Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts
In recent years, Additive Manufacturing (AM) has evolved from a niche technology for prototyping to a well-known industrial production process. In this work, we focus on Selective Laser Sintering (SLS)---one of the leading AM techniques. While SLS has many advantages, the simultaneous manufacturing of multiple components requires the subsequent recognition of components which must be done manually in today's production processes. While approaches for automatic, sensor-based object recognition have been proposed, e.g., based on Convolutional Neural Networks (CNNs), they assume the availability of real-world photos which is not given in the setting of Additive Manufacturing. Hence, we develop an approach to render realistic virtual images and demonstrate their suitability to recognize real-world objects. Although often done in the machine learning community, orienting the objects randomly generates many orientations that are physically impossible and cause distracting noise in the training process. Hence, we pay particular attention to generate physically sound training data and we demonstrate that our approach significantly improves the recognition rate compared to traditional approaches.