Training Simple CNN on Synthetic Data for Real Data Applications in CNC Manufacturing

Abstract

This paper describes the problem formulation, data set generation, and initial testing of image recognition methods for applications in Computer Numerical Control (CNC) machine vision. Synthetic part images are used to train a simple Convolutional Neural Network (CNN) for feature classification. Where potentially infinite parts could be created, but the manufacturing of a great number of different parts is itself expensive, synthetically generated images present an opportunity to train an image classifier on a great span of possible features. This paper contributes a definition of synthetic training data for a simple CNC feature understanding, and explores simple CNN methods on real and synthetic feature data.