Abstract
An experienced engineer can glance at a component and suggest appropriate methods for its manufacture. This skill has been difficult to automate but in recent years Neural Networks have demonstrated impressive image recognition capabilities in many applications. Consequently, this work is motivated by the goal of automating shape assessment for manufacturing. Specifically the reported work investigates the feasibility of training a convolutional neural network (CNN) to recognize 2D images of shapes associated with particular Near Net Shape (NNS) manufacturing processes such as casting, forging, or flow forming. The system uses multiple images generated from 3D CAD models (each manually associated with specific NNS processes) as training data and a single shop floor photograph as a classification query. While multiple views are used to train the CNN only a single view is used to assess the accuracy of the classification. Such single-view classification is designed to support the easy assessment of physical parts observed in manufacturing facilities where it would often be impractical to create an array of images from many viewpoints. The result suggests that despite limitations, single-view CNNs can classify real engineering components for manufacture.
