research

We present a new data generation method to facilitate an automatic machine interpretation of 2D engineering part drawings. While such drawings are a common medium for clients to encode design and manufacturing requirements, a lack of computer support to automatically interpret these drawings necessitates part manufacturers to resort to laborious manual approaches for interpretation which, in turn, severely limits processing capacity. Although recent advances in trainable computer vision methods may enable automatic machine interpretation, it remains challenging to apply such methods to engineering drawings due to a lack of labeled training data. As one step toward this challenge, we propose a constrained data synthesis method to generate an arbitrarily large set of synthetic training drawings using only a handful of labeled examples. Our method is based on the randomization of the dimension sets subject to two major constraints to ensure the validity of the synthetic drawings. The effectiveness of our method is demonstrated in the context of a binary component segmentation task with a proposed list of descriptors. An evaluation of several image segmentation methods trained on our synthetic dataset shows that our approach to new data generation can boost the segmentation accuracy and the generalizability of the machine learning models to unseen drawings.

While additive manufacturing has seen rapid proliferation in recent years, process monitoring and quality assurance methods capable of detecting micro-scale flaws have seen little improvement and remain largely expensive and time-consuming. In this work we propose a pipeline for training two deep learning flaw formation detection techniques including convolutional neural networks and long short-term memory networks. We demonstrate that the flaw formation mechanisms of interest to this study, including keyhole porosity, lack of fusion, and bead up, are separable using these methods. Both approaches have yielded a classification accuracy over 99% on unseen test sets. The results suggest that the implementation of machine learning enabled acoustic process monitoring is potentially a viable replacement for traditional quality assurance methods as well as a tool to guide traditional quality assurance methods.