Andrew Polonsky

Sam Arkle, Andrew Polonsky

We present a constructive formalization of Abstract Rewriting Systems (ARS) in the Agda proof assistant, focusing on standard results in term rewriting. We define a taxonomy of concepts related to termination and confluence and investigate the relationships between them and their classical counterparts. We identify, and eliminate where possible, the use of classical logic in the proofs of standard ARS results. Our analysis leads to refinements and mild generalizations of classical termination and confluence criteria. We investigate logical relationships between several notions of termination, arising from different formulations of the concept of a well-founded relation. We illustrate general applicability of our ARS development with an example formalization of the lambda calculus.

Peter Myung-Won Pak, Francis Ogoke, Andrew Polonsky et al.

We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a $R^2$ score of 0.57 and our model for porosity localization produced an average IoU score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity "Digital Twins" based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.