameter, uniformity of the nickel layer, abrasive distribution over the
electroplated surface, and bright gloss. Electroplating parameters are set
accordingly for this purpose. Industrial quality inspection for defects of
these abrasive electroplated parts with optical inspection instruments is
extremely challenging due to the diamond's light refraction, dispersion nature,
and reflective bright nickel surface. The difficulty posed by this challenge
requires parts to be quality inspected manually with an eye loupe that is
subjective and costly. In this study, we use a Convolutional Neural Network
(CNN) model in the production line to detect abrasive electroplated part
anomalies allowing us to fix or eliminate those parts or elements that are in
bad condition from the production chain and ultimately reduce manual quality
inspection cost. We used 744 samples to train our model. Our model successfully
identified over 99% of the parts with an anomaly. Keywords: Artificial
Intelligence, Anomaly Detection, Industrial Quality Inspection, Electroplating,
Diamond Abrasive Tool
