Hyperdimensional computing (HDC) is a machine learning (ML) technique that represents data using hypervectors to perform tasks such as classification. Inspired by how the brain operates in high-dimensional spaces, these hypervectors have dimensions in the range of tens of thousands. This high dimensional representation of data allows for the development of a highly robust, energy efficient, and accurate model as a machine learning technique. With the rise of the Industrial Internet of Things (IIoT), large amounts of data is being collected on edge devices during manufacturing for the purposes of defect detection and correction. Current state-of-the-art models such as neural networks suffer from poor sample efficiency, lack of interpretable learning, and low accuracy and precision with high computational cost. This project aims to solve these problems by designing an end to end and fully open-sourced hyperdimensional computing model for advanced manufacturing (AM) systems. Specifically, this model has both learning and reasoning capabilities for defect detection, classification, and casualty analysis in advanced manufacturing.